SAR Processing With Geomatica v2014

SSAARR pprroocceessssiinngg wwiitthh GGeeoommaattiiccaa TTrraaiinniinngg gguuiiddee

Version 2.1

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PCI Geomatics Page v

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Version Released Revisions Revised by 2.0 2014 09 30 Updated for 2014 G. Gosselin 2.1 2014 11 21 Minor edits G. Gosselin

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Introduction 10 Course overview 10 About this guide 10 Radar-processing workflows 11 Starting Geomatica 13 Starting Geomatica Focus 13 Starting Geomatica SPTA 14

Module 1: SAR data structure and support in Geomatica 17 Lesson 1.1: Supported SAR data format in Geomatica 17 Lesson 1.2: Data structure and matrix type 20 Lesson 1.3: Complex data support in Geomatica 26 Lesson 1.4: Ingesting and extracting a calibrated backscatter image from SAR data 30 Lesson 1.5: Conversion utilities 42

Module 2: Applying radiometric enhancements 47 About this module 47 Lesson 2.1: Applying SAR speckle filters 47 Lesson 2.2: Applying the PSBOXCAR filter to polarimetric data 55 Lesson 2.3: Applying the PSPOLFIL filter to polarimetric data 59

Module 3: Polarimetric decompositions 63 About this module 63 Polarimetry fundamentals 63 Lesson 3.1: Freeman-Durden decomposition 74 Lesson 3.2: Cloude-Pottier decomposition 80 Lesson 3.3: Touzi decomposition 85

Module 4: Using the SAR Polarimetry Target Analysis tool 94 About this module 94 Lesson 4.1: Introduction to SPTA and the target-selection mode 94 Lesson 4.2: Coherent target analysis in SPTA 100 Lesson 4.3: Incoherent target analysis in SPTA 108

Module 5: Classifying SAR data 117 About this module 117 Lesson 5.1: Classification of polarimetric data, possible workflows 117 Lesson 5.2: Unsupervised Wishart classifications 119 Lesson 5.3: Supervised Wishart classification (with targets defined in SPTA) 124

Module 6: Change detection using polarimetric data 132 About this module 132 Lesson 6.1: Examples of possible workflows for change detection 132 Lesson 6.2: Performing an intensity-change detection 139 Lesson 6.3: Wishart change detection 143 Lesson 6.4: Performing a phase change detection 147

Contents SAR processing with Geomatica

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Lesson 6.5: Incoherent change detection workflow 151

Appendix A 165 Processing with single or dualpol detected data 165 Applying intensity channels ratio or difference using EASI modeling on detected data 165 Applying an intensity-change detection on detected data 169 Computing SAR texture measures 173

Appendix B 181 Polarimetric discriminators 181 Generating polarimetric discriminators based on coherency matrix eigenvalues182 Generating polarimetric discriminators based on analysis of the Poincaré Sphere187 Synthesizing a backscatter SAR image for arbitrary transmit and receive polarizations 191 Maximizing the contrast between two targets 196

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IInnttrroodduuccttiioonn

CCoouurrssee oovveerrvviieeww Welcome to the SAR processing with Geomatica training course.

The course is designed for experienced users of geospatial software and introduces you to the radar analysis tools available with PCI Geomatica software.

This guide contains six modules. The lessons in each module are designed for tasks you are likely to perform in your analysis of radar imagery. They provide instruction for using the software to carry out key processes while sampling key Geomatica applications and features.

AAbboouutt tthhiiss gguuiiddee Each module in this guide contains a series of hands-on lessons that let you work with the software and a set of sample data. Lessons have brief introductions followed by tasks and procedures in numbered steps. The lessons provide you with an opportunity to work with several Geomatica programs, such as Geomatica Focus, OrthoEngine, and SPTA (SAR Polarimetry Target Analysis), to carry out various tasks. The goal is to become familiar with the tools available for analysis of synthetic aperture radar (SAR), and to develop your expertise with Geomatica.

The scope of this guide is confined to the core PCI software programs included in the Geomatica suite. Some radar remote-sensing concepts are reviewed in the modules and lessons.

The modules in this course provide instructions on a range of activities associated with processing SAR data. This course includes the following modules:

Module 1: SAR data structure and support in Geomatica

Module 2: Applying radiometric enhancements

Module 3: Polarimetric decompositions

Module 4: Using the SAR Polarimetry Target Analysis tool

Module 5: Classifying SAR data

Module 6: Change detection using polarimetric data

The data you will use in this course can be found in the Radar Data folder of your Geomatica installation.

SAR processing with Geomatica Introduction

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RRaaddaarr--pprroocceessssiinngg wwoorrkkfflloowwss Geomatica includes a variety of tools for working with radar data. The workflow you choose depends on several factors:

What you want to achieve with your data

Whether your data is detected imagery or complex

Whether the detected data is flat versus rugged terrain

Whether you are performing intensity analysis and statistical modeling versus texture analysis and classification

Figure 1 shows the software available in Geomatica 2014 to ingest, process, and analyze SAR data.

Figure 1. Workflow diagram

Introduction SAR processing with Geomatica

Figure 2 shows the algorithms available to process detected or complex SAR data. Most of these algorithms are described and used in this course.

Figure 2. Algorithms in Geomatica SAR Radar Analysis library

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SSttaarrttiinngg GGeeoommaattiiccaa Starting Geomatica varies slightly between Windows and UNIX operating systems.

WWiinnddoowwss With the Windows operating system, you start Geomatica from the Start menu.

To start Geomatica on a Windows system Click the Start button, and then click All Programs > PCI Geomatics >

Geomatica 2014 > Geomatica. The Geomatica Toolbar appears, followed by the Focus window.

UUNNIIXX With the UNIX operating system, you start Geomatica from a command prompt.

To start Geomatica on a Unix system 1. Enter the Unix environment.

2. At the command prompt, type geomatica. The Geomatica Toolbar appears, followed by the Focus window.

SSttaarrttiinngg GGeeoommaattiiccaa FFooccuuss You start Geomatica Focus from the Geomatica toolbar.

Note Focus starts by default when you start Geomatica.

To start Geomatica Focus On the Geomatica toolbar, click the Focus button.

The Focus window appears.

Introduction SAR processing with Geomatica

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SSttaarrttiinngg GGeeoommaattiiccaa SSPPTTAA You start Geomatica SPTA from the Geomatica toolbar.

To start SPTA On the Geomatica toolbar, click the SPTA button.

The SAR Polarimetry Target Analysis window and the Target Selection window appear.


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MMoodduullee 11:: SSAARR ddaattaa ssttrruuccttuurree aanndd ssuuppppoorrtt iinn GGeeoommaattiiccaa

LLeessssoonn 11..11:: SSuuppppoorrtteedd SSAARR ddaattaa ffoorrmmaatt iinn GGeeoommaattiiccaa Several SAR image formats are supported by the PCI generic-database technology, known as GDB. GDB allows programs to directly access data using the vendor’s format, in a uniform manner, without having to reformat it before using the data in one or more programs. GDB includes access to imagery, vectors, attributes, ground control points, and other auxiliary information of interest to users of geomatics software. The Geomatica radar suite supports many SAR sensors1, including:

Radarsat-2

Sentinel-1

Cosmos-SkyMed

TerraSAR-X

Kompsat-5

UAVSAR

Alos-PALSAR

ERS1&2

Radarsat-1

Envisat-ASAR

Data acquired from these sensors is diverse and often available for different acquisition modes, data formats, and processing levels, excluding different resolution and beam modes. This variety can categorize the data in different ways, and among them, the Geomatica radar suite uses two basic categories:

Channel type

Transmit/receive configuration

These categories define which algorithms you can use to process your SAR data.

The channel type is divided in two sub-categories:

Complex data

Detected data 1 To verify whether a sensor is supported by GDB, browse the GDB file formats in the Technical Reference section of the Geomatica online help.

Module 1: SAR data structure and support in Geomatica SAR processing with Geomatica

Complex data, also known as single-look complex (SLC) data, corresponds to the first processing level after the signal transformation into an image. Each pixel of a given channel form corresponds to a complex number composed of an imaginary (Q) and a real (I) part. Complex (coherent) arithmetic is thus possible between channels of a single image or between channels from different images, which leads to applications like radar polarimetry, compact polarimetry, Interferometry (InSAR), and Polarimetric Interferometry (POLinSAR). More information on radar polarimetry is provided in Module 3.

Detected data corresponds generally to the next processing level in which the real and imaginary parts are combined. Detected data form layers (images) are easier to interpret to the human eye, where the only available information for any given pixel is the backscattered power. Thus, a surface or object that reflects most of the incident signal toward the sensors will appear bright on the image while a surface that reflects the most of the incident signal away from the sensor will appear dark.

Table 1. Channel types

Channel type Sub-type Example Complex channel Single look complex

(SLC) or Multilook complex (MLC)

For a complex number a + bi, where a corresponds to the real part (I) and b to the imaginary part (Q), if I= 0.206871 and Q = 0.0338254, then the complex number corresponds to 0.206871 - 0.0338254i.

Detected channel Amplitude, magnitude, decibels

Intensity: corresponds to (I^2) + (Q^2). Note that the intensity is also known as power. (0.206871^2) + (–0.0338254i ^2) = 0.0439399 Amplitude: corresponds to the square root of the intensity (I^2 + Q^2)^0.5. (0.0439399)^0.5 = 0.2096184 Decibel: corresponds to 10* log10 (intensity) or, equivalently, to 20*log10 (amplitude)

The transmit/receive configuration refers to the polarization or polarizations used for transmitting (Tx) and receiving (Rx) the signal and to the number or channels forming a single image.

Table 2. Configuration types

Configuration Channel type Details Single polarization Complex or detected Tx: one polarization, Rx: one polarization

ex.1: Tx (H), Rx (H) → HH ex.2: Tx (V), Rx (V) → VV

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SAR processing with Geomatica Module 1: SAR data structure and support in Geomatica

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Configuration Channel type Details Dual polarization Detected Tx: one polarization, Rx: two

polarizations ex.1: Tx (H), Rx (H)&(V) → HH + HV ex.2: Tx (V), Rx (H)&(V) → VH + VV

Compact polarization Complex Tx: one polarization, Rx: two polarizations + phase (Φ) preservation between channels ex.1: Tx (H), Rx (H)&(V) → HH + HV + ΦHHHV ex.2: Tx (V), Rx (H)&(V) → VH + VV + ΦVHVV

Fully polarimetric (quadpol)

Complex Tx: two polarization, Rx: two polarizations + phase (Φ) preservation between channels. ex. Tx (H)&(V), Rx (H)&(V) → HH, HV, VH, VV + ΦHHVV, ΦHHHV, ΦHHVH, ΦVVVH, ΦVVHV

More information on the polarization concept is provided in Module 3 and Appendix B.


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LLeessssoonn 11..22:: DDaattaa ssttrruuccttuurree aanndd mmaattrriixx ttyyppee In this lesson, you will:

Display a RADAR (SAR) image in Focus

Explore the SAR data structure and georeferencing

Learn about channel and matrix types

DDaattaa ssttrruuccttuurree With Geomatica Focus, you can work with data in a variety of formats through GDB and the PCIDSK file format. This means that most RADAR data is supported in its original distribution format and can be opened in Geomatica or SPTA using the key-file name. Most algorithms can also process data in its raw vendor format.

Sensor, key-file name

Radarsat-2, product.xml

TerraSAR-X, *.xml

Cosmos-Skymed, *.h5

To open a file in original distribution format using its key-file name 1. In Focus, click File menu, and then click Open.

2. In the File Selector window, navigate to your file location, select the key file name, and then click Open.

3. If data calibration is supported for the sensor used, at the prompt, select a calibration type, and then click OK.

4. If supported, select a projection, and then click OK. The image displays in the Focus view pane.


For each sensor, depending on the product and acquisition type, different image layers and auxiliary segments are available and can be imported.

Figure 3. Radarsat-2, Fine Quad polarization, beam 29 (FQ29), May 7, 2010.

Figure 4. TerraSAR-X, Single Look Slant Range Complex (SSC) Dual polarization, August 18, 2007

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Figure 5. Example of SAR data structure in Geomatica

Today, most sensors provide information describing the geolocation of the acquired SAR data derived from GPS receivers on board the satellite. In its simplest form, the geolocation information corresponds to the geographic coordinates of each corner. Some sensors also provide rational polynomial coefficients (RPCs) based on hundreds of GCPs also derived from the satellite GPS receivers for greater accuracy.

Figure 6. Ground Control Points displayed in Geomatica

When available, Focus and SPTA automatically read the geolocation from the file and display the image according to the map projection from the file Projection and Bounds, Rational Functions Model Segment, or from the orbit segment.

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Figure 7. Selecting georeferencing source

Figure 8. Select georeferencing layer

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Note If an image is already open in Focus, but with a different projection, a warning message appears, prompting you to choose whether to add and reproject the file.

Finally, you choose whether to display the file North Up or Raster Up.

Figure 9. North Up or Raster Up selection

Note Focus and SPTA read geolocation information "as is", due to the variation in positional accuracy of sensors and acquisition modes. SAR data is also resampled and projected "on-the-fly", meaning the data always remains in its original format, unprojected, and at full resolution. Any processing will be applied on the original data format. If you want to increase the positional accuracy by collecting external GCPs and permanently apply map projection to the data of a given spatial resolution, you must do so in Geomatica OrthoEngine.

To take advantage of all of the features offered in Geomatica, convert your data files to PCIDSK format (*.pix). Doing so allows you to, in particular, create overviews faster and store auxiliary layers, such as lookup tables, pseudocolor tables, bitmaps, and vectors. More information on the PCIDSK format is provided in Lesson 1.5: Conversion utilities.

Matrix type Geomatica characterizes SAR data according to a matrix type, which is derived from the file metadata, the channel type, and the transmit/receive configuration.

Lesson 1.1: Supported SAR data format in Geomatica discussed that channel type is either complex (indicated by "c") or detected (indicated by "r"). A SAR image can contain a complex or detected channel or channels only or a mix of both. The number of channels of each type determines the matrix type. The matrix type is important because many SAR algorithms require a particular matrix type of input.

The following table describes the matrix types for complex data.

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Table 3. Matrix types: complex data

Symbol Full name Total No. of Channels

s4c Non-symmetrized scattering matrix (single look only) 4

S3c Symmetrized scattering matrix 3

s2c Incomplete scattering matrix (dual-pol single look only) 2

s1c Incomplete scattering matrix (single-pol single look only) 1

c4r6c Non-symmetrized covariance matrix (best for multilook) 10

C3r3c Symmetrized covariance matrix 6

c2r1c Incomplete covariance matrix (best for dual-pol multilook) 3

t4r6c Non-symmetrized coherence matrix (best for multilook) 10

T3r3c Symmetrized coherence matrix 6

k16r Non-symmetrized Kennaugh matrix (best for multilook) 16

K9r Symmetrized Kennaugh matrix 9

The following table describes the matrix types for detected data.

Table 4. Matrix types: detected data

Symbol Full name Total No. of Channels

c4r Incomplete non-symmetrized covariance matrix (quad-pol detected, single look, or multilook)

4

c3r Incomplete symmetrized covariance matrix (quad-pol detected, single-look, or multilook)

3

c2r Incomplete covariance matrix (dual-pol detected, single look, or multilook) 2

c1r Incomplete covariance matrix (single-pol detected, single look, or multilook) 1

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LLeessssoonn 11..33:: CCoommpplleexx ddaattaa ssuuppppoorrtt iinn GGeeoommaattiiccaa Complex data support has been introduced in Geomatica 2014 to natively support SAR complex data. With this version, the real and imaginary parts of each channel are no longer stored on separate channels and detected channels are no longer created and added to the file.

Figure 10. Radarsat-2, Fine Quad polarization, beam 29 (FQ29), May 7, 2010 opened in Geomatoca 2013

Figure 11. Radarsat-2, Fine Quad polarization, beam 29 (FQ29), May 7, 2010 opened in Geomatoca 2014


This enhancement of Geomatica 2014 offers many advantages, such as a reduced file size on disk. The data interpretation of any complex channels can be changed dynamically for faster data-content exploration.

To change the data interpretation of complex images in Focus 1. In Focus, click the Maps tab.

2. Right-click a complex image, point to Data Interpretation, and then click an interpretation type.

The interpretation type is automatically applied and the numeric values displayed on the Focus status bar correspond to the selected interpretation.

Figure 12. Complex Data-interpretation options

Numeric Numeric values of the selected applications and the original complex values can also be viewed using the Numeric Values tool.

To open the Numeric Values window Click the Layer menu, and then click Numeric Values.

The Numeric Values window appears.

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Figure 13. Numeric Values window: Raw (Intensity)

Figure 14. Numeric Values window: Complex data

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Other benefits of the new complex data support in Geomatica 2014 include:

In OrthoEngine, the ability to geocode, orthorectify complex data, or both, and keep the orthorectified files in complex format. For example, this is necessary for coherent change detection when a greater positional accuracy is needed while complex data is required.

The ability to generate overviews, reproject, clip, or subset complex data.

PCI Geomatics


PCI Geomatics

LLeessssoonn 11..44:: IInnggeessttiinngg aanndd eexxttrraaccttiinngg aa ccaalliibbrraatteedd bbaacckkssccaatttteerr iimmaaggee ffrroomm SSAARR ddaattaa In this lesson, you will:

Ingest a SAR image

Explore its metadata

Generate a calibrated backscatter image using different lookup tables.

IInnggeessttiinngg SSAARR ddaattaa There are two techniques to ingest (import) SAR data into a PCI pix file.

The first technique is to open an image in Focus using its key-file name, and then import it as a .pix file. The second technique is to use the SARINGEST algorithm.

To ingest a file using the Import to PCIDSK option in FOCUS 1. Open the image in Focus using its key-file name.

2. If prompted , select a calibration type and a projection.

3. Click the Maps tab, and then select the file to import.

4. On the File menu, point to Utility, and then click Import to PCIDSK. The source file is automatically set with the selected file in Focus.

5. In the Destination file box, enter a path and file name.

6. Click Import. The File Selector window appears.

7. Enter a path and file name.

8. Click Save.

The image is imported (ingested) into a .pix file.


Ingesting a file with SARINGEST Alternatively, you can use SARINGEST to import (ingest) SAR data into a .pix file. SARINGEST produces the same output file as the Import to PCIDSK option. However, an advantage of using SARINGEST is that you can automate the ingestion process to import multiple files.

To ingest SAR data in Focus 1. On the Tools menu, click Algorithm Librarian.

2. In the Algorithm Librarian window, click Find. The Find Algorithm window appears.

3. In the Find what box, type SARINGEST, and then click Find Next.

4. Double-click SARINGEST. The SARINGEST Module Control Panel window appears.

5. Under Output Ports, below Output: SAR Dataset, select Untitled.pix, and then click Browse.

6. In the File Selector window, open the ~\SAR_Training\Radar\Flevoland_RS2_SLC\FQ29_20100507\ folder.

7. In the File name box, type FQ29_20100507sig.pix, and then click Save. The new path and file name appear in the SARINGEST Module Control Panel window.

8. Click the Input Params 1 tab.

9. Click Browse, and then select the ~\SAR_Training\Radar\Flevoland_RS2_SLC\FQ29_20100507\product.xml file.

10. For the Data Calibration, click Sigma Nought.

11. Accept the default settings for the remaining input parameters.

12. Click Run. The image automatically opens in Focus, along with the output GCPs. A pop-up window appears, prompting you to choose whether to display the image North Up or Raster Up.

13. Click North Up, and then click OK. The RADARSAT-2 FQ-29 image is displayed in the view area with North at the top of the viewer.

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Note When a file is opened in its original distribution format in Focus, the calibration is applied "on the fly" only and not permanently to the original data. However, once a file is imported (ingested) to a PCIDSK file with a calibration type, it will be applied permanently and cannot be reverted to uncalibrated data or to another calibration type. To apply another calibration type, you must re-import (re-ingest) the file.

Exploring the metadata During ingestion, metadata is also imported. The metadata is retrieved directly from the file header (usually an .xml or .txt file accompanying the data) or created by Geomatica. The metadata is found at two levels, at the channel level and at the file level.

At the channel level, metadata is limited to a set of standardized descriptors, including the polarization type (HH, HV…), the data scale (linear, decibels), and the matrix-element position (1_1, 2_1, 4_4…) of the channel. Metadata at the channel level is read and used by many SAR processing algorithms.

At the file level, the metadata varies according to the sensor. While most of the metadata is for information only, some is used by certain algorithms, such as the matrix type, calibration type, and the number of looks.

To view the metadata at the file level 1. In Focus, click the Files tab.

2. Select the image for which you want to view the metadata.

3. Right click the file, and then click Properties. The File Properties window appears.

4. Click the MetaData tab. The file-level metadata is displayed.

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Note It is not mandatory to import a file in a .pix format to view its metadata.

To view the metadata at the channel level 1. In Focus, click the Files tab.

2. Select the image for which you want to view the metadata, and then expand the Rasters list.

3. Right-click a raster channel, and then click Properties. The Raster Properties window appears.

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4. Click the MetaData tab. The channel-level metadata is displayed.

SSAARR ggeeoommeettrryy aanndd rraaddiioommeettrriicc ccaalliibbrraattiioonn For most geoscience applications—quantitative analysis (biomass, soil wetness, for example) and time-series analysis, in particular—SAR data must be calibrated. You also may need to compensate for distortions in the imagery due to the geometry of acquisition.


SAR geometry Most synthetic aperture radars (SAR) used in geoscience application are usually side-looking. A series of waves, or pulses, are transmitted by the antenna toward the ground. Between each transmission, the same antenna is used to receive the transmitted signal.

Each transmitted pulse is carefully controlled; that is, the frequency, polarization, and phase of the signal are known. After the signal is scattered back to the sensor, the travel time, backscatter power, and phase is compared to the original pulse. Typically, over a thousand pulses are coherently averaged together to form a single pixel.

In a SAR image, the location of each pixel and its resolution is a combination of the time it took to be reflected back to the sensor (x, range resolution) and the time between two pulses relative to the platform velocity (y, azimuth resolution). Since it takes more time for a pulse to reach the far range of the radar swath, the slant range resolution is coarser at the far range than the near range (Figure 15 and Figure 16).

Figure 15. Slant range vs. ground range image

The SAR-image geometry can be determined by the following parameters (Lee and Pottier, 2009):

H: Height of SAR-imaging system moving at a velocity VSAR along the azimuth direction (y).

Lx and Ly: Antenna physical dimensions in height (Lx) and width (Ly), typically in meters, aiming perpendicular to the flight direction (y). Directed slant-wise toward the ground with an incidence angle 0 .

x: ground-range direction

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y: azimuth direction

r = Antenna radial axis (Radar-line-of-sight (LOS)

Radar swath: area scanned by the antenna beam

Antenna footprint: defined from the antenna apertures ( YX , ) given

by XX L and YY L , where is the wavelength corresponding to the carrier frequency of the transmitted signal

Figure 16. SAR Geometry2

Slant range geometry is the natural result of radar-range measurements. In many instances, however, it is preferred to convert the data to ground-range geometry, which is often characterized by square pixels. This is usually done using a resampling of the data when it is geocoded.

Transformation to ground range also requires a radiometric correction at each data point to compensate for incidence angle and local terrain slope and elevation This is especially true for wide-swath-imagined modes with the swath spanning more than hundreds of kilometers. This process is called radiometric calibration.

2 Source: Lee and Pottier (2009). Fig,1.1, p.6.


Extracting a calibrated backscatter image from SAR data: Radiometric calibration

Calibration is the process whereby the digital values of a received remotely sensed image can be related to physical quantities of the scene, such as brightness (reflectivity), phase, and location.

"Radiometric calibration of the data is required so that information on the magnitude of the radar backscatter coefficient of the imaged terrain can be extracted from the processed image data. There are two fundamental requirements for radiometric calibration (RSI, 2004)":

Relative radiometric calibration stability (evaluation of the electrical stability of the radar sensor hardware, antenna elevation beam pointing estimation, and pattern correction to reduce errors due to variations in satellite roll angle)

Absolute radiometric calibration knowledge (using precision active transponders, set to a known radar cross section (RCS), and passive corner reflectors or areas of known distributed target backscatter coefficient previously measured independently by other SAR sensors or scatterometers)

The goal of radiometric calibration is to adjust for all of the contributions in the radiometric values not due to target characteristics, so that the backscatter value of targets can be compared to one another or to a reference. A proper radiometric calibration is essential for any quantitative and temporal analysis of SAR data.

Output scaling of the processor Most data products are radiometrically calibrated by the data supplier prior to being distributed. The last step in processing is the application of lookup tables (LUTs), during calibration from floating-point data to digital numbers (DN) to ensure the best use of the 8 or 16 bits in storage media. These lookup tables apply a fixed offset and a range-dependent gain function to the data prior to generating the final image output. The scaling used can vary by scene, making it difficult to directly relate information between each.

The LUTs provided with the data are used by Geomatica to extract the calibrated image. To ensure proper SAR processing, pixel values (DNs) should first be converted to sigma-0 ( ), and used subsequently for any further analysis. Geomatica also has tools for converting from DNs to both beta-0 ( ) and gamma-

0 ( ).

00

0

The radar backscatter coefficient ( )) is used commonly and it is expressed per unit area in ground range (

0Figure 17).

"A problem arises if there is a non-zero slope at the local terrain site. In this case, the projected area is determined by the local incident angle. It follows that the correct values for σ° cannot be obtained unless one has at hand a reliable estimate of the local slope (Raney, 1998)".

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The radar Brightness ( ), is the most natural and observable radar measurement (Raney et al. 1994) and corresponds to the backscatter-per-unit area in slant range, and requires no knowledge of local incident angle (

0

Figure 17, Figure 18 and Figure 19). For the detected product, the radar brightness corresponds to:

jjj AADN 2/320

where DNj is the digital number that represents the magnitude of the pixel from

the start of a range line in the detected image data, and is the scaling gain

value for the pixel, and A3 is the fixed offset. Radar Brightness in decibels (dB) is given by:

thj

jA2thj

jjj AADNdB 2/3log*10)( 210

0

For complex (SLC) single-beam products, the pixel number, j, is related to the LUT index i, using the same procedure as for detected products. The radar brightness for the range pixel is then given by: thj

220 2/2/ jjjjj AQDNAIDN

where DNIj and DNQj are the digital values of the I and Q components of the

pixel from the start of the range line, and is the corresponding range

dependent gain. The offset is not used in SLC product generation. For complex data, radar brightness in decibels (dB) is given by:

thj

jA2

2210

0 2/2/log*10)( jjjjj AQDNAIDNdB

0 can be converted into using: 0

jjj sin*00

or

jjj dBdB sinlog*10)()( 1000

0 can be converted into using: 0

jjj tan*00

or

jjj dBdB tanlog*10)()( 1000

where j is the incidence angle at the range pixel. thj

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In Figure 17, dR is the Slant range distance entering into the definition of 0 .

0 represents the average reflectivity of a horizontal material sample, normalized with respect to a unit area on the horizontal ground plane. LA

0 is defined with respect to incident area , orthogonal to the incident ray from the radar .

iA

Figure 17. Definition of surface area and incident area used to derive 0 , 0 and 0 3

With newer SAR image formats, such as RADARSAT-2, for example, the calibrated data is read in on the fly, or calibration is performed during ingest with the SARINGEST algorithm.

3 Source: Raney (1998), fig.2-8, p.25 and CCRS (2007)


Figure 18. Depression, elevation, and incidence angle: j = j

Figure 19. Depression, elevation, and incidence angle: j ≠ j

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LLeessssoonn 11..55:: CCoonnvveerrssiioonn uuttiilliittiieess In this lesson, you will:

Convert complex data to other interpretations using the PSIQINTERP algorithm

Convert one matrix type to another using the PSCONV algorithm

Whether to meet the specific needs of a workflow or simply for data exploration, it is sometimes necessary to convert data from one format to another.

Geomatica 2014 includes two conversion algorithms to provide you with such flexibility: PSIQINTERP and PSCONV.

CCoonnvveerrttiinngg ccoommpplleexx ddaattaa ttoo ootthheerr iinntteerrpprreettaattiioonnss Some SAR-processing algorithms require detected data in the input; such as most SAR-speckle filtering algorithms (FEFROST, FELEE, FGAMMA, FTOUZI, et al).

The PSIQINTERP algorithm converts SAR images in single-look(SLC) or multilook (MLC) complex format to other interpretations, such as detected data or phase product. PSIQINTERP can also be used to extract the real or imaginary part of a complex number, or to store each part in a separate channel.

To convert a complex channel to other interpretations 1. In Focus, on the Tools menu, click Algorithm Librarian.


3. In the Find what box, type PSIQINTERP, and then click Find Next.

4. Double-click the PSIQINTERP algorithm. The PSIQINTERP Module Control Panel window appears.

5. Under Input Ports , click Browse, and then select the ~\SAR_Training\Radar\Flevoland_RS2_SLC\FQ29_20100507 \FQ29_20100507sig.pix file.

6. Select the first channel (HH).

7. Under Output Ports, click the Output: SAR Dataset entry, click Untitled.pix, and then click Browse.

8. In the File Selector window, open the ~\SAR_Training\Radar\Flevoland_RS2_SLC\FQ29_20100507 folder.

9. In the File Name box, type FQ29_20100507sig_HH_dB.pix, and then click Save. The new path and file name appear in the PSIQINTERP Module Control Panel window.

10. Click the Input Params 1 tab. The output data-interpretation options are displayed.

11. In the Output data interpretation list, click Decibel.

12. Click Run.


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Exercise 1: Using the PSIQINTERP algorithm, extract from FQ29_20100507sig.pix different complex channel interpretations for one or all channels.

CCoonnvveerrttiinngg aa mmaattrriixx rreepprreesseennttaattiioonn ttoo aannootthheerr mmaattrriixx ttyyppee PSCONV converts a polarimetric SAR (POLSAR) data set between different matrix formats, required by different polarimetric SAR algorithms. The available output-matrix formats depend on the input-matrix type, as described in the following procedure. Here, the entire input data set is converted.

To convert a matrix representation to another matrix type 1. In Focus, on the Tools menu, click Algorithm Librarian.


3. In the Find what box, type PSCONV, and then click Find Next.

4. Double-click the PSCONV algorithm. The PSCONV Module Control Panel window appears.

5. Under Input Ports , click Browse and select the ~\ SAR_Training\Radar\Flevoland_RS2_SLC\FQ29_20100507 \FQ29_20100507sig.pix file.


6. Select all channels (HH, HV, VH, and VV).

7. Under Output Ports, click Untitled.pix, and then click Browse.

8. In the File Selector window, open the ~\ SAR_Training\Radar\Flevoland_RS2_SLC\FQ29_20100507 folder.

9. In the File Name box, type FQ29_20100507sig_ c4r6c.pix, and then click Save. The new path and file name appear in the PSCONV Module Control Panel window.

10. Click the Input Params 1 tab..

11. In the Output matrix representation list, click c4r6c. This corresponds to a non-symmetrized covariance matrix.

12. Click Run. The original complex-scattering matrix (s4c) is converted to a non-symmetrized covariance matrix (c4r6c).

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You can use the PSCONV algorithm when a specific matrix format is required for a polarimetric algorithm that does not automatically perform the conversion. Some decomposition algorithms require a specific matrix representation as input. For example, the Cloude and Pottier decomposition (PSEBA) requires a filtered-coherency matrix while output from polarimetric filters corresponds to a covariance matrix.

Because the matrix type is read from the metadata, Geomatica will automatically convert the covariance matrix to the coherency matrix (matrix type) before applying the decomposition.

The following table lists the possible matrix conversions.

Conversion to ↓↓↓

s4c S3c s2c s1c c4r6c C3r3c t4r6c T3r3c k16r K9r

s4c -- √

S3c √ --

s2c --

s1c --

c4r6c √ -- √ √ √

C3r3c √ √ √ -- √ √ √ √

c2r1c √

t4r6c √ √ --

T3r3c √ √ √ -- √ √

k16r √ √ --

K9r √ √ --

c2r √

c1r √

In conversions between a symmetrized and a non-symmetrized form of the same matrix type, the cross-polarization terms are duplicated. In conversions between a non-symmetrized and a symmetrized form of the same matrix type, the cross-polarization terms are averaged to produce a single common value.

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MMoodduullee 22:: AAppppllyyiinngg rraaddiioommeettrriicc eennhhaanncceemmeennttss

AAbboouutt tthhiiss mmoodduullee Module 2 has three lessons:

Lesson 2.1: Applying SAR speckle filters

Lesson 2.2: Applying the PSBOXCAR filter to polarimetric data

Lesson 2.3: Applying the PSPOLFIL filter to polarimetric data

RRaaddiioommeettrriicc eennhhaanncceemmeenntt The goal of radiometric enhancement is to improve the interpretation of the radiometric information in an image using speckle and spatial filters. These filters can reduce speckle, detect edges, analyze texture, and visually enhance the image.

Image variance, or speckle, is a granular noise that is inherent to SAR imagery. Speckle gives a grainy, salt-and-pepper appearance and tends to be a dominating factor in radar imagery. Speckle filters are used primarily with radar data to remove high-frequency noise (speckle), while preserving high-frequency features (edges).

LLeessssoonn 22..11:: AAppppllyyiinngg SSAARR ssppeecckkllee ffiilltteerrss In this lesson, you will:

Filter an image using different SAR adaptive filters

Compare different filtering results from SAR adaptive filters

SSAARR ssppeecckkllee ffiilltteerrss Coherent signal-scattering in SAR data often causes image speckle or a salt-and-pepper effect. Speckle is inherent to most SAR images, and can inhibit accurate image interpretation. There are several types of speckle filters and they generally fall into either of two general categories:

Non-adaptive, or template

Adaptive

Module 2: Applying radiometric enhancements SAR processing with Geomatica

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Non-adaptive filters apply to the parameters of the whole image. They do not take into account the local properties of terrain backscatter or the nature of the sensor. Examples of non-adaptive filters are the Mean, Median, Edge Detection, and Sieve filters.

Adaptive filters accommodate changes in local properties of the terrain backscatter. They modify the image based on statistics extracted from the local environment of each pixel. The main advantage of adaptive filters is that they maintain an accurate estimate of the backscattering coefficient inside homogeneous (stationary) areas, while preserving edge and texture structure in nonstationary scenes. Examples of adaptive filters are the Frost, Lee, Gamma, and Touzi filters. Adaptive filters are designed specifically for SAR images.

The ideal speckle-reduction filter reduces the speckle, but has minimal loss of information. The filter should preserve radiometric information in homogeneous areas, and preserve radiometric information and spatial-signal variability (textural information) in textured areas.

With the FSPEC algorithm, which is a SAR-speckle filter, you can select which SAR speckle filter you want to apply to your data. From a single panel, you can select from many SAR speckle filters to apply.

The filter algorithms require that the signal represents power. If the input image is in amplitude format, each gray level will be squared to derive power, and then the square root will be applied to the filtered result.

In this lesson, you will apply and compare several adaptive SAR-speckle filters.

AAppppllyyiinngg aann eennhhaanncceedd FFrroosstt ffiilltteerr With the Frost filter, the unspeckled pixel value is estimated for each pixel by computing the weighted sum of the center pixel value, the mean value, and the variance calculated in a circular kernel surrounding the pixel. The enhanced Frost filter minimizes the loss of radiometric and textural information.

Data preprocessing Using the concepts learned in Lesson 1.4: Ingesting and extracting a calibrated backscatter image from SAR data and Lesson 1.5: Conversion utilities and the Vancouver scene located in ~\SAR_Training\Radar\Vancouver_RS2_FQ2_SLC, do the following:

1. Ingest the product.xml file into a PCIDSK file using sigma as the calibration type. Name the file Van_RS2_FQ02sig.pix.

2. Use the PSIQINTERP algorithm to convert the complex data to detected data. Convert the HH and the HV channel to Intensity. Name the file Van_RS2_FQ02sig_HH.pix. This creates a simulated dual pol image.

To open the FSPEC algorithm 1. In Focus, on the Tools menu, click Algorithm Librarian.


SAR processing with Geomatica Module 2: Applying radiometric enhancements

3. In the Find what box, type FSPEC, and then click Find Next.

4. Double-click the FSPEC algorithm. The FSPEC Module Control Panel window appears.

To set up the parameters for FSPEC 1. Under Input Ports, expand the Input: Unfiltered Layers entry.

The tree expands to show the array segments.

2. Click Browse, open the ~\SAR_Training\Radar\Vancouver_RS2_FQ2_SLC folder, select the Van_RS2_FQ02sig_HH.pix, and then select the HH channel for this file.

3. Under Output Ports, click the Output: Filter Layer(s) entry, click Untitled.pix, and then click Browse. The File Selector window opens.

4. Open the Radar Data folder.

5. In the File Name box, type Van_RS2_FQ02sig_HH_FFrost7.pix.

6. Click Save. The path and file name are updated in the FSPEC Module Control Panel window.


8. In the Image Units list, select Power. Image units specify the image format of the radar image. Note: Intensity = Power, Amplitude = Magnitude

9. In the Filter Type list, select Enhanced Frost.

10. In the Filter X list, select 7.

11. In the Filter Y list, select 7. The filter kernel size will be 7 x 7 pixels.

12. In the Number of Looks box, enter 1.

13. In the Damping Factor box, you need not change the default value of 1. You must enter correct parameters for the image files you are working with. The number of looks and the image type information is available in the format definition included with your data or in the file metadata.

14. Click Run. The Enhanced Frost filter runs and the result displays appear in Focus.

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AAppppllyyiinngg aa TToouuzzii ffiilltteerr Unlike other filters, the Touzi filter adapts the size and shape of the filter processing window based on signal nonstationarity. It also has a multiresolution ratio edge detector for better filtering of contours and edges. This is effective when applied to fine structures, such as roads and trails, which are generally smoothed out by other filters.

To apply a Touzi Filter 1. In the FSPEC Module Control Panel window, click the Files tab.

2. Right-click Van_RS2_FQ02sig_HH_FFrost7.pix, and then click Browse.

3. Open the ~\SAR_Training\Radar\Vancouver_RS2_FQ2_SLC folder.

4. In the File Name box, type Van_RS2_FQ02sig_HH_FTouzi11.pix.

5. Click Save. The path and file name are updated in the FSPEC Module Control Panel window.


7. In the Image Units list, select Power.

8. In the Filter Type list, select Touzi.


9. In the Filter X list and the Filter Y list, respectively, select 11. The filter kernel size will be 11x11 pixels.

10. In the Number of Looks box, enter 1.

11. In the Contour Threshold box, type 0.5.

12. In the Edge Threshold box, type 0.5.

13. In the Gradient Threshold box, type 0.1.

14. Click Run. The Touzi filter runs and the results appear in Focus.

The two SAR speckle filtered images can now be compared in Focus. Exercise 1: With FSPEC, run the enhanced Frost filter again on the same file (Van_RS2_FQ02sig_HH.pix). Specify a window size of 11. Specify the following name in the Output: File Layer (s) port: Van_RS2_FQ02sig_HH_FFrost11.pix.

Run the Average filter to get another comparison basis to evaluate the results obtained from the SAR adaptive filters.

Compare the results to the original HH SLC channel (Van_RS2_FQ02sig_SLC.pix), both visually and numerically.

Stats: HH SLC • Water: 0.343±0.35 (med: 0.226) • Forest: 0.199±0.24 (med: 0.12) •H-D. Urban: 2.159±5.56 (med: 0.50) • L-D. urban: 0.212±0.32 (med: 0.11)

HH SLC (non filtered) HH SLC (non filtered), sites

Stats: HH Enh. Frost 7x7 • Water: 0.341±0.14 (med:0.316) • Forest: 0.198±0.13 (med:0.173) • H-D. Urban: 2.062±4.59 (med:0.669) • L-D. urban: 0.214±0.21 (med:0.164)

HH Enh. Frost 7x7 (HH SLC) – (Enh. Frost 7x7)

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Stats: HH Enh. Frost 11x11 • Water: 0.340±0.12 (med:0.314) • Forest: 0.198±0.12 (med:0.175) • H-D. Urban: 2.150±5.34 (med:0.559) • L-D. Urban: 0.214±2.26 (med:0.160)

HH Enh. Frost 11x11 (HH-SLC) – (Enh. Frost 11x11)

Stats: Touzi 11x11 • Water: 0.263±0.102 (med:0.258) • Forest: 0.152±0.077 (med:0.148) • H-D. Urban: 1.515±3.03 (med:0.592) • L-D. urban: 0.163±0.117 (med:0.133)

Touzi 11x11 (HH SLC) – (Touzi 11x11)

Stats: Average 7x7 • Water: 0.338±0.65 (med:0.329) • Forest: 0.197±0.05 (med:0.19) • H-D. Urban: 2.126±2.06 (med: 1.38) • L-D. urban: 0.227±0.32 (med:0.08)

Average 7x7 (HH SLC) – (Average 7x7)

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Stats: Average 11x11 • Water: 0.331±0.03 (med:0.330) • Forest: 0.196±0.03 (med:0.190) • H-D. Urban: 2.184±5.56 (med:1.39) • L-D. urban: 0.228±0.32 (med:0.05)

Average 11x11 (HH SLC) – (Average 11x11)

Figure 20. Result of different filters and windows size

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LLeessssoonn ssuummmmaarryy In this lesson, you:

Filtered an image using enhanced Frost, Gamma, and Touzi adaptive filters

Visually and numerically compared the results of the different filter operations


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LLeessssoonn 22..22:: AAppppllyyiinngg tthhee PPSSBBOOXXCCAARR ffiilltteerr ttoo ppoollaarriimmeettrriicc ddaattaa In this lesson, you will:

Apply a polarimetric filter to a RADARSAT-2 image

Compare the effect of different processing window sizes

The PSBOXCAR algorithm applies a boxcar filter to an input polarimetric SAR (POLSAR) data set. The boxcar filter replaces every image pixel by an average of pixel values in a window centered on the pixel. All of the raster channels in the input image are filtered independently.

Boxcar filtering is usually used to increase the effective number of looks (ENL) in the data. This step is required if an algorithm requires a minimum ENL that is higher than the ENL of the input data. The input image must represent a non-symmetrized or symmetrized fully polarimetric data set, which could be in scattering (s4c, S3c or s2c), covariance (c4r6c or C3r3c), coherency (t4r6c or T3r3c), or Kennaugh (k16r or K9r) matrix format.

Input data sets in scattering-matrix format are converted to the covariance-matrix format before filtering. For such data, the output-matrix type and the number of channels in the output file depend on the input-matrix type, as follows:

Input s4c: output c4r6c, 10 channels

Input S3c: output c3r3c, 6 channels

Input s2c output c2r1c, 3 channels

For input data in all other matrix formats, the output-matrix type and the number of channels in the output are the same as those in the input. The physical quantity of output pixel values (sigma-0, beta-0, or gamma-0) is always the same as in the input data set.

AAppppllyyiinngg tthhee PPSSBBOOXXCCAARR ffiilltteerr You apply the PSBOXCAR filter using the PSBOXCAR algorithm.

To open the PSBOXCAR algorithm 1. In Focus, on the Tools menu, click Algorithm Librarian.

The Algorithm Librarian window opens.


3. In the Find what box, type PSBOXCAR, and then click Find Next.

4. Double-click the PSBOXCAR algorithm. The PSBOXCAR Module Control Panel window appears.


To set up the parameters for PSBOXCAR 1. Under Input Ports, expand the Input: Input polarimetric SAR image

entry.

2. Click Browse, open the ~\SAR_Training\Radar\Vancouver_RS2_FQ2_SLC folder, and then select Van_RS2_FQ02sig_SLC.pix.

3. Select each of the channels in the list.

4. Under Output Ports, expand the Output: Output polarimetric SAR filtered image entry, select Untitled.pix, and then click Browse. The File Selector window appears.

5. In the File name box, type Van_RS2_FQ02sig_PSBOXCAR_7.pix, and then click Save.

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7. In the Horizontal filter size list and Vertical filter size list, respectively, select 7. The choice of a particular window size will have a strong impact on the results produced by a polarimetric decomposition algorithm.

8. Click Run.

Repeat steps 3 to 8, specifying a different window size, and then compare the results.

SLC 3x3 5x5

7x7 9x9 11x11

15x15 21x21 31x31

Figure 21. Various window sizes with the PXBOXCAR algorithm

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Applied a polarimetric filter to the RADARSAT-2 image

Compared the effect of different processing window size


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LLeessssoonn 22..33:: AAppppllyyiinngg tthhee PPSSPPOOLLFFIILL ffiilltteerr ttoo ppoollaarriimmeettrriicc ddaattaa In this lesson, you will:

Apply a polarimetric filter to the RADARSAT-2 image

Compare the effect of different processing window size

The PSPOLFIL algorithm applies a speckle filter to the fully polarimetric SAR data set. Unlike the boxcar filter, this filter preserves the edges. Edge-detection and homogeneity parameters are estimated from the local power image.

The input image must represent a non-symmetrized or symmetrized fully polarimetric (quad-polarization, complex) data set in the scattering (s4c or S3c), covariance (c4r6c or C3r3c), coherency (t4r6c or T3r3c), or Kennaugh (k16r or K9r) matrix format.

Input data sets in the scattering-matrix format are converted to the covariance-matrix format before filtering. For such data, the output-matrix type and the number of channels in the output file depend on the input-matrix type, as follows:

Input s4c: output c4r6c, 10 channels

Input S3c: output C3r3c, 6 channels

For input data in all other matrix formats, the output-matrix type and the number of channels in the output file are the same as in the input file.

AAppppllyyiinngg aa ssppeecckkllee ffiilltteerr The PSPOLFIL algorithm applies a speckle filter.

To open the PSPOLFIL algorithm 1. In Focus, on the Tools menu, click Algorithm Librarian.


3. In the Find what box, type PSPOLFIL, and then click Find Next.

4. Double-click the PSPOLFIL algorithm. The PSPOLFIL Module Control Panel window appears.

To set up the parameters for PSPOLFIL 1. Under Input Ports, expand the Input: Input polarimetric SAR image

entry, click Browse, and then open the Van_RS2_FQ02sig_SLC.pix file.


3. Under Output Ports, expand the Output: Output polarimetric SAR filtered image entry, select Untitled.pix, and then click Browse. The File Selector window opens.

4. In the File Name box, type Van_RS2_FQ02sig_PSPOLFIL_7.pix, and then click Save.



6. In the Filter size list, select 7. Note: The choice of a particular window size will have a strong impact on the results produced by a polarimetric decomposition algorithm.

7. Accept the default Number of Looks (1). Note: You can estimate the ENL by calculating the mean squared to variance ratio (of the intensity data) in a homogenous area.

8. Click Run.

Exercise 3: When the module runs to completion, open the file in Focus, and then compare the results of this filter to those of the boxcar filter.

SLC 3x3 5x5

7x7 9x9 11x11

15x15 21x21 31x31

Figure 22. PSPOLFIL results for different window size

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Filtered a quad-polarized image using the PSPOLFIL algorithm

Compared the PSPOLFIL filtering with PSBOXCAR and the original unfiltered data

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MMoodduullee 33:: PPoollaarriimmeettrriicc ddeeccoommppoossiittiioonnss


Lesson 3.1: Freeman-Durden decomposition

Lesson 3.2: Cloude-Pottier decomposition

Lesson 3.3: Touzi decomposition

PPoollaarriimmeettrryy ffuunnddaammeennttaallss The study of polarimetry refers to the science behind, and the techniques relating to, the analysis of the polarimetric properties of electromagnetic waves, and the scattering of these waves.

When orthogonal polarization combinations are used, the complete scattering properties of a radar target can be determined for a specific frequency, incidence angle, and azimuth direction. The extraction of information, such as terrain classification, is an important component of polarimetry.

In single-polarized images, the return is proportional to the target cross-section. The amount of backscattering the radar system measures is dependent on the target, the relative orientation of the target and radar system, and the polarization.

Figure 23. Diversity of polarization modes4 4 Source: JAXA (2012), adapted from eoPORTAL

Module 3: Polarimetric decompositions SAR processing with Geomatica

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With a monostatic (that is, the XT

and XR

antennas are co-located) polarimetric

radar, the transmitting antenna ( XT

) and the receiving antenna ( XR

) can be modeled as a vector containing two polarization states that form an orthogonal basis. Because the polarization state of the transmitted signal is known, and the state of the received signal, the polarimetric properties of the target can be represented. This matrix is called the complex-scattering matrix ( ). S

V

HTX

,

V

HRX

SRT XX T

, HH HV

VH VV

S SS

S S

It is important to stress that the retrieval of the polarimetric properties of a target is possible only if the phase information of the H and V polarization is carefully controlled in the SAR hardware, and carefully measured and preserved at the signal-reception and focusing (processing) stages.

There are numerous advantages to using polarimetric data for image analysis. Agricultural applications include crop-type identification, crop-condition monitoring, and soil-moisture measurement. Forestry applications include biomass estimation and species identification. Other applications of polarimetric data include geology, hydrography, oceanography, and wetlands mapping.

CCaannoonniiccaall ttaarrggeettss Canonical targets correspond to simple geometric structures whose interpretation of diffusion is facilitated by the presence of symmetry plane in the matrices used to represent them. Interpretation of polarimetric responses, like that of the parameters from a polarimetric decomposition, is often based on a comparison with the canonical targets.

Target Scattering matrix (H-V basis)

Co-pol polarimetric response

Sphere =1 0

0 1S

SAR processing with Geomatica Module 3: Polarimetric decompositions

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Trihedral = 1 0

0 1S

Dihedral =1 0

0 1S

For an Oriented Dihedral

2cos2sin

2sin2cosS

Horizontal Dipole

1 0

0 0S


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0 1S

Vertical Dipole

0 0

Oriented Dipole

2 21cos sin

2S2 21

sin sin2

Left Helix

1

1

jS

j

Right Helix

1

1

jS

j


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Figure 24. Canonical targets representation (in H-V basis), (Source: van Zyl et Ulaby (1990), p.33-45.)

The sphere and the trihedral are both characterized by an odd number of bounces, which results in a 0° phase difference in the backscatter alignment (BSA) convention. Each produces a uniform scattering for all linear polarizations (χ=0°), which results in HH = VV. For an even number of bounces, (dihedral, for example)

a

rieve

ding

s not equal to 0° (HH) or 90° (VV).

tion, because it does not correspond to a ering can occur in an urban environment

ized and strong scattering. These pixels are called

the target introduces a 180° phase difference between the HH and VV polarization and HH = -VV.

However, the HH and VV channels are still equal in intensity. If the dihedral inplane perpendicular to the radar line of sight (LOS) is rotated, it introduces a depolarization of the signal, and the HV and VH channels are no longer equal tozero. For a pure canonical dihedral, the signal is repolarized more than depolarized. If the value of the angle ϕ is found, it is possible to cancel out its effect and reta pure dihedral scattering.

A dipole will produce a strong scattering in only one polarization channel accorto its orientation. Like the dihedral, it is possible to cancel out the effect of the orientation angle ϕ for the case where it i

Finally, the helix is an abstract construcreal physical target. This kind of scattwhere multiple scattering is common. One way to produce a pure-helix scattering isto place two dihedrals, oriented at 45° from each other. The phase difference between the HH and VV channel will be 180°.

If a pixel, or a small group of pixels, corresponds to a canonical target, it will generally produce a highly polarpoint targets or coherent point targets.

BBackscattering mechanisms ackscattering mechanisms

(mul mate the dominant scattering mechanisms, if any,

at

g from the soil surface

rough

to corner reflection from tree trunks

(e) Other interaction components due to multiple scattering between foliage and ground surface, big branches and surface, foliage and shrub canopy, and so forth"

The reality, however, is often more complex and only a small fraction of an image pixel corresponds to a coherent point target. The scattering mechanisms tend to be horizontally and vertically superposed. In such a case, it is necessary to average

tilook) several pixels, to estiand the degree of polarization.

For example, a "forest stand can be broken down into individual components thdrive radar backscatter, these components are (Lo, 1998):

(a) Direct backscatterin

(b) Volume scattering from foliage, shrub canopy, and leaf litter if present

(c) Direct backscattering from big branches and trunks if they areor at normal incidence

(d) Interaction components due


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With the same area, depending on the characteristics of the sensor (incidence and orientation angle, spatial resolution, and wavelength), different scattering mechanisms might dominate the scattered signal. Several polarimetric decompositions have been proposed to facilitate the interpretation of the scattered mechanism of a full-polarimetric image.

Figure 25. Horizontal and vertical superposition of scattering mechanisms5

oPPollaarriimmeettrriicc ddeeccoommppoossiittiioonn

incoherent; that is, they cover more than one pixel, are partially polarized, and in most cases have more than one scattering mechanism for any given pixel. The complex-scattering matrix ([S]) is no longer appropriate to represent an incoherent target, and a second-order representation is needed. Several matrices can be used to represent an incoherent target: the Mueller ([M]), Kennaugh ([K]), the coherency ([T]) and the covariance ([C]) matrices are used commonly:

There are two families of polarimetric decomposition: the coherent and the incoherent decomposition.

The coherent target decompositions are applied only on single-look complex (SLC) images and generally pixel-by-pixel for the characterization of a point target. Although a dominant scattering mechanism can be found for each pixel of an image, a coherence test is generally applied on each before the decomposition to ensure a meaningful result. This topic will be covered in Lesson 4 of this course.

As mentioned previously, most natural targets, called extended targets, are

, 2 ,T

c hh hv vvk S S S

5 Source: Lo (1998)


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3T

c cC k k

2 * *

2* *

2* *

2

2 2 2

2

hh hh hh vv

hv hv hv vv

vv hh vv hv vv

hv

hh

S S S S S

S S S S S

S S S S S

Covariance matrix

1,h vv hh vvS S S S , 2

T

t h hvk S

2

*3

Tt tT k k

*2 *

* 2 *

* * 2

2

12

2

2 2 4

hh vv hh vv hh vv hh vv hv

hh vv hh vv hh vv hh vv hv

hv hh vv hv hh vv hv

S S S S S S S S S

S S S S S S S S S

S S S S S S S

Coherence matrix

Where ... indicates a temporal or spatial ensemble averaging. Symmet

are presented by

rized cases

(HV=VH).

mposition theory" is to express the ndependent elements to associate a

its on

zi, 2007)". The angle ϕ of Figure 24

T bd

PPhh

The "objective of the incoherent target decoaverage-scattering mechanism as the sum of iphysical mechanism with each component. Target-scattering decomposition permthe extraction of target-characteristic information, provided that the decompositisatisfies the general requirement of being robust under a change of wave polarization basis (that is, roll invariant) (Toucan be calculated and its effect compensated. (Touzi, 2007)"

here are several types of incoherent decomposition, but each can be distinguishedy two broad classes: the physical and the mathematical incoherent ecomposition.

yysical incoherent decomposition sical incoherent decomposition

bauthor he total power (that is, the span) could be decomposed

first-order Bragg surface scattering. This component contains the contribution of all

example, a trihedral will surface component.

.

The Freeman-Durden decomposition (PSFREDUR) corresponds to a model-ased decomposition of the coherency matrix developed for forested areas. The

hypothesized that tas the sum of three scattering mechanisms:

Odd: corresponding to a slightly rough surface modeled as a

single-bounce or odd-bounce scatterers. Forproduce a strong contribution to the slightly rough

Double-bounce: modeled as a dihedral corner reflector and representingthe ground-tree-trunk scattering (enhanced when the forest is inundated)


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Volume: modeled as the contribution from a cloud of randomly oriented cylinder-like (dipole) scatterers that represent the volume scattering from a forest canopy.

Today, this decomposition is used to decompose (interpret) the scattering of many

MM

environments, such as urban areas, wetlands, and agricultural lands.

aatthheemmaattiiccaall--iinnccoohheerreenntt ddeeccoommppoossiittiioonn

ition, while mathematically unique, is not necessarily a n compared to a physical decomposition). This is

ering

The mathematical decompositions are not based on a physical model; however, they can be applied to the analysis of all kinds of land-use and land-cover classes.The current mathematical decompositions are based on an eingenvector decomposition of the coherency matrix, which is analogous to a principal-component analysis (PCA).

The eigenvector decomposstraightforward interpretation (whebecause there is no guarantee that the eigenvectors will represent any known physical scattering mechanism directly (van Zyl and Kim, 2011).

In this module, the results of two mathematical incoherent decompositions will be examined: the Cloude-Pottier and the Touzi decomposition.

The Cloude and Pottier eigen decomposition proposes an unsupervised classification of the coherency matrix. Many parameters representing the scattcharacteristics of a distributed target are derived from the resulting eigenvectors and eigenvalues. Among the parameters, the average alpha ( ) and Beta ( ) angles are the most used. While corresponds to the average scattering mechanism of an extended target, correspond to its orientation around the radar line of sight (LOS).

Derived from the eigenvalues, the entropy (H), and the Anisotropy (A) complete the description of the scattering type. The entropy (H) is used to characterize the

nce of a domi hich m

scattering randomness. A low entropy value (H ≈ <0.3) indicates the presenant and deterministic scattering mechanism (dominant eigenvalue), w

eans that only one scattering mechanism is contributing significantly to .

H increases, the randomness of the scattering increases, which indicates a reater mix between different kinds of scattering mechanisms. At H=1, the cattering is random (completely depolarized) corresponding to pure white noishe polarimetric anisotropy (A) measures the relat

As gs e. T ive importance of the second and

ond sign

T

third eigenvalues. A high anisotropy value indicates the presence of a secificant scattering mechanism, along the dominant scattering mechanism.

he average alpha angle and the Entropy are often combined to form the H / e, which leads to an unsupervised classification of a polarimetric image into the

lowing nine zone classes: plan fol

Zone 9: Low-entropy surface scatter. Low-entropy scattering processes with values less than 42.5° occur. These include GO (geometrical optics) and PO (physical optics) surface scattering—surface scattering and specular scattering phenomena which do not involve 180° phase inversions between HH and VV.

Bragg

Physical surfaces, such as water at L and P-bands, sea-ice at L-band, and very smooth land surfaces, all fall into this category.


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Zone 8: Low-entropy dipole scattering. Strongly correlated mechanisms that have large imbalances between HH and VV in amplitude. An isolated dipole scatterer would appear here, as would scattering from vegetation with strongly correlated orientation of anisotropic scatteringelements. The width of this zone is determined by the ability of the radato measure the HH/VV ratio; that is, on the quality of the calibration.

Zone 7: Low-entropy multiple scat

r

tering events. Corresponds to low-entropy double, or even-bounce scattering events, such as those provided by isolated-dielectric and metallic-dihedral scatterers. These are characterized by > 47.5°.

Zone 6: Medium-entropy surface scatter. Reflects the increase in entropy H due to changes in surface roughness and canopy-propagation effects. In surface-scattering theory, the entropy H of low-frequency Bragg scattering is zero. As the roughness and correlation length of a surface increases, its entropy H also increases. Furthermore, a surface cover comprising oblate-ellipsoidal scatterers (leafs or discs for examplegenerates an entropy 0.6 < H < 0.7.

Zone 5: Medium-entropy vegetation scattering. Moderate entropy H,but with a dominant dipole-type scattering mechanism. The increased entropy H is due to a central statistical distribution of orientation angles. Such a zone includes scattering from vegetated surfaces with anisotroscatterers and moderate correlation of scatterer orientations.

)

pic

zones 4, 5, 6, and 1,

Zone 4: Medium-entropy multiple scattering. Accounts for dihedral scattering with moderate entropy H. This occurs, for example, in forestry applications, where double-bounce mechanisms happen at P and L bandsfollowing propagation through a canopy. The effect of the canopy is to increase the entropy H of the scattering process. A second important process in this category is urban areas, where dense packing of localizedscattering centers can generate moderate entropy H with low-order multiple scattering dominant. The boundary between2, 3 is set as 0.9. This is chosen on the basis of the upper limit for surface, volume, and dihedral scattering before random distributions apply.

Zone 3: High-entropy surface scatter. Not part of the feasible region in H- space (surface scattering with entropy H>0.9 cannot be distinguished). This is a direct consequence of the increasing inability to classify scattering types with increasing entropy. It is included to reinforce the idea that increasing entropy really does limit the ability to use polarimetric behavior to classify targets. Radar polarimetry will then be most successfully applied to low-entropy problems.


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Zone 2: High-entropy vegetation scattering. High-entropy volume scattering arises when

d of= 45° and H > 0.9. This can occur for single

scattering from a clou anisotropic needle-like particles, or for multiple scattering from a cloud of low-loss symmetric particles. In both cases, however, the entropy H lies above 0.9, where the feasible region of H/ plane is shrinking rapi y. Scattering from forest canopies lies in this region, as does the scattering from some types of vegetated surfaces with random highly anisotropic scattering elements. The extreme behavior in this class is random noise; that is, no polarization dependence, a point which lies to the extreme right in Figure 26.

Zone 1: High-entropy multiple scattering. ln the H> 0.9 region, it is still possible to distinguish double-bounce mechanisms in a high-entropy environment. Again, such mechanisms can be observed in forestry applications or in scattering from vegetation with a well-developed branch and crown structure.6

dl

Figure 26. H- classification plane7

Touzi decomposition (PSTOUZIDEC) is based The on the characteristic , the characteristic

decomposition leads to the representation of the coherency matrix as the

positive eigenvalues (

decomposition of the coherency matrix. For reciprocal targets

incoherent sum of three single scatterers, each weighted by its normalized and

i , i =1, 2, 3).

6 Source: Cloude, S.R., Pottier, E. (1997). An Entropy based Classification scheme for Land Applications of Polarimetric SARs. IEEE Transactions on Geoscience and Remote Sensing. Vol.35, no.2, p.68–78. 7 Source: Lee et Pottier, 2009


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The Touzi decomposition uses the Touzi scattering-vector model to represent each coherency eigenvector with unique target characteristics. Each coherency eigenvector is uniquely characterized by five independent parameters. Scattetype is described with a complex entity, whose magnitude ( si

ring ) and phase ( si )

characterize the magnitude and phase of target scattering. T helicity (he i )

osition except that:

It does not proceed to the weighted sum of each eigenvectors’s parameters by their respective eigenvalues

It takes into account the polarimetric phase (

characterizes the symmetric-asymmetric nature of target scattering.

The Touzi decomposition is similar to the Cloude-Pottier decomp

si )

It compensates for symmetric-asymmetric nature of target scattering ( i )

The orientation angle ( i ) is the conventional Huynen tilt angle.

Target scattering can be characterized by a deep analysis of the parameters of the three eigenvectors. Touzi and others have shown that the analysis of dominant scattering parameters can lead to efficient wetland classification

The psi angle compensates for the rotation of an object in the plane perpendicular to the radar line of sight (LOS) to provide an unbiased estimation of the scattering type (alpha angle). The Touzi decomposition also compensates for a lack of symmetry (of an object), in a plane parallel to the radar LOS based on the value stored in the helicity parameter.

A symmetric object will correspond to a helicity value close to zero, while an asymmetric object will correspond to higher positive and negative helicity values. Target asymmetry will introduce a helix-like type of scattering.


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LLeessssoonn 33..11:: FFrreeeemmaann--DDuurrddeenn ddeeccoommppoossiittiioonn In this lesson, you will:

Ingest a RADARSAT-2 quadpol image using the SARINGEST algorithm

Apply a boxcar filter on the ingested SAR data with the PSBOXCAR algorithm

Use the PSFREDUR algorithm to perform the Freeman-Durden decomposition

Use Focus EASI Modeling to create a normalized version of the Freeman-Durden parameters

IInnggeessttiinngg aa qquuaaddppooll iimmaaggee To ingest a quadpol image, you use the SARINGEST algorithm.

To open the SARINGEST algorithm 1. In Focus, on the Tools menu, click Algorithm Librarian.


3. In the Find what box, type SARINGEST, and then click Find Next.

4. Double-click the SARINGEST algorithm. The SARINGEST Module Control Panel window appears.

5. Under Output Ports, in the Output: SAR Dataset entry, select Untitled.pix, click Browse, and then open the ~\SAR_Training\Radar\Flevoland_RS2_SLC\FQ29_20100507 folder.

6. Enter FQ29_20100507sig as the output file name.

7. Click the Input Params 1 tab, beside the File box, click Browse, and then select the product.xml file.

8. In the Data Calibration menu, click Sigma Nought as the calibration type.

AAppppllyyiinngg tthhee bbooxxccaarr ffiilltteerr You apply a boxcar filter using the PSBOXCAR algorithm.

To set up the parameters for PSBOXCAR 1. Under Input Ports, expand the Input: Input polarimetric SAR image

entry.

2. Click Browse, open the ~\SAR_Training\Radar\Flevoland_RS2_SLC\FQ29_20100507 folder, and then select FQ29_20100507sig.



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4. Under Output Ports, in the Output: Output polarimetric SAR filtered image entry, select Untitled.pix, and then click Browse. The File Selector window appears.

5. In the File name box, type FQ29_20100507sig_PSBOXCAR_7.pix, and then click Save.


7. In the Horizontal filter size list and the Vertical filter size list, respectively, select 7.

8. Click Run.

PPeerrffoorrmmiinngg tthhee FFrreeeemmaann--DDuurrddeenn ddeeccoommppoossiittiioonn You apply a Freeman-Durden decomposition using the PSFREDUR algorithm.

To open the PSFREDUR algorithm 1. In Focus, on the Tools menu, click Algorithm Librarian.


3. In the Find what box, type PSFREDUR, and then click Find Next.

4. Double-click the PSFREDUR algorithm. The PSFREDUR Module Control Panel window appears.

To set up the PSFREDUR algorithm 1. Under Input Ports, click Browse, and then open the

~\SAR_Training\Radar\Flevoland_RS2_SLC\FQ29_20100507 folder.

2. Select the FQ29_20100507sig_PSBOXCAR_7.pix file.

3. Select each raster of the imported file.

4. Under Output Ports, click Browse, and then enter FQ29_20100507sig_PSBOXCAR_7 _PSFREDUR.pix as the name of the output file.

5. Click Run.

To view the result of PSFREDUR 1. In Focus, on the File menu, click Open.

2. Open the ~\SAR_Training\Radar\Flevoland_RS2_SLC\FQ29_20100507 folder.

3. Select the FQ29_20100507sig_PSBOXCAR_7 _PSFREDUR.pix file, and then click Open.

Exercise 1: Examine an RGB composite made of the Freeman-Durden parameters, and each parameter in grayscale. You can compare the Freeman-Durden decomposition to the Landsat-5 mosaic provided with this course:

~\SAR_Training\Landsat\Flevoland_L5_20100906_p198_r23r24.pix


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Exercise 2: Locate and examine some features that are producing a strong scattering in only one of the components, in two components, or in all components.

For example, locate the bright area centered on 1789P, 3073L (671 036E, 5 823 936N). This industrial area produces a strong double-bounce scattering, as predicted by the theory, but many buildings seem also to be characterized by a stronger volume-scattering compared to the surrounding forest, which is less intuitive. This is because the Freeman-Durden decomposition directly uses the HV channel to calculate the volume contribution of the total backscattered signal.

In urban areas, many scattering processes can create a strong return in HV, which are not volumic by nature. Among these are multiple scattering (the addition of many scattering mechanisms in one cell of resolution), a building not aligned with the radar line of sight (orientation effects), or a non-symmetric object.

Interpreting the Freeman-Durden decomposition can be easier by normalizing the radiometry to calculate the fraction of the total scattered power associated with each component.

PPrroodduucciinngg aa nnoorrmmaalliizzeedd vveerrssiioonn ooff tthhee FFrreeeemmaann--DDuurrddeenn ddeeccoommppoossiittiioonn You can produce a normalized version of the Freeman-Durden decomposition in Focus with EASI Modeling.

To add four new 32-bit real channels 1. In Focus, click the Files tab, and then select the

FQ29_20100507sig_PSBOXCAR_7 _PSFREDUR.pix file.

2. Select New Raster Layer.

3. Add four new 32-bit real channels.

To normalize the decomposition 1. In Focus, on the Tools menu, click EASI Modeling.

2. In the Input File list, click the FQ29_20100507sig_PSBOXCAR_7 _PSFREDUR.pix file.

3. In the box below the Input File list, enter the code as shown in the following figure. Note: Comments indicated by "!" can be omitted Note: The code can be loaded from: ~\SAR_Training\EASI_script\Module3_FreDur_Norm.eas


4. Click Run.

Both versions can now be compared; each has its own utility. The power version is often preferred if the components are to be used in a classification, while the normalized version helps to better understand the composition of backscattered power in mixing of scattering mechanisms.

Power version (RGB) R: Double G: Volume B: Surface

Normalized version (RGB) R: % Double G: %Volume B: % Surface

Total power (span)

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% Double % Surface % Volume

Power, Double Power, Surface Power, Volume

HH VV HV

Figure 27. PSFREDUR, Freeman-Durden decomposition results

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Ingested a RADARSAT-2 quadpol image using SARINGEST

Applied a PSBOXCAR filter on the ingested SAR data

Used PSFREDUR to perform the Freeman-Durden decomposition

Used Focus EASI Modeling to create a normalized version of the Freeman-Durden’s parameters


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LLeessssoonn 33..22:: CClloouuddee--PPoottttiieerr ddeeccoommppoossiittiioonn In this lesson, you will:

Use PSEABA to perform the Cloude-Pottier decomposition

Use EASI modeling to analyze of the Cloude-Pottier parameters

PPeerrffoorrmmiinngg tthhee CClloouuddee--PPoottttiieerr ddeeccoommppoossiittiioonn To perform the Cloude-Pottier decomposition, you use the PSEABA algorithm.

To open the PSEABA algorithm 1. In Focus, on the Tools menu, click Algorithm Librarian.


3. In the Find what box, PSEABA, and then click Find Next.

4. Double-click the PSEABA algorithm. The PSEABA Module Control Panel window appears.

To set up the PSEABA algorithm 1. Under Input Ports, click Browse, and then open the




4. Under Output Ports, click Browse, and then enter FQ29_20100507sig_PSBOXCAR_7_PSEABA.pix as the name of the output file.

5. Click the Input Params 1 tab, and then select the Retrieve eigenvalues and eigenvectors check box.

6. Click Run

Exercise 3: Examining the PSEABA decomposition results using thresholds.

Examine the Cloude-Pottier decomposition parameters. Keep in mind that the entropy is stored in channel 1 (%1), the anisotropy in channel 2 (%2), the alpha angle in channel 3 (%3), and the beta angle in channel 4 (% 4).

1. Find the targets characterized by a double-bounce ( ≥ 55°).

2. Are these double-bounce targets characterized by a low entropy? What is the mean entropy of these targets (mean = 0.61±0.2, med = 0.63)?

3. Is the double-bounce scattering generally characterized by one dominant or two scattering mechanisms ( ≥ 55° U A≥0.8)?

4. What is the mean entropy of ≥ 55° U A ≥0.8 and ≥ 55° U A< 0.8?


Alpha angle ( ) Beta angle ( ) Entropy (H)

Anisotropy (A) Red = ≥55° Red= ≥55° and A<0.8

Green = ≥55° and A≥0.8

Figure 28. Cloude-Pottier parameters and thresholding result

It appears that there are two classes of double-bounce scatterers: one class appears in red and is characterized by only one dominant scattering mechanism (H), and a second class appears in green and is the result of two scattering mechanisms (with a high anisotropy). The entropy of two classes are 0.63±0.2 and 0.39±0.15, respectively.

Extract the forested areas that correspond, in theory, to a dipole scattering with medium-to-high entropy (zones 5 and 2). Do you observe only forested areas in these zones? Are there forested areas outside these zones?

Note The following script can be loaded from: ~\SAR_Training\EASI_script\Module3_ForestedAreas.eas. Also, the Landsat-5 mosaic can be used to facilitate the interpretation, ~\SAR_Training\Landsat\Flevoland_L5_20100906_p198_r23r24.pix.

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Figure 29. EASI Modeling script to extract Cloude-Pottier zones 2 and 5

Figure 30. Green: Zone 5, Red: Zone 2

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Green: Zone 5, Red: Zone 2 (detail) Landsat-5, R: tm4, G:tm3, B:tm2

(2010-09-06)

Figure 31. Cloude-Pottier zones 2 and 5

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Used PSEABA to perform the Cloude-Pottier decomposition

Used EASI modeling to analyze of the Cloude-Pottier parameters


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LLeessssoonn 33..33:: TToouuzzii ddeeccoommppoossiittiioonn In this lesson, you will:

Use the PSTOUZIDEC algorithm to perform the Touzi decomposition

Compare the Touzi decomposition to the Cloude-Pottier decomposition

Compare the Touzi 1s phase to the vvhh phase difference

PPeerrffoorrmmiinngg tthhee TToouuzzii ddeeccoommppoossiittiioonn To perform the Touzi decomposition, you use the PSTOUZIDEC algorithm.

To open the PSTOUZIDEC algorithm 1. In Focus, on the Tools menu, click Algorithm Librarian.


3. In the Find what box, type PSTOUZIDEC, and then click Find Next.

4. Double-click the PSTOUZIDEC algorithm. The PSTOUZIDEC Module Control Panel window appears.

To set up the PSTOUZIDEC algorithm 1. Under Input Ports, click Browse, and then open the




4. Under Output Ports, click Browse, and then enter FQ29_20100507sig_PSBOXCAR_7_PSTOUZIDEC.pix as the name of the output file.

5. Click the Input Params 1 tab, and then in the Angle Units list, click Degrees.

6. Click Run.

Exercise 4: Examine the Touzi decomposition parameters. Unlike Cloude-Pottier decomposition, the Touzi decomposition does not proceed to the weighted mean of each parameter ( si , ,1s i , i ) by their respective eigenvalues ( i , i=1,2,3).

Why do some of the parameters of the second component (i=2) and most of the parameters of the third component (i=3) look noisy?

The Landsat-5 mosaic can be used to facilitate the interpretation, ~\SAR_Training\Landsat\Flevoland_L5_20100906_p198_r23r24.pix.

Note Some of the parameters are best viewed in pseudo colors, especially the angle parameters ( si , i and i ) and the

phase . 1s


To view a parameter in pseudo colors 1. In Focus, click the Files tab.

2. Expand the Raster list of the file containing the parameter or parameters to be viewed in pseudo colors.

3. Right-click the parameter, point to View, and then click As Pseudocolor.

4. Click the Maps tab.

5. Right-click the parameter, and then click PCT Editing.

1s -90 90 PCT Editing – Stepped

i -45 45 PCT Editing – Stepped

1s 0 90 PCT Editing – Smooth

1s 2s 3s

1s 1s (in pseudo-colors) 2s

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1 1 (pseudo-colors) 2

1 1 (in pseudo-colors) 2

1 (min:0.37, max:0.99)

2 (min:0.23, max:0.48)

3 (min:0.10, max:0.29)

Figure 32. Touzi decomposition parameters (selection)

Note The parameter values are important, but their spatial distribution (the texture) is important too.

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Note In some analysis, the sign of the orientation ( i ) or

helicity ( i ) doesn’t matter, calculating their absolute

values ( 1 , 1 ) might facilitate their interpretation.

Exercise 5: Compare the Touzi 1s to the average Cloude-Pottier .

Find areas in the Flevoland data set, where Touzi 1s differs from Cloude-Pottier . Can you explain the observed differences?

Calculate the global Touzi alpha angle ( s ). Do you still observe differences with the Cloude-Pottier ? If yes, why?

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Touzi 1s Touzi sg Cloude

Touzi 1s , detail Touzi sg, detail Cloude , detail

Figure 33. Comparison between Touzi 1s , sg and Cloude

Exercise 6: Find areas where 1 is low or high. What kinds of land use and land cover are characterized by high-and-low helicity values? Use EASI Modeling to create bitmaps containing high helicity with different thresholds, such as 5°, 10° and 25°.

Tip The following EASI script can be loaded from: ~\SAR_Training\EASI_script\Module3_Helicity_thresholding.EAS

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Exercise 7: Compare the Touzi dominant orientation angle 1 with the Cloude-

Pottier average beta angle ;

Note In theory, 1 and both measure the orientation of a target from the radar line of sight (LOS) (angle in

Figure 24). While 1 is distributed between -45° and 45°,

is distributed between 0 and 90°; 12 .

Both are also sensitive to bare-soil roughness. Bare soil with a high roughness tends to produce higher 1 (or ) values, while smooth surfaces result in 1 (or

) values centered around 0°.

Exercise 8: Locate the urban or industrial areas on the Flevoland image, and observe the main street orientation from the radar line of sight (LOS). What are the average 1 and values when the streets are parallel to the LOS? What are the

average 1 and values when the main streets orientation departs from the LOS?

The following vector file can help to locate the urban area and interpret the relation between 1 , and the LOS: ~\SAR_Training\Vectors\Flevoland_Streets.pix.

Exercise 9: Find some bare fields and observe the 1 and values.

TOUZI PHASE The Cloude-Pottier decomposition does not include a phase parameter, as does the Touzi decomposition with . This phase is similar, but not equivalent, to the

1s

1s

vvhh phase difference. To better understand the 1s phase, the vvhh phase difference will be generated with the PSPHDIFF algorithm.

Generating the vvhh phase difference

You generate the vvhh phase difference using the PSPHDIFF algorithm.

To open the PSPHDIFF algorithm 1. In Focus, on the Tools menu, click Algorithm Librarian.


3. In the Find what box, type PSPHDIFF, and then click Find Next.

4. Double-click the PSPHDIFF algorithm. The PSPHDIFF Module Control Panel window appears.

To set up the PSPHDIFF algorithm 1. Under Input Ports, click Browse, open the

~\SAR_Training\Radar\Flevoland_RS2_SLC\FQ29_20100507 folder, and then select the FQ29_20100507sig_PSBOXCAR_7.pix file.



3. Under Output Ports, expand the Output: Output phase difference raster entry, select Untitled.pix, and then click Browse. The File Selector window appears.


5. Enter FQ29_20100507sig_PSBOXCAR_7_PSPHDIFF_HHVV.pix as the name of the output file.

6. Click the Input Params 1 tab, and then in the First input polarization list, select HH and in the Second input polarization list, select VV.

7. In the Angle Units list, select Degrees.

8. Click Run.

Exercise 10: Compare the Touzi 1s and the vvhh phase difference.

Before the widespread use of polarimetric decompositions, the vvhh

hh

phase difference was sometimes used as a (crude) way to identify the main scattering mechanisms (see Ulaby et al., 1987). Single-bounce scattering occurs around 0° ([-15, 15], while double-bounce scattering occurs at both ends of the vv distribution ([-180,100] U ([-100,180]).

The Touzi phase 1s is similar, but not equivalent to vvhh . The main differences are:

Different dynamic range

A phase representation in the dihedral-trihedral basis instead of the HH-VV basis

An independence of 1s from a polarization basis

For example, if the signal hit a tilted surface or an object with a complex geometry, the phase of the maximum polarized (backscattered) component will no longer be in the HH-VV basis. The vvhh will still provide information about the target structure, but this information might be biased and not be representative of the target’s real structure. Since the departure of the backscattered signal from the H-V basis (satellite transmitting antenna) is registered in 1 (or ) angle, it is possible to use this information to retrieve an unbiased phase to characterize the target’s structure.

Exercise 11: Explore the differences between 1s and vvhh using the

absolute value of 1 as a guide. The anisotropy (A) can also be used.

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Touzi 1s vvhh vvhh , Blue: single bounce

scattering Red: Double-bounce

scattering

Touzi , detail 1s vvhh , detail vvhh , Blue: single bounce

scattering Red: Double-bounce

scattering

Figure 34. Comparison between Touzi 1s phase and vvhh phase difference

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Used PSTOUZIDEC to perform the Touzi decomposition

Compared the Touzi decomposition to the Cloude-Pottier decomposition

Compared the Touzi 1s phase to the vvhh phase difference

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MMoodduullee 44:: UUssiinngg tthhee SSAARR PPoollaarriimmeettrryy TTaarrggeett AAnnaallyyssiiss ttooooll


Lesson 4.1: Introduction to SPTA and the target-selection mode

Lesson 4.2:Coherent target analysis in SPTA

Lesson 4.3: Incoherent target analysis in SPTA and FOCUS

LLeessssoonn 44..11:: IInnttrroodduuccttiioonn ttoo SSPPTTAA aanndd tthhee ttaarrggeett--sseelleeccttiioonn mmooddee In this lesson, you will:

Understand the different target-selection modes.

Draw a target in SPTA

Understand the target manager

Lear how to export and import a target using SPTA.

With Geomatica SPTA—the SAR polarimetry target analysis tool—you can select targets in a polarimetric SAR scene, draw a target (or load an existing target), perform a variety of polarimetric operations on it, and display the results numerically and graphically.

You can use SPTA to analyze all types of SAR data products. However, to use the full functionality of SPTA, a fully polarimetric SAR data set in complex format is required.

To start the SPTA tool On the Geomatica toolbar, click the SPTA button.

The SAR Polarimetry Target Analysis and Target Selection windows open.

SAR processing with Geomatica Module 4: Using the SAR Polarimetry Target Analysis tool

To open an image in SPTA 1. On the File menu, click Open.

The File Selector window appears.


3. Select the FQ29_20100507sig.pix file, and then click Open. A pop-up window appears, prompting you to specify whether to display the image North Up or Raster Up.

4. Select North Up, and then click OK. The FQ29_20100507sig.pix file is displayed in the Target Selection window with channels 1, 2, and 4 mapped to RGB, respectively.

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Module 4: Using the SAR Polarimetry Target Analysis tool SAR processing with Geomatica

The Target Selection toolbar menu contains buttons to:

Change the channels displayed in the RGB channels.

Select a different enhancement ( ) or to adjust the image contrast

( ) or brightness ( ). Selecting a different enhancement or modifying the image brightness and contrast will not change the computed statistics for a particular target.

Pan ( ) and zoom ( ) the displayed image.

Change the image interpretation ( ), that is to display the channels in intensity, amplitude, Decibels, to display their phase or only their Real or imaginary components.

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UUnnddeerrssttaannddiinngg ttaarrggeett--sseelleeccttiioonn mmooddeess There are three target-selection modes available in the SAR Polarimetry Target Analysis window:

Pixel plus clutter estimation region: you can choose a single target pixel at the cursor position and four clutter-estimation regions at the corners of an overall square region that it is centered on, but excludes, the target pixel and the sidelobe pixels. The Overall size and the Gap size option can be modified to estimate the properties of the coherent target against a larger clutter. Square neighbor: you can choose a target that includes all pixels within a square region that is centered on the cursor position.

Arbitrary region: you can choose either a rectangular or a polygonal target that includes all pixels within the target

The selection mode determines the target type and, therefore, the polarimetric parameters that can be computed. For example, Pixel plus clutter estimation region determines a point target (you are analyzing the response of the center pixel in relation to the clutter pixels). Square neighborhood and Arbitrary region delineate surface targets.

Depending on the type of target, several polarimetric parameters are available. For example, the symmetric scattering parameters are only applicable for point targets, whereas parameters that are applicable to surface targets or require an averaging of the data (such as entropy) are only enabled when surface targets (Square neighborhood or Arbitrary region) are enabled.

The polarimetric parameters that can be computed also depend on the data set. Polarimetric parameters that require multilook will be disabled for a single-look complex image and, similarly, the ones that require a fully polarimetric data set (such as polarimetric decompositions) are disabled for single or dual polarimetric data.

To draw a target in SPTA 1. In the Target Selection window, pan or zoom to the area of interest, as

applicable.

2. In the SAR Polarimetry Target Analysis window, select a target-selection mode.

3. With the mouse, drag to draw your target over the desired area. If the Arbitrary region – polygon option is selected, double-click to close the shape. The new target is added to the list in the Target Manager window.


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A) Point target location Coordinates: 1380P , 2610L

B) Overall size: 11 ( ) Gap size:3 ( )

C) Overall size: 11

Gap size: 1 D) Overall size: 11

Gap size:3 E) Overall size: 11

Gap size: 5

F) Overall size: 17

Gap size: 1 G) Overall size: 17

Gap size: 3 H) Overall size: 17

Gap size: 5

Figure 35. Target selection mode: pixel plus clutter estimation region

UUnnddeerrssttaannddiinngg tthhee TTaarrggeett MMaannaaggeerr SPTA keeps a record of the target modes you have applied during a session. Using the Target Manager, you can save one or more target records to a file. You can also import a saved target and apply it to your imagery.

Each time a new target is drawn, it will be temporally stored in the Target Manager window. A short description is given (target 1, target 2...) along with its selection mode, the number of samples it contains, and its image coordinates (Figure 36).

Note You can edit the default description by click a target description, and then typing a new name.


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Figure 36. Target Manager window

Note Targets are not saved automatically; on exiting SPTA, any unsaved targets will be lost.

To export (save) a target 1. In the Target Manager window, select a target.

2. Click Export. The Save Target As window appears.

3. Specify a target file name and location.

4. Click Save. The file is saved to disk as a vector file.

Note The exported target (vector) will be in the same geographic projection (if any) as the image on which it was drawn. It can be opened for different images in SPTA and Focus.

IImmppoorrttiinngg aanndd uussiinngg aa ttaarrggeett You can use the Target Manager to import targets that have been defined on a different image and set it as the current target on a new image. The imported target will not be selectable if it falls outside the bounds of the imagery displayed in SPTA. To be recognized by SPTA, point targets must have the target-type, total-size, and gap-size attributes for each point.

1. In the SAR Polarimetry Target Analysis window, on the File menu, click Target Manager.

2. In the Target Manager window, click Import.

3. In the Import Targets window, open the folder containing the target file you want to import.

4. Select the file, and then click Open.

5. In the Target Manager window, click the target you want to apply, and then click Set as Current. The target displays on the imagery in the Target Selection window.


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LLeessssoonn 44..22:: CCoohheerreenntt ttaarrggeett aannaallyyssiiss iinn SSPPTTAA In this lesson, you will:

Select coherent targets in SPTA

Analyze the scattering characteristics of a coherent target

Automatically detect coherent targets

The incoherent target analyses are restricted to single-look matrix type. Here, you will use the s4c format (single-look, non-symmetrized scattering matrix).

To select a coherent target 1. In the Target Selection window, use the Pan and Zoom toolbar buttons to

locate a point target.

2. In the SAR Polarimetry Target Analysis window, under Target Selection Mode, click Pixel plus clutter estimation region. The default values will suffice for this lesson.

3. In the SAR Polarimetry Target Analysis window, do the following:

Under Coherent Target Decomposition, select the Huynen and Cameron parameters check box

Under Symmetric scattering parameters, select the Symmetric scattering characterization check box

4. In raster coordinates, select the target located at 411.5P, 4236.6L, and then click Compute. A series of statistics characterizing the scattering properties of the target appears in the Results box.

Note The statistics are calculated only if the selected target is coherent or symmetric according to the values specified under Coherent Target Decomposition and Symmetric scattering parameters. To include more targets in the coherent target analysis, the default values cans be lowered; however, this may produce less meaningful statistics.


Figure 37. Target Selection Mode, Coherent Target Decomposition, and Symmetric scattering parameters

A second target, located at 1585.5P, 4437.5L can be selected. If the default Overall size (=3) and the Gap size (=1) are used, the target does not appear to be coherent or symmetric. Set the Overall size to 9 and the Gap size to 3, select the target again, and then click Compute.

The target is now defined against a larger clutter corresponding to a flat ground surrounding what appears to be a pylon. The scattering characteristics can now be estimated.

Exercise 1: Compare the statistics of the two selected targets (see Figure 38)

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Target 1 Target 2

========================= C:\PCIGeomatics\Geomatica2014\TrainingData\SAR\Radar\Flevoland_RS2_SLC\FQ29_20100507sig.pix ========================= Matrix Representation is s4c ==Phase Difference HH and VV ==== 0.000000 deg == Huynen and Cameron parameters=== Target is coherent Eigenvalues: -7.640306 + 7.640072 i, -7.640305 + 7.640073 i, Eigenvectors: 0.000000 + 0.000000 i, 1.000000 + 0.000000 i, -1.000000 + 0.000000 i, 0.000000 - 0.000000 i, Maximum returned power density: 163.651016 (linear) Characteristic angle: 45.000000 deg Absolute phase of the target: -135.000870 deg Target skip angle: 0.000001872167 deg

========================= C:\PCIGeomatics\Geomatica2014\TrainingData\SAR\Radar\Flevoland_RS2_SLC\FQ29_20100507sig.pix ========================= Matrix Representation is s4c ==Phase Difference HH and VV ==== 170.485611 deg == Huynen and Cameron parameters=== Target is coherent Eigenvalues: 0.058370 + 1.219124 i, 0.060395 - 0.086951 i, Eigenvectors: 0.623113 - 0.034474 i, 0.780887 - 0.027509 i, -0.780887 - 0.027509 i, 0.623113 + 0.034474 i, Maximum returned power density: 4.416056 (linear) Characteristic angle: 16.410576 deg Absolute phase of the target: -16.021254 deg Target skip angle: 35.618809 deg

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Target 1 Target 2 Maximum polarization orientation relative to the horizontal: -90.000000 Maximum polarization ellipticity: 0.000002504478 Rotation angle for maximum symmetrical scattering component: 45.000000 deg Degree of symmetry: 1.0000000 Real Component of Co-polarized ratio: 1.0000000 Imaginary Component of Co-polarized ratio: 0.000000 Nearest elemental symmetric scattering mechanism: Trihedral Distance to nearest elemental symmetric scattering mechanism: 0.000000 ==Symmetric Scattering Characterization = Degree of Coherence: 0.9872652 Scattering Vector Direction: 0.000000 deg Phase Difference: 0.000000 deg Target Sphere Angle (PSI): 0.000000 deg Target Sphere Angle (CHI): 0.000000 deg Rotation Angle: 45.000000 deg ===========================

Maximum polarization orientation relative to the horizontal: -51.411686 Maximum polarization ellipticity: -2.527805 Rotation angle for maximum symmetrical scattering component: -6.243052 deg Degree of symmetry: 0.9990352 Real Component of Co-polarized ratio: -0.4406728 Imaginary Component of Co-polarized ratio: 0.07385702 Nearest elemental symmetric scattering mechanism: Narrow diplane Distance to nearest elemental symmetric scattering mechanism: 0.07743849 ==Symmetric Scattering Characterization = Degree of Coherence: 0.6910781 Scattering Vector Direction: 68.639687 deg Phase Difference: 169.543076 deg Target Sphere Angle (PSI): 68.878654 deg Target Sphere Angle (CHI): -3.536424 deg Rotation Angle: -6.243052 deg ===========================

Figure 38. SPTA Target selection window and extracted statistics

Note Coherent targets can be difficult to find by clicking randomly on an image. It is possible to use the PSWHITE or PSSCM algorithm from the SAR Radar Analysis toolbox to help find the coherent targets of an image. PSWHITE produces a bitmap representing the coherent point targets. PSSSCM produces a bitmap representing the coherent point targets, and an image file containing six channels characterizing the scattering properties of the coherent targets, where the first channel represents the degree of coherence.

The bitmaps produced by the PSWHITE or PSSSCM algorithm can be viewed in Focus.

Exercise 2: Using the PSSSCM algorithm, produce a bitmap representing the coherent point target.

To open the PSSSCM algorithm 1. In Focus, on the Tools menu, click Algorithm Librarian.



3. In the Find what box, type PSSSCM, and then click Find Next.

4. Double-click the PSSSCM algorithm.

To set up the parameters for PSSSCM 1. Under Input Ports, click Browse, and then select the

FQ29_20100507sig.pix file.

2. Under Input Ports, expand the Input: Input polarimetric SAR image, and then select each intensity raster file.

3. Leave the Input Targets: Input coherent target bitmap unchanged.

4. Under Output Ports, expand the OutputRaster: Output scattering characteristics raster, select Untitled.pix, click Browse, and then select the location in which to save the file.

5. In the File Name box, type FQ29_20100507sig_PSSSCM.pix, and then click Save.

6. Under Output Ports, expand the OutputBitmap: Output coherent targets bitmap, select Untitled.pix, click Browse, and then select the location in which to save the file.

7. In the File Name box, type FQ29_20100507sig_PSSSCM_CTmap.pix, and then click Save.

8. Click the Input Params 1 tab, and then change the default value of the parameters according to the values provided in Figure 39.

9. Click Run.

The resulting bitmap can be opened in Focus over the FQ29_20100507sig.pix file. Figure 40 and Figure 41, following, show the detected coherent point target using a different configuration of window sizes.

In comparison, the PSWHITE algorithm is less strict in its definition of a coherent point target. This algorithm relies mainly on a threshold-based detection to discriminate bright-point targets.

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Figure 39. PSSSCM Control Panel window

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Configuration Target 1 Target 2 No. 1 Parameters WS=9 CWOW=15 CWCW=3 DST=0.8 DCH=0.8 SCRT=12

No. 2 Parameters WS=15 CWOW=25 CWCW=15 DST=0.8 DCH=0.8 SCRT=12

Figure 40. Coherent targets detection using PSSSCM (HH channel in background)

Parameters Target 1 Target 2 WS=9 DT=12

Figure 41. Coherent targets detection using PSWHITE (HH channel in background)

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Selected coherent targets in SPTA

Analyzed the scattering characteristics of a coherent target

Automatically detected coherent targets


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LLeessssoonn 44..33:: IInnccoohheerreenntt ttaarrggeett aannaallyyssiiss iinn SSPPTTAA In this lesson, you will:

Define and select targets

Produce numerical and graphical outputs for selected targets

Analyze numerical and graphical outputs

Exercise 3: You are now ready to select some regions. From the results obtained in Module 3 and Lesson 4.1 and Lesson 4.2, the major land-use and land-cover classes present in the Flevoland region are readily apparent. For this analysis, you will select regions corresponding to the following land-use and land-cover classes:

Open water

Urban area 1 [double-bounce dominated, aligned with the radar line of sight (LOS)]

Urban area 2 (With volume, multiple scattering, or both)

Forested areas

Wetland (meadow, reeds)

Agriculture (with strong HV, vegetated, rough or both)

Agriculture (strong HH, double-bounce).

Agriculture (smooth bare field).

To select regions 1. In the Target Selection window, use your mouse to digitize a polygon over

an open water area in the image.

2. In the Target Manager window, in the Description box, type a_OpenWater.

3. To save the region as a vector file, click Export.


4. Browse to the folder in which you want to save the file, and then enter a_OpenWater.pix as the name of the output file. The region will be saved as a vector file.

5. Repeat steps 1 to 4 for the remaining land-use and land-cover classes.

Tip To get a meaningful analysis of incoherent targets, select a homogenous region. Also, avoid selecting a region with too many or too few samples. All the target polygons are provided in ~\SAR_Training\Radar\Flevoland_RS2_SLC\FQ29_20100507\targets

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Figure 42. Location of land-use classes

PPrroodduucciinngg nnuummeerriiccaall oouuttppuutt Now that you have defined a set of targets, you can analyze their diffusion properties with the numerous numerical output tools available in SPTA by checking the corresponding parameters of interest. Note that the available options vary depending on the selected target type (box A).

For an incoherent target represented by an arbitrary region, like the ones you have defined in the previous section, the following options are available:

Matrix Representations (box B)

Parameters and Discriminators (box C)

Channel Comparisons (box D)

After selecting a target and checking the output, click Compute (box F) to get the numerical results.

To save the numerical-output results to disk 1. In SPTA, on the File menu, click Save Numerical Output.

2. In the File Name box, enter a name for the file and specify a location where you want to save the file, and then click Save.

The results can be saved to disk by copying them from the Results box to a document of your choice (*. txt, *. doc, *. docx).


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Note All the options in boxes B, C, and D have their equivalent in the Radar Analysis toolbox (see Module 3), which can be applied to the whole image instead of a selected region.

Exercise 4: Select open water and forested areas and compare their scattering characteristics by selecting some of the available numerical-output options. You will use the targets defined in the previous lesson.

Figure 43. SAR Polarimetry Target Analysis window, Numerical Output options

PPrroodduucciinngg ggrraapphhiiccaall oouuttppuutt SPTA offers three main options to visualize your data. Each can be configured (customized) in several ways:

Histogram: examine the distribution of one element (or channel)


Scatter plot: examine the relationship between two (2-D) or three (3-D) elements

Response plot: examine the polarimetric properties of one target

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Figure 44. Histogram, Scatter Plot, and Response Plot

Exercise 5: With the urban and the forest target, produce and compare:

1. the co-polarized response plot using the normalized scaling

2. the scatter plot using the HH and VV channels

3. The co-polarized response plot with the ones in Figure 24 for canonical targets.

To produce a response plot 1. In the Target Manager window, select the urban target

(b_UrbanArea_DB_LOS.pix), and then click Set as Current.

2. In the SAR Polarimetry Target Analysis window, under Graphical Output, click the Response Plot tab.

3. In the Type list, click Co-Polarized, and in the Scaling list, click Normalized.

4. Click Plot.

To produce a scatter plot 1. In the Target Manager window, select the urban target

(b_UrbanArea_DB_LOS.pix), and then click Set as Current.

2. In the SAR Polarimetry Target Analysis window, under Graphical Output, click the Scatter Plot tab.

3. In the Parameter 1 list, click Intensity, and in the Element 1 list, click HH (or Element 1,1 of covariance matrix).

4. In the Parameter 2 list, click Intensity, and in the Element 2 list, click VV (or Element 4, 4 of covariance matrix).

5. Leave the other options blank.

6. Click Plot.

Repeat the previous steps for the forest target (d_Forest.pix).

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b_UrbanArea_DB_LOS response plot

d_Forest response plot

b_UrbanArea_DB_LOS

HH-VV scatter plot d_Forest HH-VV

HH-VV scatter plot

VV LL HH

RR VV VV

Figure 45. Examples of polarimetric response and scatter plot for an urban and a forest target

Note You can customize the graphical output using the standard Gnuplot command lines (http://www.gnuplot.info/). To customize a graphic, switch to Interactive mode. In the SAR Polarimetry Target Analysis window, click the Preferences menu, point to Plot Options, and then click Interactive.

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http://www.gnuplot.info/


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Defined and selected targets

Produced numerical and graphical output for the selected targets

Analyzed the numerical and graphical output

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MMoodduullee 55:: CCllaassssiiffyyiinngg SSAARR ddaattaa


Lesson 5.1: Classification of polarimetric data, possible workflows

Lesson 5.2: Unsupervised Wishart classifications

Lesson 5.3: Supervised Wishart classification (with targets defined in SPTA)

LLeessssoonn 55..11:: CCllaassssiiffiiccaattiioonn ooff ppoollaarriimmeettrriicc ddaattaa,, ppoossssiibbllee wwoorrkkfflloowwss Applying polarimetric decompositions to fully polarimetric data (Module 4) is not an end in itself, but usually part of a workflow (an application). One obvious use of polarimetric decomposition parameters is for land-cover and land-use classification.

In this regard, Geomatica provides several supervised and unsupervised classification algorithms designed primarily for optical imagery. While some polarimetric parameters can be used directly in the classification algorithms (for example entropy, eigenvalues), others may require further processing (scaling, thresholding) before being used.

Another option is to use a parameter or a set of parameters to create bitmaps to restrain (or exclude) a classification to specific areas using different parameters.

Polarimetric decomposition tends to focus more on scattering-type characterization and identification while the information in the backscattered power is sometimes neglected or not fully used. With a fully polarimetric image (s4c), it is also possible to generate many polarimetric discriminators based on an extensive analysis of the scattered power (using the PSPOLSYN, PSPOLSYNC, and PSPOLSYNR algorithms). This topic is covered in Appendix B.

All generated channels and bitmaps can be combined in a single PCIDSK file and then classified with a conventional classification algorithm (MINDIS, MCL, ISOCLUS).

Module 5: Classifying SAR data SAR processing with Geomatica

Figure 46. High-level workflow for polarimetric data classification using supervised or unsupervised classifications

The Geomatica radar suite includes algorithms that are designed specifically for classifying polarimetric SAR-data sets. The PSSWIS and PSUWIS (supervised and unsupervised Wishart classification) algorithms use customized classes distances measures for polarimetric data. The training classes are derived from an existing polarimetric decomposition result or based on polygons and bitmaps provided by the user representing different land-use or land-cover classes. This topic will be covered in Lesson 5.2 and Lesson 5.3.

Figure 47. High-level workflow for polarimetric data classification using supervised or unsupervised Wishart classifications

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SAR processing with Geomatica Module 5: Classifying SAR data

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LLeessssoonn 55..22:: UUnnssuuppeerrvviisseedd WWiisshhaarrtt ccllaassssiiffiiccaattiioonnss In this lesson, you will:

Run a Wishart unsupervised classification

Analyze the results of the unsupervised Wishart classification

UUnnssuuppeerrvviisseedd WWiisshhaarrtt ccllaassssiiffiiccaattiioonn The Wishart unsupervised classification is performed on polarimetric SAR (POLSAR) data using the PSUSWIS algorithm. The classification is similar to the method used in the supervised maximum-likelihood classification for optical imagery, except that the distance measured is customized for polarimetric data.

With the Wishart unsupervised classification, the training classes are derived from an existing polarimetric decomposition: either a Freeman-Durden or Cloude-Pottier decomposition. The output image is a single-channel image with pixel values representing the class number, which can be between 1 and 9 for a Freeman-Durden input, and between 1 and 16 for a Cloude-Pottier input. The use of iteration refines the classification, but increases the processing time.

To complete this exercise you will use

Output generated by the PSBOXCAR algorithm in Lesson 2.2

Freeman-Durden decomposition output created in Lesson 3.1

Cloude-Pottier image classification produced by the PSCLOPOT algorithm

To open the PSUSWIS algorithm 1. In Focus, on the Tools menu, click Algorithm Librarian.


3. In the Find what box, type PSUSWIS, and then click Find Next.

4. Double-click the PSUSWIS algorithm. The PSUSWIS Module Control Panel window appears.

PPeerrffoorrmmiinngg aann uunnssuuppeerrvviisseedd WWiisshhaarrtt ccllaassssiiffiiccaattiioonn uussiinngg tthhee FFrreeeemmaann--DDuurrddeenn ddeeccoommppoossiittiioonn Here, you will use the Wishart classification with the Freeman-Durden decomposition.

To set up the PSUSWIS parameters 1. Under Input Ports, expand the Input: Input polarimetric SAR image

entry, click Browse, and then open the ~\SAR_Training\Radar\Flevoland_RS2_SLC\FQ29_20100507 folder.

2. Select the FQ29_20100507sig_PSBOXCAR_7.pix file, and then select each raster in the file.


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3. Expand the Input: Input Freeman-Durden or Cloude-Pottier classified raster entry.

4. Click Browse, and then open the ~\SAR_Training\Radar\Flevoland_RS2_SLC\FQ29_20100507 folder.

5. Select the FQ29_20100507sig_PSBOXCAR_7_PSFREDUR.pix file, and then select each raster in the file.

6. Under Output Ports, expand the Output: Output Wishart classified raster entry, select Untitled.pix, and then click Browse. The File Selector window appears.

7. In the File Name box, type FQ29_20100507sig_PSBOXCAR_7_PSFREDUR_WIS.pix, and then click Save. The new path and file name appear in the PSUSWIS Module Control Panel window.


9. In the Number of iterations list, click 10.

10. Click Run

PPeerrffoorrmmiinngg aann uunnssuuppeerrvviisseedd WWiisshhaarrtt ccllaassssiiffiiccaattiioonn uussiinngg tthhee CClloouuddee--PPoottttiieerr ddeeccoommppoossiittiioonn Before running the Wishart classification using the Cloude-Pottier decomposition, the PSCLOPOT algorithm must be run.

PSCLOPOT performs an unsupervised Cloude-Pottier classification of a fully polarimetric SAR (POLSAR) data set. The classification is based on the partitioning of the entropy, alpha angle, and anisotropy feature space using a series of thresholds that can you can set. The output image has one channel, where the pixel values indicate the class number assigned to each pixel.

To set the PSCLOPOT parameters 1. Under Input Ports, expand the Input: Input polarimetric SAR image

entry.



4. Under Output Ports, expand the Output: Output Cloude-Pottier classified raster entry, select Untitled.pix, and then click Browse. The File Selector window appears.

5. In the File Name box, type FQ29_20100507sig_PSBOXCAR_7_PSCLOPOT.pix, and then click Save. The new path and file name appear in the PSCLOPOT Module Control Panel window.


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6. Click Run.

To set up the PSUSWIS parameters 1. Under Input Ports, expand the Input: Input polarimetric SAR image

entry.

2. Click Browse, and open the ~\SAR_Training\Radar\Flevoland_RS2_SLC\FQ29_20100507 folder.


4. Expand the Input: Input Freeman-Durden or Cloude-Pottier classified raster entry.


6. Select the FQ29_20100507sig_PSBOXCAR_7_PSEABA.pix file, and then select each raster in the file.


8. In the File Name box, type FQ29_20100507sig_PSBOXCAR_7_PSEABA_WIS.pix, and then click Save. The new path and file name appear in the PSUSWIS Module Control Panel window.


10. In the Number of iterations list, click 10.

11. Click Run.

EEvvaalluuaattiinngg aanndd ccoommppaarriinngg tthhee uunnssuuppeerrvviisseedd WWiisshhaarrtt ccllaassssiiffiiccaattiioonnss Exercise 1: Using the Landsat-5 image (Flevoland_L5_20100906_p198_r23r24.pix) and the filtered FQ29 Flevoland image (FQ29_20100507sig_PSBOXCAR_7.pix), evaluate and compare the Freeman-Durden and the Cloude-Pottier unsupervised classification. What are the similarities? What are the main differences? Pay special attention to the urban areas.


Figure 48. Unsupervised Wishart classifications using the Freeman-Durden decomposition (7x7)

Figure 49. Unsupervised Wishart classifications using the Cloude-Pottier decomposition (7x7)

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Ran a Wishart unsupervised classification

Analyzed the results of the unsupervised Wishart classification


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LLeessssoonn 55..33:: SSuuppeerrvviisseedd WWiisshhaarrtt ccllaassssiiffiiccaattiioonn ((wwiitthh ttaarrggeettss ddeeffiinneedd iinn SSPPTTAA)) SPTA provides several tools to enable an in-depth analysis of the scattering properties of point (Lesson 4.1) and distributed targets (Lesson 4.2), based on regions you define. It is then possible to interactively use SPTA and the Focus SAR Radar Analysis toolbox to perform a complete polarimetric analysis.

In this lesson, you will use targets defined in SPTA to train the supervised Wishart classification of the FQ29_20100507sig_PSBOXCAR_7.pix file in Focus.

In this lesson, you will:

Set up preclassification tasks

Edit training sites

Run a Wishart supervised classification

Analyze the results of the Wishart classification

Compare the results of the supervised and unsupervised Wishart classifications

SSuuppeerrvviisseedd WWiisshhaarrtt ccllaassssiiffiiccaattiioonn The Wishart supervised classification is performed on polarimetric SAR (POLSAR) data sets using the PSSWIS algorithm. The classification is similar to the method used in the supervised maximum-likelihood classification for optical imagery, except that the distance measured is customized for polarimetric data. In the Wishart supervised classification, you provide the training classes.

You specify a file containing the classification results in a single 8-bit channel. Its pixel values specify the class number that is assigned to each pixel. Pixels that cannot be classified (usually with zero values in all input channels) are represented by a pixel value of 0. The classification file can hold up to 255 classes.

PPrree--ccllaassssiiffiiccaattiioonn ttaasskkss The supervised Wishart classifier (the PSSWIS algorithm) requires as input a series of bitmaps representing each class to be classified. These bitmaps can be imported or created in Geomatica Focus. Alternatively, a series of polygons (vectors) can be imported and then converted into bitmaps.

Creating the training sites from polygons (vector-to-bitmap transformation) In Lesson 4.2, a series of targets (polygons) representing several land-use and land-cover classes has already been created; these targets can be converted into bitmaps using the POLY2BIT algorithm, and can then be used as training sites.

Note The targets are provided in ~\SAR_Training\Radar\Flevoland_RS2_SLC\FQ29_20100507\targets.

To open the POLY2BIT algorithm 1. In Focus, on the Tools menu, click Algorithm Librarian.


2. In Algorithm Librarian window, click Find. The Find Algorithm window appears.

3. In the Find what box, type POLY2BIT, and then click Find Next.

4. Double-click the POLY2BIT algorithm. The POLY2BIT Module Control Panel window appears.

To set up the parameters for POLY2BIT 1. Under Input Ports, click Browse, locate and select all the targets

(polygons) created in Lesson 5.1, and then click Open.

2. Under Input Ports, expand the Input: Polygon Layer entry, and then select a_OpenWater.pix

3. Expand the InputArea: Bounding Area entry, and then select FQ29_20100507sig_PSBOXCAR_7.pix.

4. Under Output Ports, expand the Output: Filter Layer(s) entry, select Untitled.pix, click Browse, and then locate the folder in which you want to save the file.

5. In the File name box, type Flevoland_20100507_Training_Sites.pix, and then click Save.

6. Click Run (leave the Input Params 1 tab unchanged).

7. Repeat this procedure for the remaining targets (b_*.pix to h_*.pix). Do not change the InputArea: Bounding Area and the OutputBitmap: Bitmap Layer ports.

Note All bitmaps must be stored in the same *.pix file.

To inspect the results of POLY2BIT 1. In Focus, on the File menu, click Open, locate and select the training

sites file (Flevoland_20100507_Training_Sites.pix), and then click Open.

2. Click the Files tab, and then expand the Bitmap layers list.

3. In the list, right-click a bitmap, and then click View. To view the loaded bitmap, click the Maps tab.

For clarity, it is recommended that you rename the bitmap layers according to their content (see the following example). If necessary, you can edit a bitmap layer.

Figure 50. POLY2BIT Result

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Creating the training sites in Focus (bitmap format) You will now create the training sites in Focus.

1. In Focus, on the File menu, click Open, select the image to be classified, and then click Open.

2. Click the Maps tab, right-click New Area, and then click New Bitmap Layer. The New Bitmap window appears.

3. Select Use Layer Georeferencing, and then click OK to create the new bitmap layer.

4. On the Focus toolbar, click the New Shapes button, and then draw a shape over an urban area on the new bitmap layer using the Polygon, Rectangle, or Ellipse command.

5. Save the created bitmap as Flevoland_20100507_Training_Sites.pix.

6. To add a second bitmap, click the Files tab, right-click Flevoland_20100507_Training_Sites.pix, and then click New > Bitmap Layer.

7. Right-click the newly created bitmap, and then click View.

8. Click the Maps tab.

9. Draw a new shape representing the desired land-use and land-cover class using the Polygon, Rectangle, or Ellipse command. Tip: Be sure the new bitmap layer is selected before editing.

10. After editing, right-click the bitmap layer, and then click Save.

11. Repeat steps 7 to 11 for each new class.

12. Click the Files tab, and then select the Flevoland_20100507_Training_Sites.pix file.

13. Expand the Bitmap layer tree, double-click a bitmap layer, and then rename it to better describe its content.

Note All bitmaps must be stored in a single *.pix file.

PPeerrffoorrmmiinngg aa ssuuppeerrvviisseedd WWiisshhaarrtt ccllaassssiiffiiccaattiioonn You will now perform a supervised Wishart classification using the PSSWIS algorithm.

To open the PSSWIS algorithm 1. In Focus, on the Tools menu, click Algorithm Librarian.


3. In the Find what box, type PSSWIS, and then click Find Next.

4. Double-click the PSSWIS algorithm. The PSSWIS Module Control Panel window appears.


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To set up the PSSWIS parameters 1. Under Input Ports, expand the Input: Input polarimetric SAR image

entry.

2. Select each raster in the FQ29_20100507sig_PSBOXCAR_7.pix file.

3. Expand the InputBitmaps: Input sample data entry.

4. Select each bitmap in the Flevoland_20100507_Training_Sites.pix file.


6. In the File Name box, type FQ29_20100507sig_PSBOXCAR_7_PSSWIS.pix, and then click Save. The new path and file name appear in the PSWIS Module Control Panel window.

7. Click Run.

IInntteerrpprreettiinngg tthhee PPSSSSWWIISS ccllaassssiiffiiccaattiioonn rreessuullttss After running the PSSWIS algorithm, you can analyze the results.

To interpret the PSSWIS classification results 1. In Focus, open the FQ29_20100507sig_PSBOXCAR_7_PSEABA.pix

file created in Lesson 3.2.

2. If you do not have the FQ29_20100507sig_PSBOXCAR_7_PSSWIS.pix file created in the previous lesson open already, on the File menu, click Open, and open the file from its location.

3. To facilitate the interpretation of the supervised Wishart classification, you can edit the legend names and colors, as shown in the following figure.

Exercise 2: What are the main differences between these two classifications? A RGB composite made of the HH, HV and VV channels of the FQ29_20100507sig_PSBOXCAR_7.pix file can be use to help the interpretation.


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Unsupervised Wishart Supervised Wishart

(PSSWIS) HH-HV-VV

Unsupervised Wishart legend Supervised Wishart legend

Figure 51. Classifications comparison and legends

EEddiittiinngg tthhee ttrraaiinniinngg ssiitteess ((bbiittmmaapp ffoorrmmaatt)) If the classification result is not what you expect, it may be because the training sites are not representative enough of the classes to be classified. If necessary, you can edit the bitmap layer in Focus.

To edit a training site in Focus 1. On the File menu, click Open, and then open the

Flevoland_20100507_Training_Sites.pix file.

2. Click the Files tab, and expand the Bitmaps layer list.

3. Right-click the bitmap layer you want to edit, a_OpenWater layer, and then click View.

4. Click the Maps tab, and then select the bitmap you want to edit.


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5. Click the New Shapes button ( ), and then draw a new bitmap on the same layer using the Polygon ( ), Rectangle ( ), Ellipse ( ), oTrace (

r ) command.

6. Click the Raster Erase button ( ), and then use the Erase Trace ( ) or Erase Polygon ( ) commands to erase a bitmap or part of a bitmap of the selected layer.

7. Right-click the bitmap layer being edited, and then to save your changes, click Save.


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Set up preclassification tasks

Ran a Wishart supervised classification

Analyzed the results of the Wishart classification

Visually compared the results of the Wishart supervised and unsupervised classifications

Edited training sites

RReeffeerreenncceess Ulaby, F.T., Held, D., Dobson, M.C., McDonald, K.C., Senior, T.B.A (1987). Relating Polarization Phase Difference of SAR Signals to Scene Properties. IEEE Transactions on Geocience and Remote Sensing. Vol. GE-25, no.1, p.83-92.

Lee, Jong-Sen., Pottier, Eric (2009). Polarimetric Radar Imagining: from basics to applications. CRC Press, Taylor & Francis Group, Boca Raton, Florida, USA. 398 pages.

Lo, C.P. (1998). Applications of Imaging Radar to Land Use and Land Cover Mapping. Published in: Manual of remote sensing: principles and applications of imaging radar. R.A. Ryerson (ed). John Wiley & Sons, Inc., New York. 896 pages.

van Zyl, J.J., Ulaby, F.T (1990). Chapter 2: Scattering Matrix Representation for simple targets. Publié dans: Radar Polarimetry for Geoscience Applications. F.T. Ulaby. C. Elachy, editors. Artech House, Norwood, MA, USA. 388 pages.

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MMoodduullee 66:: CChhaannggee ddeetteeccttiioonn uussiinngg ppoollaarriimmeettrriicc ddaattaa

AAbboouutt tthhiiss mmoodduullee Module 6 has five lessons:

Lesson 6.1: Examples of possible workflows for change detection

Lesson 6.2: Intensity change detection

Lesson 6.3: Wishart change detection

Lesson 6.4: Phase change detection

Lesson 6.5: Incoherent change detection workflow

LLeessssoonn 66..11:: EExxaammpplleess ooff ppoossssiibbllee wwoorrkkfflloowwss ffoorr cchhaannggee ddeetteeccttiioonn In this lesson, you will:

Learn about the different options for change detection in Geomatica

Examine examples of possible workflows for coherent and incoherent change detection

TTyyppeess ooff cchhaannggeess aanndd tteecchhnniiqquueess ttoo ddeetteecctt tthheemm Detecting and monitoring changes between two images, or from a time series of images, is an important use of SAR data. There are many types of changes possible, such as:

1. Permanent (non cyclic) changes:

Short term or abrupt changes: disaster monitoring (flood, fire), ships and other vehicles movement detection

Medium, long term or gradual changes: mostly land-use, land-cover changes like deforestation and reforestation, urban sprawl, glacier receding, wetland and forest monitoring, and so forth

2. Seasonal (cyclic) changes, crop growth and rotation, leafs cycle in forests.

The success of a change-detection application depends on the adequacy between the nature of change to be detected and the sensor spatial, spectral, radiometric and temporal resolution. Obviously, the selected change-detection techniques also have a strong impact on the results.

SAR processing with Geomatica Module 6: Change detection using polarimetric data

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There are many techniques for detecting change with remote-sensing data. At one end of the spectrum there is hard-change detection based on comparison of land-use or land-cover classification and the resulting changes in discrete (qualitative) categories.

At the other end, it is possible to directly compare two images using a band or ratio technique, or a principal component analysis, for example. These techniques reveal the magnitude of change, but give little information on the nature of the changes.

Between these two types, a multitude of hybrid techniques exists, with varying levels of sophistication8.

The Geomatica radar suite has three coherent change-detection algorithms that can extract the magnitude of change between two images (Table 5).

Table 5. Change-detection algorithm included in the Geomatica radar suite

Change-detection algorithm Input type Description CCDINTEN • Detected or complex

• Filtered or not • Single, dual or fully-polarimetric (symmetrized or not)

CCDINTEN measures the change in total radar backscattering between the test and reference SAR images. If the image is single-look complex, the intensity is derived and output to the specified output file. CCDINTEN also supports integer or real valued speckle filtered data as test or reference file input. A number of speckle filters can be applied to the files before running CCDINTEN.

8 See Jensen (2005) and Hussain et al. (2013) for a review.

Module 6: Change detection using polarimetric data SAR processing with Geomatica

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Change-detection algorithm Input type Description CCDWISH • Complex

• SLC (not filtered) • Fully-polarimetric (symmetrized or not)

CCDWISH uses Wishart statistics to test the equality of two complex covariance matrices that are assumed to follow a Wishart distribution. When the backscatter coefficients are very close, but either the phase or the correlation coefficient between the co-polarization have changed, the Wishart test statistic is much more sensitive to the differences than test statistics based only on the intensity. In other words, CCDWISH can highlight changes not detected by CCDINTEN. The total number of channels to process must be less than or equal to four.

CCDPHASE • Complex • Not filtered • Fully-polarimetric (symmetrized or not)

CCDPHASE measures change based on the average coherence (over a specified window size) between two coregistered single-look complex SAR images. Coherence values will range between zero and one, where areas of change will have coherence values close to zero (that is, they are incoherent) while unchanged areas will have higher values (that is, will be coherent). The total number of channels to process must be less than or equal to four.

Each change-detection algorithm in the preceding table automatically extract the overlap area between each image pairs, all return four channels in the output:

Intensity value (or sum of intensities) of the input test data

Intensity value (or sum of intensities) of the reference data

Change metric (absolute intensity ratio, Wishart metric, or the absolute intensity ratio of the coherence)

Change, as percentiles, ranked from zero (no change) to 100 percent (maximum change)


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Image quality in the context of change detection using SAR data refers to the comparability of the source data. As far as possible, source images should have the same calibration applied, the same georeferencing, pixel size, viewing geometry, and polarization. Image quality also refers to the quality of the coregistration of each pair of images to be processed by the change-detection algorithm.

Note It is recommended that you check the pixel alignment (or run AUTOSHIFT) before performing a change analysis. However, for this lesson, the Radarsat-2 scenes provided are well aligned, so running AUTOSHIFT is not necessary.

EExxaammpplleess ooff ppoossssiibbllee wwoorrkkfflloowwss On page 136, Figure 52 shows a typical workflow for detecting and interpreting incoherent changes in a pair of SAR images.

With most applications, applying the same calibration type to all of the data (sigma-0, Beta-0, gamma-0) is more important than the selected calibration type itself. The second step is to verify if all the data is well aligned. If not, many workflows are possible depending on the data type, the severity of the misalignment, and the application objectives.

If orbit segments are available, you can use OrthoEngine to collect GCPs and orthorectify the data. If an orthorectification is inappropriate or if there is insufficient information on the space-borne or airborne sensor, a different technique (polynomial, thin-plate spline) can be used to geocode the data. If there is only a slight misalignment between an image pair, you can use the AUTOSHIFT algorithm (Algorithm Librarian) to coregister the pair.

It is worth mentioning that any geocoding applied to complex data will keep them as such once they are geocoded. By doing so, they will remain compatible with the change-detection algorithms that require complex data in input (CCDPHASE and CCDWISH).

The third step concerns the change detection. CCDINTEN, CCDPHASE, and CCDWISH can be used alone or in combination to produce images of change based on different change metrics (see Table 5). Similar to the output from polarimetric decompositions (Module 3), output from the change-detection algorithms do not generally represent the end of the workflow, but rather the beginning.

One option is to extract areas of change based on image thresholding to produce binary maps representing the changed areas versus areas of no change. It is also possible to convert those binary maps into vector layers so they can be overlaid or combined with ancillary data for change interpretation.

Another option for change interpretation, if working with fully polarimetric data, is to apply one or many polarimetric decompositions on the same data set. In this case, the change-detection algorithms provide the magnitude of change (quantitative) while the polarimetric decompositions will help you to understand the nature of change (qualitative) by identifying the scattering mechanisms. This strategy will be demonstrated in Lesson 6.4.

Finally, in order to evaluate the same area, a similar processing-window size should be used for change detection and for polarimetric decompositions.


Figure 52. Incoherent change detection, example of a possible workflow. Dotted boxes and arrows indicate nonmandatory steps.

A similar workflow for change detection can be applied to coherent targets; that is, hard targets that cover from one to a few tens of pixels that can be compared to canonical targets (see Module 3, polarimetry fundamentals). A coregistration between each single-look complex image pair is usually preferred over an orthorectification to preserve the highest possible spatial resolution and to preserve the signal as close as possible to its raw (calibrated) values. Because the characterization of coherent targets relies in good parts on the phase, algorithms like CCDPHASE and CCDWISH are preferred over CCDINTEN using a smaller windows size.

One difficulty is to distinguish change that occurs over few contiguous pixels from the speckle. This is common because (conventional) speckle filtering is usually not applied to data before the change-detection analysis or coherent-target characterization.

Another difficulty is to identify pixels that correspond to coherent targets according to tests (assumptions) that range from a simple thresholding of numeric values—to extract bright pixels—to more sophisticated tests that include an evaluation of the target phase, a comparison of the target with its surrounding area (the "clutter", see Lesson 4.2), or both.

The Whitening Filter (PSWHITE) algorithm or the Symmetric Scattering Characterization algorithm (PSSSCM) can be used to find and characterize such targets. Coherent polarimetric decomposition is another way to characterize coherent targets for those pixels identified as such by PSWHITE or PSSCM.

Finally, coherent-target change can be interpreted in the same way as incoherent changes by combining the change-detection results with the target-characterization workflow.

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Figure 53. Possible workflow for coherent change-detection. Dotted boxes and arrows indicate nonmandatory steps.

Today, many sensors, such as Radarsat-2 and TerraSAR-X have a high-resolution-acquisition mode, so the concept of point target is less relevant. For example, large ships can be characterized by tens of pixels, and more for large buildings, while smaller targets like cars and poles, can encompass tens of pixels and can be treated as incoherent targets.

On average, however, such targets are more coherent compared to extended targets (hundreds or thousands pixels). A coherent analysis conducted at full resolution might reveal a precise characterization of their properties and structure.

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LLeessssoonn ssuummmmaarryy In this lesson you:

Learned about the options available for change detection in Geomatica

Examined examples of possible workflows for coherent and incoherent change detection.


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LLeessssoonn 66..22:: PPeerrffoorrmmiinngg aann iinntteennssiittyy--cchhaannggee ddeetteeccttiioonn In this lesson, you will:

Run an intensity-change detection using the CCDINTEN algorithm on the HH, HV, and VV channels and the SPAN

Analyze the results of intensity-change detection

To complete this lesson, you will need:

FQ29_20100507sig.pix, located in: ~\SAR_Training\Radar\Flevoland_RS2_SLC\FQ29_20100507\

To ingest the Radarsat-2 scene located in ~\SAR_Training\Radar\Flevoland_RS2_SLC\FQ29_20100531\, using the FQ29_20100531sig.pix file

CCDINTEN measures the change in total radar backscattering between the test and reference SAR images by comparing the sum of the intensities of the input channels. Given two registered detected or single-look complex SAR images, CCDINTEN determines the overlap area, normalizes the intensity values based on the total span of intensity values, and calculates the change (as a percentile).

If the image is single-look complex, the intensity is derived and output to the specified output file. CCDINTEN is NoData-aware, meaning that pixels that contain a value defined as NoData in the metadata of the input file are excluded from processing. The input and reference layers must be comparable on a corresponding basis; that is, the first layers from each image must have the same polarizations.

To open the CCDINTEN algorithm 1. In Focus, on the Tools menu, click Algorithm Librarian.


3. In the Find what box, type CCDINTEN, and then click Find Next.

4. Double-click the CCDINTEN algorithm. The CCDINTEN Module Control Panel window appears.

To set up the CCDINTEN algorithm 1. Under Input Ports, expand the Input: Input complex or detected

SAR image entry, click Browse, and then open the FQ29_20100507sig.pix file.

2. Select the HH channel.

3. Under Input Ports, under Reference: Reference complex or detected SAR image, click Browse, and then open FQ29_20100531sig.pix.

4. Select the HH channel.

5. Under Output Ports, click Browse.

6. In the File name box, enter FQ29_20100507_0531sig_CCDINTEN_15x15_HH.pix as the name of the output file.


7. Click the Input Params 1 tab, and then in the Window size list, click 15.

8. Click Run.

Exercise 1: Repeat steps 1 to 7, by selecting the HV channel instead of HH. Specify FQ29_20100507_0531sig_CCDINTEN_15x15_HV.pix as the name of the output file.

Repeat steps 1 to 7, by selecting the VV channel instead of HH. Specify FQ29_20100507_0531sig_CCDINTEN_15x15_VV.pix as the name of the output file.

Repeat steps 1 to 7, by selecting all the raster channels instead of HH. Specify FQ29_20100507_0531sig_CCDINTEN_15x15_span.pix as the name of the output file.

Exercise 2: Interpret and compare the results of the CCDINTEN change detection for HH, HV, VV, and the span.

HH/R: 0507

/G:0531/B:0507 HH

Change metric 15x15 HH

Ranked change 15x15

HV/R: 0507

/G:0531/B:0507 HV

Change metric 15x15 HV

Ranked change 15x15

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VV/R: 0507

/G:0531/B:0507 VV

Change metric 15x15 VV

Ranked change 15x15

Span

/R: 0507 /G:0531 /B:0507 Span

Change metric 15x15 Span

Ranked change 15x15

Figure 54. CCDINTEN change-detection results using a 15x15 processing window

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Ran intensity-change detection on SAR data

Analyzed the results of the intensity-change detection


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LLeessssoonn 66..33:: WWiisshhaarrtt cchhaannggee ddeetteeccttiioonn In this lesson, you will:

Run an intensity-change detection using the CCDWISH algorithm on the SPAN

Analyze the results of Wishart change detection



FQ29_20100531sig.pix, located in ~\SAR_Training\Radar\Flevoland_RS2_SLC\FQ29_20100531\

CCDWISH measures change between two single-look complex SAR images, based on modified Wishart statistics. The statistical probability of the area of interest being "the same" is computed in the defined analysis window. Areas with a high probability are considered to be unchanged, while areas with a low probability are considered to be changed; that is, they have a low probability of being the same.

The Wishart test statistic is much more sensitive to the differences than test statistics based only on the intensity; that is, CCDWISH can indicate changes not detected by CCDINTEN.

To open the CCDWISH algorithm 1. In Focus, on the Tools menu, click Algorithm Librarian.


3. In the Find what box, type CCDWISH, and then click Find Next.

4. Double-click the CCDWISH algorithm. The CCDWISH Module Control Panel window appears.

To set up the CCDWISH algorithm 1. Under Input Ports, under the Input: Input complex or detected SAR

image, click Browse, and then open FQ29_20100507sig.pix.

2. Select each raster in the FQ29_20100507sig.pix file.

3. Under Input Ports, under the Reference: Reference complex or detected SAR image, click Browse, and then open FQ29_20100531sig.pix.

4. Select each raster in the file FQ29_20100531sig.pix.

5. Under Output ports, click Browse, and then in the File Name box enter FQ29_20100507_0531sig_CCDWISH_15x15_span.pix as the name of the output file.

6. Click the Input Params 1 tab, and then in the Window size list, click 15.

7. Click Run.


Exercise 4: Compare the change-detection results for the span obtained using CCDINTEN and CCDWISH. Which type of land use and land cover shows the most difference, and which one shows the least difference?

CCDWISH change metric CCDWISH ranked change R: CCDINTEN ranked

G: CCDWISH ranked B: CCDINTEN ranked

Figure 55. CDWISH change-detection results for span (15x15 window)

Exercise 5: The differences in results between CCDINTEN and CCDWISH shown in Figure 55 may be linked to a change that occurred in the HH-VV correlation or phase difference instead of a change in intensity. To verify this, use the PSCC algorithm to calculate the correlation coefficient between the HH and VV channels.

To open the PSCC algorithm 1. In Focus, on the Tools menu, click Algorithm Librarian.


3. In the Find what box, type PSCC, and then click Find Next.

4. Double-click the PSCC algorithm. The PSCC Module Control Panel window appears.

To set up the PSCC algorithm 1. Under Input Ports, expand the Input: Input polarimetric SAR image

entry, click Browse, and then open _20100507sig_PSBOXCAR_7.pix.


3. Under Output Ports, expand the Output: Output correlation coefficient raster entry, click Browse, open the ~\SAR_Training\Radar\Flevoland_RS2_SLC\FQ29_20100507\ folder, and then specify FQ29_20100507sig_PSBOXCAR_7_PSCC_HH-VV.pix as the name of the output file.


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5. In the First input polarization list, click HH in the Second input polarization list, click VV, and then in the Angle measurement list, click Degrees.

6. Click Run.

Repeat steps 1 to 5 selecting FQ29_20100531sig_PSBOXCAR_7.pix in the Inputs Ports and specifying FQ29_20100531sig_PSBOXCAR_7_PSCC_HH-VV.pix as the output file name.

Correlation coefficient

magnitude (2010-05-07)

Correlation coefficient magnitude

(2010-05-31)

R: PSCC 05-31 G: PSCC 05-07 B: PSCC 05-31

Figure 56. PSCC correlation coefficient ([0-1]) between HH and VV channel.

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Ran a Wishart change detection on SAR data

Analyzed the results of a Wishart change detection

Compared the results of a Wishart change detection and an intensity-change detection

Compared the results of a Wishart change detection with a change observed in the HH-VV correlation coefficient


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LLeessssoonn 66..44:: PPeerrffoorrmmiinngg aa pphhaassee cchhaannggee ddeetteeccttiioonn In this lesson, you will:

Run a phase-change detection using the CCDPHASE algorithm

Analyze the results of the phase-change detection



FQ29_20100531sig.pix, located in ~\SAR_Training\Radar\Flevoland_RS2_SLC\FQ29_20100531\

CCDPHASE measures change based on the average coherence (over a specified window size) between two coregistered single-look complex SAR images. Coherence values will range between zero and one, where areas of change will have coherence values close to zero (that is, they are incoherent) while unchanged areas will have higher values (that is, they will be coherent).

To open the CCDPHASE algorithm 1. In Focus, on the Tools menu, click Algorithm Librarian.


3. In the Find what box, type CCDPHASE, and then click Find Next.

4. Double-click the CCDPHASE algorithm. The CCDPHASE Module Control Panel window appears.

To set up the CCDPHASE algorithm 1. Under Input Ports, expand the Input: Input complex or detected

SAR image entry, click Browse, and then open FQ29_20100507sig.pix.


3. Under Input Ports, under the Reference: Reference complex or detected SAR image entry, click Browse, and open FQ29_20100531sig.pix.


5. Under Output ports, click Browse, and then enter FQ29_20100507_0531sig_CCDPHASE_span.pix as the name of the output file.

6. On the Input Params 1 tab, in the Window size list, click 15.

7. Click Run.


Span

Change metric Span

Ranked change

Figure 57. CCDPHASE change-detection results (15x15 window)

Exercise 5: Run the CCDPHASE detection algorithm for HH, HV and VV channels.

HH

Change metric 15x15 HH

Ranked change 15x15

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HV Change metric 15x15

HV Ranked change 15x15

VV

Change metric 15x15 VV

Ranked change 15x15

Figure 58. CCDPHASE change-detection results (15x15 window)

Exercise 6: Compare the CCDPHASE change-detection results to the HH-VV phase difference.

PSPHDIFF derives the phase-angle difference between two polarizations of a polarimetric SAR (POLSAR) data set. The dual-polarization and quad-polarization polarimetric data are accepted for processing.

To open the PSPHDIFF algorithm 1. In Focus, on the Tools menu, click Algorithm Librarian.


3. In the Find what box, type PSPHDIFF, and then click Find Next.

4. Double-click the PSPHDIFF algorithm. The PSPHDIFF Module Control Panel window appears.

To set up the PSPHDIFF algorithm 1. Under Input Ports, expand the Input: Input polarimetric SAR image

entry, click Browse, and then open FQ29_20100507sig_PSBOXCAR_7.pix.


3. Under Output ports, click Browse, and then enter FQ29_20100507sig_PSBOXCAR_7_PSPHDIFF_HHVV.pix as the name of the output file.


5. In the First input polarization list, click HH, in the Second input polarization list, click VV, and then in the Angle units list, use the default setting Degrees.

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6. Click Run.

Repeat steps 1 to 5 using FQ29_20100531sig_PSBOXCAR_7.pix in step one and specifying FQ29_20100531sig_PSBOXCAR_7_PSPHDIFF_HHVV.pix in step 2.

2010-05-07, HH-VV phase

2010-05-31, HH-VV phase

R:05-07 / G:05-31 B:05-07

Figure 59. PSPHDIFF phase difference between HH and VV channels

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LLeessssoonn 66..55:: IInnccoohheerreenntt cchhaannggee ddeetteeccttiioonn wwoorrkkffllooww In this lesson you will:

Use CCDWISH to produce a map of changes

Extract areas of change using channel thresholding

Export the areas of change to a vector file using BIT2POLY

Interpret the nature of changes using a polarimetric decomposition (PSTOUZIDEC)

DDaattaa pprreeppaarraattiioonn To complete this lesson you will need:

The FQ29_20100507_0531sig_CCDWISH_15x15_span.pix file produced in Lesson 6.3. If this file has not been produced, it can be generated using CCDWISH (15x15 processing windows) using as input the two Radarsat-2 Flevoland data sets ingested with the sigma-naught calibration (FQ29_20100507sig.pix and FQ29_20100531sig.pix).

The results from the Touzi decomposition (PSTOUZIDEC) applied on FQ29_20100507sig.pix and FQ29_20100531sig.pix. Remember to first filter those files using PSBOXCAR with a 7x7 processing window. The outputs can be named FQ29_20100507sig_PSBOXCAR_7_PSTOUZIDEC.pix and FQ29_20100531sig_PSBOXCAR_7_PSTOUZIDEC.pix.

AArreeaa--ooff--cchhaannggee eexxttrraaccttiioonn uussiinngg bbaanndd tthhrreesshhoollddiinngg After the change-metric file is produced, the next step is to extract the areas of change, the possibilities of which are numerous. Image thresholding is a simple technique in which numeric values above a certain value (threshold) will be considered as change.

The question is how to determine the threshold value? While there is no definitive answer—the threshold value depends on the situation, the application objective and the processing-window size used—a good starting point is to look at the histogram of the change metric.

To display the histogram of a channel 1. In Focus, open the file in question.

2. Click the Files tab, and then expand the Rasters list of FQ29_20100507_0531sig_CCDWISH_15x15_span.pix.

3. Right-click the third channel (CCDWISH: Change metric), and then click "Histogram with statistics". The Histogram with Statistics window appears.

The histogram for the CCDWISH change metric is shown in Figure 60. The histogram of the same metric produced with a 7x7 processing window is also showed for comparison.


As a reminder, the change metric from CCDWISH (and CCDPHASE and CCDINTEN) does not consider the direction of change; therefore, the higher the number, the stronger the change is according to the metric. Based on a visual inspection of the histogram, the majority of pixels are distributed between 24.1 and 31 with a mean of 25.52. The shape of the histogram suggests that a value of approximately 26 may be a good starting point for setting the threshold value.

A) CCDWISH: Change metric, 15x15 B) CCDWISH: Change metric, 7x7

Figure 60. CCDWISH change-metric histograms

Using Focus EASI Modeler to set threshold value and extract areas of change The change metric will be thresholded and the result stored in a bitmap layer.

1. If not open already, in Focus, open FQ29_20100507_0531sig_CCDWISH_15x15_span.pix.

2. Click the Files tab, and then select the FQ29_20100507_0531sig_CCDWISH_15x15_span.pix file.

3. Right-click, point to New, and then click Bitmap Layer.

4. Load the (empty) bitmap.

5. Right-click the bitmap, and then click View. The bitmap is displayed automatically at the top of the tree list on the Map tab.

6. On the Tools menu, click EASI Modeling. You are now ready to extract the areas of change.

7. Using the following script, extract all the pixels that are above 26 and output the results in the new bitmap (%%2).

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A visual inspection indicates that a value of 26 may to be too permissive, especially over the water areas (Figure 61, A and C). Inland, most of the detected changes are associated to agricultural land and wetlands. None of the urban areas are showing changes (except for some localized targets), which is expected because of the short lapse of time between the two acquisitions. This is also an indication that the coregistration between the two images is good.

In comparison, a threshold value of 27.5 produced better results (Figure 61, B and D) by restricting the changes to the most important ones while reducing the amount of change over the water area.

A) CCDWISH >26 B) CCDWISH >27.5

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C) CCDWISH >26 (detail) D) CCDWISH >27.5 (detail)

Figure 61. Thresholding of the CCDWISH change metric

CCoonnvveerrttiinngg aa bbiittmmaapp llaayyeerr ttoo aa vveeccttoorr llaayyeerr The bitmap representing the areas of change can now be exported to a vector-polygon layer. The benefits of this are:

A bitmap layer is characterized by a pixel-and-line-coordinate system tied to the raster file from which it has been created. A vector layer is referenced according to a geographical projection and not attached to a particular layer.

It is easier to overlay vectors on relevant images (data) layers to interpret change

It is possible to filter the polygons of change based on their areas or shape, if necessary

It is possible to automatically collect statistics for every polygon in a vector layer

To convert a bitmap layer to a polygon layer 1. In Focus, on the Tools menu, click Algorithm Librarian.


3. In the Find what box, type BIT2POLY, and then click Find Next.

4. Double-click the BIT2POLY algorithm.

5. If not already open, under Input Ports, click Browse, and then select the FQ29_20100507_0531sig_CCDWISH_15x15_span.pix file.

6. Under Input: Bitmap Layer, select the bitmap you want to convert to a polygon layer. For this exercise, select the bitmap that corresponds to CCDWISH>27.5.

7. Under Output Ports, click the Output: Polygon Layer entry, click Untitled.pix, and then click Browse.


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8. In the File Selector window, enter a file name and a location. For this exercise, name the file polygons_CCDWISH_15_Change_sup27.5.pix

9. Click the Input Params 1 tab. Optionally, you can select the Smooth Vector option.

10. Click Run. Each contiguous region of on pixels (i.e. values of 1) will be grouped together into a single polygon. Only pixels with a value of 1 in the input bitmap result in polygons in the output. Create a copy of polygons_CCDWISH_15_Change_sup27.5.pix.

FFiilltteerriinngg aanndd eexxttrraaccttiinngg ssttaattiissttiiccss ffrroomm ppoollyyggoonnss For each polygon, BIT2POLY will automatically create four fields:

Shape ID (unique identifier)

Area (sq m)

Perimeter (m)

PixelValue (inherited from the bitmap layer)

To view the vector file attributes table 1. In Focus, click the Maps tab.

2. Right-click the vector file for which you want to see the attribute table, and then click Attribute Manager. The Attribute Manager window appears.

In the following procedure, the boxes in the Area (sq m) column will be used to remove all polygons of area inferior to 3000 sq m that correspond to approximately 110 pixels.


To filter the polygons based on area 1. In the Attribute Manager window, on the Record menu, point to Query

By, and then click Example.

2. In the Attributes box, select Area.

3. Click the menu, and then select the Less than operator (<).

4. In the New statement box, enter 3000, and then click Add.

5. Click Apply. All polygons with an area less than 3000 sq m (4458 of 6116 polygons) will be selected (Figure 62).

6. In the Attribute Manager window, on the Field menu, click Delete. All selected polygons are removed.

7. On the Layer menu, click Save.

8. Rename the vector file polygons_CCDWISH_15_Change_sup27.5_sup3000sqm.pix.

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Figure 62. Polygons >3000 sq m (white) superposed on the original bitmap layer in red (CCDWISH>27.5)

EExxttrraaccttiinngg ssttaattiissttiiccss ttoo tthhee ppoollyyggoonn aattttrriibbuutteess ttaabbllee For each polygon, you will now extract a series of statistics that will help you to understand the nature of change. For this example, you will use the polarimetric parameters of the Touzi decomposition (see Lesson 3.3). For each polygon, the mean of the following parameters will be calculated:

1s , dominant symmetric scattering type

Span, which corresponds to the sum of the eigenvalues ( 1 + 2 + 3 )

N1 , normalized first eigenvalue, which corresponds to 1 /span

To continue this exercise, you will need to apply the Touzi decomposition (PSTOUZIDEC) to the Radarsat-2 Flevoland data set. Remember to add extra 32R channels to each file and compute the normalized version of the first eigenvalue (see Lesson 3.3).

You will now use the Geomatica Overlay Wizard to compute the mean of each parameter of each polygon in polygons_CCDWISH_15_Change_sup27.5_sup3000sqm.pix. This example will calculate the mean of the dominant scattering type ( 1s ) of the May 7 image.

To use the Overlay Wizard 1. In Focus, on the Analysis menu, click Overlay.

The Overlay Wizard window appears.

2. Under Select type of overlay, click the Statistical - Computes statistics by virtually overlaying them option button, and then click Next.


3. Under Primary Input, under Layer, click the File list, and then select the vector file to add the new attributes (statistics); that is, select polygons_CCDWISH_15_Change_sup27.5_sup3000sqm.pix.

4. Under Secondary Input, under Layer, click the File list, and then select the file containing the layer of interest; that is, select FQ29_20100507sig_PSBOXCAR_7_PSTOUZIDEC.pix. If the file is already open in Focus it will be available in the list; otherwise, to open the file, click Browse.

5. Click the Layer list, select (3 [32R] Dominant Touzi Alpha_S Parameter, and then click Next.

6. In the table, select one or more attributes to compute (you may need to click Advanced to see all of them), and then click Finish. The selected attributes will be calculated.

7. Repeat steps 1 to 7 for each layer of interest. After all of the statistics have been collected, the vector file should contain 1663 Records (polygons) and 10 fields. This file can be found in ~\SAR_Training\Radar\Vectors\ (polygons_CCDWISH_15_Change_sup27.5_sup3000sqm.pix ).

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Tip You can export the attributes to other formats, such as .xls (Microsoft Excel) and .ora (Oracle): on the Layer menu, click Export Attributes.

IInntteerrpprreettiinngg tthhee cchhaannggeess The polygons representing the areas of change, the collected statistics for each polygon, the original parameters from the Touzi decomposition, and the Landsat images can all be used to interpret and understand the changes.

In Figure 61 on page 154, you can see that most changes are associated with surface scattering in agricultural fields. This is confirmed in Figure 63, where 1s shows values inferior to 40° for the two dates for most of the polygons. In the same figure, ( 1s ), most of the polygons are clustered along the 1:1 line and do not show a big change in the dominant scattering mechanisms, which might look counterintuitive at first.

In comparison, as expected, most polygons show a strong difference in the span scatter plot because CCDWISH uses the trace of the coherency matrix (Figure 63, 12-D). One explanation is that soil moisture increases the backscatter power, but with little affect on the phase relationship between the two orthogonal polarizations used for signal Tx/Rx (usually HH-VV). The backscattering mechanism remains the same (wet soil versus dry),especially because the backscattered power is normalized in the calculation of 1s .


A) 1s , dominant symmetric scattering type

B) N1 , normalized first eigenvalue

C) Span (intensity) D) Span (Intensity, axis cut at 0.5)

Figure 63. Change-detection results, agricultural area

Some polygons are characterized by a change in both 1s and the span. A typical situation is fields occupied by row crops where the combination of the plant growth and rows increase 1s (from surface toward dipole or a week double-bounce scattering) while the span has decreased due to soil drying between May 7 and May 31 (Figure 63, A and B), Figure 64 A and B vs. C).

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Figure 65 and Figure 66 show changes associated with moving targets over land or water. Those targets produced generally to the most dramatic changes in the scattering type, the span and the purity of the backscattered power represented here by N1 .

You can see in Figure 65, E) that the workflows presented in this section worked well in urban areas in filtering out the few changes which were associated to small differences in the viewing geometry between the two images. On the other end, in the same figure, you can also see the effect of the use of a large window size (15x15) for CCDWISH, where changes associated with small targets have been expanded to their surrounding areas.

A) May 7, 2010, 1s , dominant symmetric

scattering type

B) May 31, 2010, 1s , dominant symmetric

scattering type

C) Span (R: May 7, G: May 31, B:

May 7)

D) May 7, 2010, N1 , first eigenvalue

E) May 31, 2010, N1 , first eigenvalue

Figure 64. Change-detection results, agricultural area

Note The polygons correspond to CCDWISH>27.5 and> 3000 sq m.

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scattering type


scattering type


May 7)

D) High-resolution imagery (from Google Earth)

E) CCDWISH, original change-detection bitmap in

red.

Figure 65. Change-detection results, Urban environment.

Note The polygons correspond to CCDWISH>27.5 and> 3000 sq m.

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scattering type


scattering type


May 7)

D) May 7, 2010, N1 , first eigenvalue

E) May 31, N1 , first eigenvalue

Figure 66. Change-detection results, ship detection

Note The polygons correspond to CCDWISH>27.5 and> 3000 sq m

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Used CCDWISH to produce a map of changes

Extracted areas of change using channel thresholding

Exported the areas of change to a vector file using BIT2POLY

Interpreted the nature of changes using a polarimetric decomposition (PSTOUZIDEC)

RReeffeerreenncceess Masroor Hussain, Dongmei Chen , Angela Cheng , Hui Wei , David Stanley (2013). Change detection from remotely sensed images: From pixel-based to object-based approaches. ISPRS Journal of Photogrammetry and Remote Sensing, Vol.80, p.91-106.

Jensen, J. R (2005). Introductory Digital Image Processing: A Remote Sensing Perspective. 3rd ed. Prentice-Hall Series in Geographic Information Science. Pearson/Prentice Hall, Upper Saddle River, N.J., USA, 526 pages.

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AAppppeennddiixx AA

PPrroocceessssiinngg wwiitthh ssiinnggllee oorr dduuaallppooll ddeetteecctteedd ddaattaa Appendix A has three lessons:

Applying a band ratio or a band difference to a pair of detected images using EASI Modeling

Performing intensity-change detection

Computing SAR texture measures

The goal of this appendix is to show how Geomatica Focus can increase the information content of a detected single or dual polarization image. This appendix uses a detected dual-polarization image (simulated) acquired over the Flevoland region of the Netherlands.

AAppppllyyiinngg iinntteennssiittyy cchhaannnneellss rraattiioo oorr ddiiffffeerreennccee uussiinngg EEAASSII mmooddeelliinngg oonn ddeetteecctteedd ddaattaa In this lesson you will:

Apply a band ratio and a band difference between two polarimetric channels using EASI Modeling

Compare the band ratio and band difference results

BBand ratio and band difference and ratio and band differenceThe band-ratio and band-difference techniques can be used to extract:

Changes between two images acquired on different dates

Differences between two polarization channels of the same image

You can use various strategies to extract the changes or differences between two images or channels. First, you can use a pixel-by-pixel ratio or difference, and then filter the result using an average (or median) filter. You can also compute the mean (or other statistics) over a window, return the result to the central pixel, and then proceed to the ratio or difference. Another strategy is to first filter the data, and then proceed to a pixel-by-pixel comparison between the two channels or images.

Data preprocessing Using the concepts learned in Lesson 1.4, Lesson 1.5, and the Flevoland scene located in ~\SAR_Training\ Radar\Flevoland_RS2_SLC\FQ29_20100507:

Appendix A SAR processing with Geomatica

1. Ingest the product.xml file into a PCIDSK file using sigma as the calibration type. Name the file FLE_FQ29_20100507sig.

2. Use the PSIQINTERP algorithm to convert complex data to detected data. Convert the HH channel to intensity. Name the file FLE_FQ29_20100507sig_HH-HV.

To preprocess an image when applying a speckle filter 1. In Focus, on the Tools menu, click Algorithm Librarian.


3. In the Find what box, type FSPEC, and then click Find Next.

4. Double-click the FSPEC algorithm. The FSPEC Module Control Panel window appears.

5. Under Input Ports, open the ~\SAR_Training\Radar\Flevoland_RS2 folder, and then select the FLE_FQ29_20100507sig_HH-HV file.

6. Under Output Ports, select Untitled.pix, and then click Browse. The File Selector window appears.

7. In the File name box, type FLE_FQ29_20100507sig_HH-HV_FEfrost_7.pix.


9. In the Image Units list, click Power, in the Filter Type list, click Enhanced Frost, in the Filter X Size list and Filter Y Size list, respectively, click 7. Leave the other options set to the default values.

10. Click Run.

To perform a channel ratio and a channel difference using EASI modeling 1. Add two new raster layers: in Focus, click the Files tab, right-click the

FLE_FQ29_20100507sig_HH-HV_FEfrost_7.pix. file, point to New, and then click Raster Layer. The Add Image Channels window appears.

2. Add two (2) 32 bit real channels, and then click Add.

3. On the Tools menu, click EASI Modeling. The EASI Modeling window appears.

4. In the Input File list, click the FLE_FQ29_20100507sig_HH-HV_FEfrost_7.pix file.

5. Enter the code to perform the ratio and difference (the comments can be omitted), as shown in Error! Reference source not found..

Note The modeling results will be saved automatically.

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SAR processing with Geomatica Appendix A

Exercise 1: Compare the ratio and difference results to the original HH and HV channels.

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Applied a band ratio and a band difference between two polarimetric channels using EASI Modeling

Compared the band ratio and band difference result


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AApplying an intensity-change detection on detected pplying an intensity-change detection on detecteddata dataIn this lesson you will:

Apply intensity-change detection between two polarimetric channels

Analyze the results

The CHDET algorithm detects changes in SAR imagery. Given two registered amplitude or intensity SAR images, CHDET calculates a difference image, removes the speckle noise, and generates bitmap overlays, which display changes greater than a specified threshold and the number of pixels.

CHDET is intended to detect changes between two single-channel images acquired on different dates. However, you can use this program to detect changes between two channels of a dual-pol image.

AAppppllyyiinngg aann iinntteennssiittyy--cchhaannggee ddeetteeccttiioonn You can apply an intensity-change detection with the CHDET algorithm.

To open the CHDET algorithm 1. In Focus, on the Tools menu, click Algorithm Librarian.


3. In the Find what box, type CHDET, and then click Find next.

4. Double-click the CHDET algorithm. The CHDET Module Control Panel window appears.

To set up the parameters for CHDET 1. Open the nonfiltered Fle_FQ29_20100507sig_HH-HV.pix file.

2. Under Input Ports, expand the InputA: Layer A entry, and then select the HH channel.

3. Expand the InputB: Layer B entry, and then select the HV channel.

4. Under Output Ports, expand the Output: A-B Difference Layer entry, and then select the Untitled.pix check box.

5. Click Browse, open the folder in which you want to save the file, and then, in the File name box, type Fle_FQ29_20100507sig_CHDET-avg7.pix.


7. Set the following options:

For Filter Type, select AVG For Positive Threshold and Negative threshold, enter 5 and -30

decibels, respectively For Image Format, select Power For Filter Size, select 7


For Number of Looks, enter 1 For Data Type, select 32R.

8. Click Run.

Exercise 2: For which kind of land use and land cover is HH higher than HV? For which kind of land use and land cover is HH similar to HV?

Compare the results from the previous lesson (ratio). What are the main differences that can be observed between these two techniques? Are some differences related to type of filter used?

To facilitate the interpretation of the results, a Landsat-5 image mosaic of the region is provided. Select the Flevoland_L5_20100906_p198_r23r24.pix file located in the ~\SAR_Training\Landsat folder.

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HH (non-filtered) HV (non-filtered)

HH/HV ratio, Enhanced Frost filter 7x7

(Lesson 3.1) CHDET HH/HV ratio, Average 7x7

(Lesson 3.2)

Figure 67. Band-ratio results comparison

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Applied and intensity change detection between two polarimetric channels

Analyzed the results


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CComputing SAR texture measures omputing SAR texture measuresIn this lesson, you will:

Calculate SAR-specific texture measures using SARTEX

Calculate texture measures based on co-occurrence matrices using TEX

The SARTEX algorithm creates a set of texture images from a single channel in an input image. The radar-specific measures are based on pixel-value statistics in a window surrounding each pixel. In SAR images, a single pixel conveys very little information about the underlying ground cover. Only by looking at the values of many pixels can some interpretations be made. The statistics that apply best to a given area depend on the actual image formation process.

The extracted texture measures may be used as input features to classification algorithms.

The SAR-specific texture measures in this module account for radar-image formation and statistical properties of radar speckle. Therefore, it is essential that no prior filtering has been performed on the input channel.

Texture measures extracted by SARTEX are based on SAR-specific statistics of pixel values in the rectangular window of user-specified dimensions. They complement measures extracted in the module TEX, based on a gray-level co-occurrence matrix (GLCM), and in the module HISTEX, based on the histogram of pixel values in a window. The three types of measures can be combined for further analysis, such as segmentation and classification of the image.

The optimal window size depends on image characteristics (terrain roughness, land-cover type, imaging parameters). With large windows, some blocky artifacts can appear in texture images, if a small, bright target is fully enclosed by the window. Therefore, a small-to-moderate window size is recommended.

CCrreeaattiinngg SSAARR--ssppeecciiffiicc tteexxttuurree iimmaaggeess To create SAR-specific texture images, you use the SARTEX algorithm.

To open the SARTEX algorithm 1. In Focus, on the Tools menu, click Algorithm Librarian.


3. In the Find what box, type SARTEX, and then click Find Next.

4. Double-click the SARTEX algorithm. The SARTEX Module Control Panel window appears.

To set up the parameters for SARTEX 1. Under Input Ports, expand the Input: Input detected SAR band

entry.

2. From the Fle_FQ29_20100507sig_HH-HV.pix file, select the HH channel.


3. Under Output Ports, expand the Output: Output texture measure raster entry, select Untitled.pix and then click Browse. The File Selector window appears.


5. In the File name box, type Fle_FQ29_20100507sig_HH-HV_SARTEX_7.pix, and click Save.


7. Under Basis of SAR Texture Measurement, select all four of the check boxes.

8. In the Horizontal Filter Size list, click 7.

9. In the Vertical Filter Size list, click 7.

10. In the Image Units list, click Power. Note: It is important to select the correct image format for the input layer. Any required conversions are performed internally to use the correct values for each computed texture measure.

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11. Click Run.

Note To solve a specific problem (distinguishing between several land cover types for example), the usual approach is to compute all texture measures and use the measure that provides the best result.

Table 6. SAR-specific texture measures

Texture measure Explanation Normally distributed intensity (VI)

This is a ratio of the mean of squared intensity to the mean intensity squared. It is applicable when pixel intensity has a Gaussian distribution.

Normally distributed amplitude (VA)

This is a ratio of mean intensity to the squared mean amplitude. It is applicable when pixel amplitude has a Gaussian distribution.

Lognormal distributed intensity (VL)

This is a difference of the mean value of the squared intensity logarithm and the square of the mean intensity logarithm. It is applicable when pixel intensity has a log-normal distribution.

K-distributed intensity (U) This is a normalized log measure of texture. It approximates k-distribution when the number of looks in the SAR image is large.

Exercise 3: Compare the different textures measure from SARTEX.

VI, normally distributed intensity VI, detail

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VA, normally distributed amplitude VA, detail

VL, lognormal distributed intensity VL, detail

U, K-distributed intensity U, detail

Figure 68. SARTEX results (7x7)

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Creating teCreating texture images based on co-occurrence matrices using TEX

xture images based on co-occurrence matrices usingTEX

The TEX algorithm calculates a set of texture measures for all pixels in an input image. The measures are based on second-order statistics computed from the gray-level co-occurrence matrices. Either texture measures for a specific direction or directional-invariant measures can be computed. The texture measures can be used as input features to classification algorithms.

To open the TEX algorithm 1. In Focus, on the Tools menu, click Algorithm Librarian.


3. In the Find what box, type TEX, and then click Find Next until the TEX algorithm is selected.

4. Double-click the TEX algorithm. The TEX Module Control Panel window appears.

To set up the parameters for TEX 1. Under Input Ports, expand the Input: Image Layer entry.

2. From the Fle_FQ29_20100507sig_HH-HV.pix file, select the HH channel.

3. Under Output Ports, expand the Output: Output texture measure Layer(s) entry, select Untitled.pix, and then click Browse. The File Selector window appears.


5. In the File name field, type Fle_FQ29_20100507sig_HH-HV_TEX_11.pix and click Save.


7. Under Texture Measure PARAMETERS, select the Homogeneity, Variance, Entropy, Angular Second Moment, and the Correlation Texture check boxes.

8. In the Filter X Size list, click 11.

9. In the Filter Y Size list, click 11.

10. In the Number of Gray Levels list, click 32.

11. Click Run.


Exercise 4: Compare the different textures measure from TEX.

Homogeneity, 11x11, 32 grey levels Homogeneity, details

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Standard Deviation, 11x11, 32 grey levels Standard Deviation, details

Entropy, 11x11, 32 grey levels Entropy, details

Angular second moment, 11x11, 32 grey

levels Angular second moment, details

Figure 69. TEX results (11x11)

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Calculated SAR-specific texture measures using SARTEX

Calculated texture measures based on co-occurrence matrices using TEX

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AAppppeennddiixx BB

PPoollaarriimmeettrriicc ddiissccrriimmiinnaattoorrss Polarimetric decompositions are useful for identifying the scattering mechanisms characterizing a point or a distributed target. However, they only characterize a part of the polarimetric information available for a given target. It is possible to deepen the characterization of a target by analyzing its eigenvalues ( i , i=1, 2, 3) produced by a polarimetric decomposition (Module 3).

The polarimetric response plots introduced in Module 3 and Module 4 can also be used to identify the backscattering mechanisms characterizing a target. These responses also provide an analysis of the backscattered power that is often overlooked with the polarimetric decompositions.

Analysis of the peaks and valleys of a polarimetric response plot is particularly interesting: "the polarization plots have peaks at polarizations that give rise to maximum received power, and valleys where the received power is smallest, in agreement with the concept of Huynen's polarization fork in the Poincaré sphere (CCRS, 2007, Boerner, et al. 1998, fig.5-3-9)".

The polarimetric response plot corresponds to a projection of the Poincarée sphere, where the orientation angle ( ) represent the longitude and the ellipticity angle ( ) represents the latitude. Using an increment of 1°, there are more than 16,000 possible combinations of and for signal transmission, and a similar number for reception (16,000 x 16,000 possible transmit-receive combinations).

Fortunately, the SAR Analysis toolbox provides algorithms to automatically analyze the properties of a target using the concept of the Huynen fork on the Poincarée sphere. The PSPOLDIS algorithm is used to calculate a number of polarimetric discriminators for a fully polarimetric SAR (POLSAR) data set. The PSPOLSYN algorithm creates a synthesized backscatter SAR image for arbitrary transmit and receive polarizations. The PSPOLSYNC algorithm creates a synthesized backscatter image to maximize the contrast between two targets.

Appendix B has four lessons:

Generating polarimetric discriminators based on coherency matrix eigenvalues

Generating polarimetric discriminators based on analysis of the Poincarée sphere

Synthesizing a backscatter SAR image for arbitrary transmit and receive polarizations

Maximize the contrast between two targets

Appendix B SAR processing with Geomatica

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GGenerating polarimetric discriminators based on enerating polarimetric discriminators based oncoherency matrix eigenvalues coherency matrix eigenvaluesIn this lesson, you will:

Generate some polarimetric discriminators based on the coherency matrix eigenvalues using EASI Modeling in Focus

Compare the resulting polarimetric discriminators

The entropy and the anisotropy are calculated from the three eigenvalues of the coherency matrix. Several parameters can be derived from these eigenvalues to perform an in-depth analysis of the scattering properties of the examined scene. In this lesson, you will use EASI modeling to calculate a series of polarimetric discriminators.

DDaattaa pprreepprroocceessssiinngg Before you can calculate polarimetric discriminators, you must preprocess your data.

To preprocess the data 1. Ingest into a pix file the

~\SAR_Training\Radar\Flevoland_RS2_SLC\FQ29_20100507 file using product.xml as the key name file. Select sigma as the calibration type, and then name the file Fle_FQ29_20100507sig.pix.

2. Filter the ingested image using the PSBOXCAR algorithm. Select a filter size of 7 x 7, and then name the file Fle_FQ29_20100507sig_PSBOXCAR_7.pix.

3. Using Fle_FQ29_20100507sig_PSBOXCAR_7.pix as input file, apply the Cloude and Pottier decomposition (PSEBA).

4. Click the Input Params 1 tab, and then select the Retrieve eigenvalues and eigenvectors option.

5. Name the output: Fle_FQ29_20100507sig_PSBOXCAR_7_PSEABA.pix From Fle_FQ29_20100507sig_PSBOXCAR_7_PSEABA.pix, extract channels 5, 6 and 7. Name the output: Fle_FQ29_20100507sig_eigen.pix.

6. This new file should contain only three channels.

To calculate a series of polarimetric discriminators 1. In Focus, on the File menu, click Open, open the

~\SAR_Training\Radar\Flevoland_RS2_SLC\FQ29_20100507 folder, and then open the Fle_FQ29_20100507sig_eigen.pix file.

2. Right-click the Fle_FQ29_20100507sig_eigen.pix file, point to New, and then click Raster Layer. The Add Image Channels window appears.

3. Add seven (7) 32-bit real channels, and then click Add.

SAR processing with Geomatica Appendix B

4. In Focus, on the Tools menu, click EASI Modeling. The EASI Modeling window appears.

5. In the Input File list, click Fle_FQ29_20100507sig_eigen.pix.

6. Enter the following script, and then click Run. Note: The script can be loaded from ~\SAR_Training\EASI_script\ AppendixB_PolarimetricDiscriminator.eas.

Exercise 1: Compare the different eigenvalues based on polarimetric discriminators.

Note There is some correlation between anisotropy, entropy, and the polarimetric discriminators based on the coherency matrix eigenvalues. Which Polarimetric discriminators are less correlated? More correlated?

Figure 70. EASI Modeling windows, eigenvalues polarimetric discriminators

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Table 7. Correlation coefficients between the polarimetric discriminators (channel description given in Figure 70, for whole image except open-water areas)

%1 %2 %6 %7 %8 %9 %10 %11 -0.28 x x x x x x x

%2 -0.86 -0.19 x x x x x x

%6 0.80 0.73 0.37 x x x x x

%7 0.29 0.81 -0.59 0.18 x x x x

%8 0.79 -0.78 -0.46 -0.89 -0.35 x x x

%9 -0.89 0.61 0.65 0.84 0.15 -0.96 x x

%10 0.89 -0.61 -0.65 -0.84 -0.15 0.96 -1.00 x

%11 0.88 -0.49 -0.70 -0.76 -0.05 0.89 -0.98 0.98

%12

%1, entropy (H) %2, anisotropy (A) %6, dominant point target

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%7, more than one scattering mechanism

%8, two strong scattering mechanism

%9, fully diffused scattering

%10,polarization fraction %11, radar vegetation index %12, pedestal height

Figure 71. Polarimetric discriminators from the coherency matrix eigenvalues

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Lesson summLesson summary aryIn this lesson, you:

Generated polarimetric discriminators based on the coherency matrix eigenvalues

Compared the polarimetric discriminators produced


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GGeenneerraattiinngg ppoollaarriimmeettrriicc ddiissccrriimmiinnaattoorrss bbaasseedd oonn aannaallyyssiiss ooff tthhee PPooiinnccaarréé SSpphheerree In this lesson, you will:

Generate different polarimetric discriminators using PSPOLDIS

Compare the polarimetric discriminators produced

The PSPOLDIS algorithm calculates a number of polarimetric discriminators for a fully polarimetric SAR (POLSAR) data set. Polarimetric discriminators are useful to characterize the features in your image, by identifying the different types of scattering mechanisms. The discriminators are based on the polarimetric synthesis, and describe the polarimetric response of features in the image.

These include:

Minimum and maximum polarization

Minimum and maximum completely polarized component

Minimum and maximum completely unpolarized component

Minimum and maximum received power

Minimum and maximum scattered intensity

Coefficient of variation

Fractional polarization

The orientation and ellipticity angles for the extrema of the completely polarized component are also written to the output file.

Because PSPOLDIS searches for the extrema of the response at every pixel, the processing can take a long time, depending on the number of lines and pixels in the image, and the step-size values. Reducing step-size values for orientation and ellipticity increases the processing time, but makes the results more accurate. For example, changing STEPPSI and STEPCHI from the default values of 10 degrees to 5 almost quadruples the processing time, but the determined extrema are accurate to within plus or minus 2.5 degrees.

Table 8. PSPOLDIS parameters

No. Parameters Min Max Unit

1. Ratio of the intensity of the maximum polarized part on total power 0 1 N/A

2. Ratio of the intensity of the minimum polarized part on total power 0 1 N/A

3. Maximum intensity of the of the completely polarized component 0 N.A intensity

4. Orientation angle ( ) at the maximum of the completely polarized component

-90 90 Degree

5. Ellipticity angle ( ) at the maximum of the completely polarized component

-45 45 Degree


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No. Parameters Min Max Unit

6. Minimum intensity of the completely polarized component 0 N.A Intensity

7. Orientation angle ( ) at the minimum of the completely polarized component

-90 90 Degree

8. Ellipticity angle ( ) at the minimum of the completely polarized component

-45 45 Degree

9. Maximum intensity of the completely unpolarized component 0 N.A Intensity

10. Minimum intensity of the completely unpolarized component 0 N.A Intensity

11. Maximum of the received power 0 N.A Intensity 12. Minimum of the received power 0 N.A Intensity 13. Maximum of the scattered intensity 0 N.A Intensity 14. Minimum of the scattered intensity 0 N.A Intensity 15. Coefficient of variation 0 1 Unitless 16. Coefficient of fractional polarization 0 1 Unitless

To open the PSPOLDIS algorithm 1. On the Tools menu, click Algorithm Librarian.


3. In the Find what box, type PSPOLDIS, and then click Find Next.

4. Double-click the PSPOLDIS algorithm. The PSPOLDIS Module Control Panel window appears.

To set up the parameters for PSPOLDIS 1. Under Input Ports, expand the Input: Input polarimetric SAR image

entry. The tree expands to show the array segments.

2. Click Browse, open the folder ~\SAR_Training\Radar\Flevoland_RS2_SLC\FQ29_20100507, and then select the FQ29_20100507sig_PSBOXCAR_7.pix file.

3. Select each raster for this file.

4. Under Output Ports, expand the Output: Output polarimetric discriminator entry, select Untitled.pix, and then click Browse. The File Selector window appears.

5. Open the ~\SAR_Training\Radar\Flevoland_RS2_SLC\FQ29 folder.

6. Type FQ29_20100507sig_PSBOXCAR_7_PSPOLDIS.pix as the file name.

7. Click Save. The path and file name are updated in the PSPOLDIS Module Control Panel window.



9. In the Orientation angle step size list and the Ellipticity angle step sizeb list, respectively, click 5.

10. Click Run. The PSPOLDIS analysis runs and the results appear in the Focus viewer.

Exercise 2: Compare the different polarimetric discriminators generated by PSPOLDIS.

% 3, Max. Int. C.P.C %6, Min. Int. C.P.C % 1, Int. Max. Pol.

part/total power

% 4, at max. C.P.P % 5, at max. C.P.P % 2, Int. Min. Pol. part/total

power

%9, Max. Int. C.U.C %10, Min. Int. C.U.C %16, Coef. Frac. Pol.

Figure 72. Polarimetric discriminators from PSPOLDIS (selection)

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Generated different polarimetric discriminators using PSPOLDIS

Compared the polarimetric discriminators produced


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SSynthesizing a backscatter SAR image for arbitrary ynthesizing a backscatter SAR image for arbitrarytransmit and receive polarizations transmit and receive polarizationsIn this lesson you will:

Create synthesized backscatter SAR images for arbitrary transmit and receive polarizations

The PSPOLSYN algorithm creates a synthesized backscatter SAR image for arbitrary transmit and receive polarizations, from a fully polarimetric SAR (POLSAR) data set. Creating an image with polarizations that are different from the original ones may reveal information that is not apparent in the input data set. The input file must represent a non-symmetrized, or a symmetrized, fully polarimetric (quad-polarization) SAR data set in the scattering (s4c or S3c), covariance (c4r6c or C3r3c), coherency (t4r6c or T3r3c), or Kennaugh (k16r or K9r) matrix format.

In Figure 73, an urban target, corresponding to a building not aligned with the radar line of sight, has been selected. This misalignment has created a stronger backscattering in HV, but the backscattering in HH might be lower than what you would normally expect for this kind of target.

Target coordinates 5°38'08.9''E; 52°33'57.8N HH=0.307 (-5.12dB) HV=0.193 (-7.14 dB) VV=0.246 (-6.09 dB) Nb. pixels = 44

R: HH, G:HV, B:VV Target in red (R=HH, G=HV, B=VV)

Figure 73. Urban target

In the previous lesson, you used PSPOLDIS to characterize the backscattered power of the FQ29_20100507sig_PSBOXCAR_7.pix scene. In this lesson, you will try to enhance the backscattered power by generating a synthesized backscatter SAR image that corresponds to the target’s orientation ( ) and ellipticity ( ) angles at the maximum of the completely polarized component (Table 9).

Table 9. PSPOLDIS parameters (selection) for a small urban target

Description Mean (°) Med (°) S-D (°) Ellipticity angle ( ) at the maximum of the completely polarized component

46.1 44.9 11.8

Orientation angle ( ) at the minimum of the completely polarized component

12.5 10.0 5.9


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Description Mean (°) Med (°) S-D (°) Ellipticity angle ( ) at the minimum of the completely polarized component

-57.0 -60.0 18.4

Ellipticity angle ( ) at the maximum of the completely polarized component

-12.9 -10.0 13.4

To open the PSPOLSYN algorithm 1. On the Tools menu, click Algorithm Librarian.


3. In the Find what box, type PSPOLSYN, and then click Find Next.

4. Double-click the PSPOLSYN algorithm. The PSPOLSYN Module Control Panel window appears.

To set up the parameter for PSPOLSYN 1. Under Input Ports, expand the Input: Input polarimetric SAR image


2. Click Browse, open the ~\SAR_Training\Radar\Flevoland_RS2_SLC\FQ29_20100507 folder, and then select the FQ29_20100507sig_PSBOXCAR_7.pix file.

3. Select each raster for this file.

4. Under Output Ports, expand the Output: Output synthesized detected SAR image entry, select Untitled.pix, and then click Browse. The File Selector window appears.


6. In the File name box, type FQ29_20100507sig_PSBOXCAR_7_PSPOLSYN_t1.pix.

7. Click Save. The path and file name are updated in the PSPOLSYN Module Control Panel window.


9. In the Transmitted orientation angle box and the Transmitted ellipticity angle box, enter 46.1 and 12.5, respectively.

10. In the Received orientation angle box and the Received ellipticity angle box, enter 46.1 and 12.5, respectively.


11. Click Run. The PSPOLSYN analysis runs and the results appear in Focus.

Exercise 3: A co-polarized (transmit=received) image has been generated. You will synthesize a second image using the minimum of the completely polarized component angles.

Repeat the previous steps with the following modifications:

In step 5, specify RS2_FQ29_Fle_20100507sig_PSPOLSYN_t2.pix

In step 9, specify -57.0 and -12.9 for the Received orientation angle and Received ellipticity angle boxes, respectively

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T1 T :46.1

T :12.5

R :46.1

R :12.5

Mean = 1.25 (0.96 dB)

T2 T :46.1

T :12.5

R :-57.04

R :-12.95

Mean = 0.12 (-9.2dB)

T3 T :-57.0

T :-12.9

R :-57.0

R :-12.9

Mean = 0.22 (-6.57dB)

Figure 74. Synthesized backscatter SAR images for various transmit and receive polarization configurations

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Created synthesized backscatter SAR images for arbitrary transmit and receive polarizations


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MMaaxxiimmiizziinngg tthhee ccoonnttrraasstt bbeettwweeeenn ttwwoo ttaarrggeettss In this lesson you will:

Create a synthesized backscatter image to maximize the contrast between two targets

The PSPOLSYNC algorithm creates a synthesized backscatter image from a fully polarimetric SAR data set. The polarizations used for synthesis are selected automatically by PSPOLSYNC to maximize the contrast (ratio) between two targets. The polarizations are determined by searching the full range of the orientation and ellipticity angles for the transmitted and received polarizations. The identified transmitted and received polarizations are then used to synthesize the radar backscatter at every pixel in the input data set. The synthesis algorithm is the same as in PSPOLSYN.

The input file must represent a non-symmetrized or symmetrized fully polarimetric (quad-polarization, complex) data set in the scattering (s4c or S3c), covariance (c4r6c or C3r3c), coherency (t4r6c or T3r3c), or Kennaugh (k16r or K9r) matrix format.

In the following procedure, the urban target described in the previous lesson will be compared to a forest target.

Details Forest target: HH=0.140 (-8.53dB) HV=0.054 (-12.67 dB) VV=0.142 (-8.47 dB) Nb. pixels = 44

R: HH, G:HV, B:VV Target in red: Urban, in blue: Forest R:HH, G:HV, B:VV

Figure 75. Targets used in input for the PSPOLSYNC algorithm.

To open the PSPOLSYNC algorithm 1. On the Tools menu, click Algorithm Librarian.


3. In the Find what box, type PSPOLSYNC, and then click Find Next.

4. Double-click the PSPOLSYNC algorithm. The PSPOLSYNC Module Control Panel window appears.


To set up the parameter for PSPOLSYNC 1. Under Input Ports, expand the Input: Input polarimetric SAR image


2. Click Browse, open the ~\SAR_Training\Radar\Flevoland_RS2_SLC\FQ29_20100507 folder, select the FQ29_20100507sig_PSBOXCAR_7.pix file, and then select each raster for this file.

3. Under Input Ports, expand the InputBitmap1: First input target layer.

4. Click Browse, open the ~\SAR_Training\Radar\Flevoland_RS2_SLC\FQ29_20100507 folder, and then select the FQ29_20100507_mod6_urban1.pix bitmap file.

5. Under Input Ports, expand the InputBitmap2: Second input target layer.

6. Click Browse, select the FQ29_20100507_mod6_mod6_forest.pix bitmap file.

7. Under Output Ports, expand the Output: Output synthesized detected SAR image entry, select Untitled.pix, and then click Browse. The File Selector window appears.


9. In the File Name box, type FQ29_20100507sig_PSBOXCAR_7_PSPOLSYNC_t1.pix.

10. Click Save. The path and file name are updated in the PSPOLSYNC Module Control Panel window.

11. Click Run. The PSPOLSYNC analysis runs and the results appear in the Focus viewer.

To view the metadata 1. In Focus, click the Files tab.

2. Right click the FQ29_20100507sig_PSBOXCAR_7_PSPOLSYNC_t1.pix file, and then click Properties. The File Properties windows appears.

3. Click the MetaData tab. The metadata shows that the maximum contrast between the two targets is obtained with the following configuration: Optimum Contrast Receive Ellipticity: 26.4° Optimum Contrast Receive Orientation: 7.5° Optimum Contrast Transmit Ellipticity: 16.0° Optimum Contrast Transmit Orientation: 11.0°.

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Details Red: Target 1, urban 0.464 (-3.33dB) Blue: Target 2, forest 0.054 (-12.7dB)

Figure 76. Synthesized backscatter image maximizing the contrast between the two targets

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Created a synthesized backscatter image to maximize the contrast between two targets

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SAR Processing With Geomatica v2014

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