Rs lect 2day_2

Dr.-Ing. Görres Grenzdörffer Remote Sensing

Digital image processing and MS-classification 1

Universität Rostock, Professur für Geodäsie und Geoinformatik X 2006

Dr.-Ing. Görres Grenzdörffer

Basics and Applications ofRemote SensingBasics and Applications ofRemote Sensing

Universität Rostock, Professur für Geodäsie und Geoinformatik

Schedule Lectures

• Remote Sensing Basics (1st Day)• Examples of modern airborne and spaceborne remote sensing• The EM-spectrum• Reflectance properties of different objects• Spaceborne sensors

• Satellite Remote Sensing (1st Day)• Resolution• Examples• SRTM• Change Detection

• Airborne Remote Sensing (2nd Day)• (digital) photography• Lidar• Examples

• Digital image Processing (2nd Day)• Pixels and mixels• Spectral bands / low level image operations• MS-classification• Object oriented classification




Digital image processing and MS-classification

Dr.-Ing. Görres Grenzdörffer


Outline

• What is an image coordinate system ?

• What is resolution ?• Geometrical, spectral, radiometrical, temporal

• What are low level image enhancements and how do they work ?

• How do i geocode a satellite image ?

• How does a multispectral classification work ?




Digital Remote Sensing

(Europe from the human perspective and the computer perspective)1 2 2 1 45 32 4 56 7 22 21 213 220 34 5633 22 33 123 231 160 165 180 210 2 34 43 120 87 3398 245 12 87 33 22 33 123 231 160 45 32 4 56 745 32 43 56 7 43 55 243 54 8 21 78 32 76 3232 4 56 7 22 123 231 160 45 22 33 123 231 87 345 32 43 56 7 43 55 243 54 43 55 243 54 8 43231 160 165 180 210 2 34 43 2 2 61 45 32 160 45123 33 22 33 123 231 160 165 170 240 2 34 43 120 8733 55 243 54 22 33 123 231 160 22 33 123 231 160 4532 4 56 7 45 32 43 56 77 47 55 243 54 8 2178 32 76 32 32 4 56 7 22 123 231 160 45 22 33123 231 87 3 45 32 43 56 7 43 55 87 33 22 39173 139 160 45 32 4 56 7 45 32 43 56 7 43 55243 54 8 21 78 43 178 77 47 55 243 54 8 21 7832 76 32 32 4 56 7 22 160 165 170 240 2 34 43120 87 33 55 243 54 22 33 123 231 240 195 175 240 234 43 120 87 33 55 243 54 22 33 123 231 67 7 4532 43 56 77 47 55 243 54 8 21 78 32 76 32 324 56 7 22 123 231 160 45 22 33 123 231 87 3 14943 56 77 47 55 243 54 8 21 78 32 76 32 32 456 7 22 123 231 160 45 22 33 123 231 87 3 45 3243 56 7 43 55 87 33 22 39 173 139 160 45 32 456 7 45 32 43 56 7 43 55 243 54 8 21 78 4378 44 87 217 209 32 43 56 77 47 55 243 54 8 21


(0,0)

DN(x,y)

X

YRows

Columns

Two dimensional image matrix of a raster

Also satellite images are made up of individual elements (pixels)that are arranged in a grid of rows and columns




Resolution of digital data - Pixel and Mixel

A Pixel (Picture Element) is the smallest element of an Image (IFOV). The geometric Resolution of an image is generally determined by the size of a pixel on the ground,e.g. 10 m on a SPOT scene. Similarly the expression ground sampling distance (GSD)is used.

A Mixel is a pixel, in which different object signatures, e.g. (house and surrounding)are represented. Mixel cause different problems in the classification procedure. The Proportion of mixels depends upon the geometric resolution and the spatial distribution the objects.

A B C

A C


Resolution

•• SpectralSpectral: portion of em spectrum imaged

•• TemporalTemporal: how often an area is imaged (change detection)

•• SpatialSpatial: smallest ground feature imaging system can detect (pixel

resolution)

•• RadiometricRadiometric: how many bits sensor uses to represent pixel

values




Spatial Resolution

• The ability to distinguish between two closely spaced objects in an image.

• Minimum distance between two objects that can be resolved by thesensor.

5 m5 m 30 m30 m


Spatial Resolution and Contrast

• Resolution of Landsat-7 ETM+• 30 meters (multispectral) 15 meters (panchromatic)

• Linear features as narrow as a few meters can be seen on images if their reflectance contrasts highly with their surroundings.

• Features much wider than 30 meters may not be visible if they have a very low contrast with their surroundings




Spectral Resolution

• Number and dimension of specific wavelengths of the electro-magnetic spectrum which the sensor collects

• High spectral resolution hyperspectral - many small-width bands• Low spectral resolution - panchromatic - one large (wide) band


Radiometric Resolution

• Sensitivity of sensor to differences in signal strength (detection of differences in brightness of objects and features)

8 8 bitbit = 2= 288 (0 (0 –– 255 255 valuesvalues))10 10 bitbit = 2= 21010 (0 (0 –– 1023 1023 valuesvalues))12 12 bitbit = 2= 21212 (0 (0 –– 4095 4095 valuesvalues))16 16 bitbit = 2= 21616 (0 (0 –– 65,535 65,535 valuesvalues))

8 8 bitbit = 1 = 1 bytebyte




Temporal Resolution

• How often is your target imaged?• Change detection…

May provide a larger advances over a single highresolution spatial or spectral image!


Temporal Resolution II

• The revisit period of a satellite sensor is usually several days. Therefore the absolute temporal resolution of a remote sensing system to image the exact same area at the same viewing angle a second time is equal to this period.

• But, because of some degree of overlap in the imaging swaths of adjacent orbits for most satellites and the increase in this overlap with increasing latitude, some areas of the Earth tend to be re-imaged more frequently.

• Also, some satellite systems are able to point their sensors to image the same area between different satellite paths separated by periods from one to five days.

• Thus, the actual temporal resolution of a sensor depends on a variety of factors, including the satellite/sensor capabilities, the swath overlap, and latitude.




Simple gray value manipulations - low level image processing

x Contrast- and brightnessHistogram (reduction, amplification)logarithmiclinear stretchindividual stretch

x Equidensitesdensity slicing of(single channel images (thermal images, ratio images))

Def.: The look-up table represents the relation between the grey values and the colour values on the computer screen, without changing the original imagevalues.

Histograma Averagea Mediana Variance

Freq

uenc

ygrey values

0 255min maxmedianaverage


Landsat TM Channel 4 Rostock with histogram

2550min

MedianmaxAverage

Modal value

Freq

uenc

y

n-Pixel




Possibilities of histogram manipulation

Linear

nonlinear

partlylinear

00

255

255Orginal gray values

Ste

tche

dgr

ay v

alue

s


Rostock, 30.8.1999 Landsat 7: Channel 2

Histogram manipulation II

Original

2-times standard dev.

Orginal

stretched




Orginal histogram After Histogram equalisation

Pixel at theFringe becomecompressedcontrast loss

Fringe

Peak

Pixel at thePeaksbecomebroadend -Contrast gain

{

Histogram Equalization


Histogram manipulations

Orginal lineare shift50 DN

linear Multi-plication 1.4

tw. lineare stret-ching 2. Std.

Histogram-equalisation




Multispectral satellite scene Landsat TM 7

Rostock30.08.1999

21 3

4 5 7


Color composites

3 - 2 - 1(= RGB) 4 - 3 - 2

(= CIR) 5 - 4 - 34 - 7 - 2




Correlation matrix Landsat TM 7 - scene Rostock

Cha

nnel

3

Channel 4

Cha

nnel

1

Channel 2

Band 1 Band 2 Band 3 Band 4 Band 5 Band 7Band 1 1.00 0.96 0.91 0.18 0.65 0.81Band 2 1.00 0.97 0.37 0.80 0.89Band 3 1.00 0.33 0.82 0.90Band 4 1.00 0.72 0.46Band 5 1.00 0.92Band 7 1.00


Normalized Difference Vegetation Index

Hottest colours -pinks and reds show the most vigorous plantgrowthLandsat 7 data sample

NDVI = (NIR - red) / (NIR + red)




Quantifying impervious areas in our cities

Simple analysis based on NDVI (NIR-red/NIR+red) then thresholded

Impervious /(Water)

Green space

Jimma, EthiopiaLandsat TM, 22.11.2000


Georectification- Geocoding

Relative georectification between two images (image to image)

• stereopairs• images with different sensors• multi temporal images

Cartographic geocoding: Transformation of satellite image onto given map projection(Map to image)

Necessary for • Combination / analysis with ancillary data from a GIS• the generation of satellite maps

Necessary for orientation of




Geometric Correction

• Raw satellite images contain geometric distortions due to e.g.

• Variations in altitude, attitude, and velocity of sensor platform

• Panoramic distortion

• Earth curvature

• Atmospheric refraction

• Relief displacement

• Non-linearity's in the sweep of a sensor’s IFOV


Geocoding procedure

Definition of GCP‘s(Map, GPS,

reference image)

Calculation of the transformation matrix

Assessment of residuals

Resamplingof the image

Requirements for ground control points (GCP)• Evenly distributed over the whole image• Evenly distributed over all elevations• Clear visibility in the satellite image (scale dependent)• GCP‘s at permanent locations (street junctions)• Min. of 4 ground control points for 1. order rectification• Geometric accuracy of GCP‘s = 10 times better than

pixel size




Geometric Transformations

Polynomial transformation

Affine transformation

Projective Transformation


Geocoding- direct and indirect methods

abT

ab

T

Problem: Holes in the output image requires special filling algorithms

Direct: Transformation from the input image to the output image

Indirect: Transformation from the output image to the input image

a b

a b

Commonly used approach




Resampling

abT

A B

Def.: Through the resampling process the grey values of the original image Bwill be interpolated to the output image A.

Nearest neighbor: Assignment of gray value of nearest neighbour

Bilinear interpolation: linear interpolation of 4 neighbours

Bicubic interpolation (Lagrange): Interpolation of 16 neighbours

Interpolation strategies:


Accuracy assessment

x Root mean square (RMS) errorThe RMS is calculated through the inverse computation of the transformation matrix.The RMS describes the distance between the original position of a GCP and the computedposition as a result of the transformation matrix.

The RMS is usually given in pixel values

1 pixel error tolerance

GCP Pixel

GCP pixel

2 pixel errorTolerance (radius)

RMS of 1 in Landsat 5, 7 scene = deviation of 30 m




Concept of supervised multispectral classification

WaterForest

GrasslandIndustry

Urban

Desert

1276522023180

C5

C4

C3

C2

C1

Channel

23

45

1

Multi spectral image data Training phase

Pixel (2,5)

Classification phase Result(Grey values replacedby land use classes)

(Comparison ofunknown pixel withthe spectral signaturesof the training sites)

(Selection of trainingsites with typicalspectral properties(signatures))

(5 Channels = DN/Pixel)


Landsat TM 5 - Rostock Area (Baltic Coast)

Landsat TM, 31.07.95




Multi- spectral land use classification Rostock 1989/1995

Arable LandSettlements Green SpaceWaterbodies Forest

Beach

3 km3 km

1989 1995


Change of land use 1989-1995 (Rostock)

Additional Settlements

No change

New green space

Source:

Multispektral Classification of Landsat-TM12.06.1989 und 13.07.1995Diploma Thesis B. Winter, 1996




Multispectral classification

Pre-processing

Definition of land use classes

Definition of training sites

unsupervised classification

supervisedclassification

Ancillary data

Classification results

Post Processing

Accuracy assessment

Maps ReportsData


Training sites

Prerequisites for training sites

Location: high visibility in maps and the satellite image, accessible for ground control

Number: several (min. 5-10), evenly distributed over the imageSize: For statistics min 10* (n+1) Pixel (n=number of channels)Homogeneity:

1. Only pixel of one land use class per training site

2. Distribution of pixels: unimodal and normally distributed

Representativity: For land use classes which are not unimodaland normally distributed the land use classeshave be split up into normally distributes sub classes

Seperability: Reduction of the multi dimensional object space by Jeffrey-Matusita-Distance valuesGray values 2550

AB




Minimum distance classification

Centroid definition through averaging of the grey values over all multi spectral channels of the training sites

AdvanDayss- All pixel are classified- Simple method

DisadvanDayss- Classes are different in size- No “unclassified” class- Erroneous classified pixel

200

150

100

50

50 100 150 200

K1

K2

K3K4

Band 1

Band 2


Accuracy – sources of error

AccuracyAccuracyProportion of correctly classified pixel in comparisonTo reference map / training sites (non site specific)Proportion of correctly classified pixel at a validationsite (site specific)

Sources of error (selection)Sources of error (selection)Wrong selection of land use classes vs. spectralclassesComplex interactions between objects and landscape structureSensor resolutionPre processingClassification procedure

Channel 1

Channel 2

1. Proportion of the training site,which does not belong to the classbut is classified as such

2. Proportion of the training sitewhich belongs to the classbut is not classified as such

Object cluster

Validation of the classification accuracy with additional training sites whichwere not used in the classification procedure




Error matrix

Urban Forest Grassland Desert Wasser

Water

Urban

Forest

Grassland

Desert

85,6

81,8

83,9

97,5

98,5

7,3 4,8 2,0 0,3

0,80,110,86,5

5,7 9,6 0,4 0,4

0,0

0,0

0,0

0,5

1,0

0,30,7

1,5

Average accuracy 447,3/5 = 89,46 %

Input image

Channel 1

Channel 2

Object cluster A

Analysis of error matrices• How big is the over all proportion of correctly classified pixel ?• How big is the proportion of correctly classified pixel of a single class ?

• Are single classes over or under represented ?• Are the errors distributed evenly ?

Object cluster B Trai

ning

site

s


Post processing of a classification

Filtering Visual post processing

Cartographic output

LegendFrame, scale bar, north arrowStatisticsReportsOverlay of GIS data(Names, thematic information …)

The land use of greater Atlanta

N

Atlanta

Forest

Downtown

Housing

Grassland

Industrial / Commercial

0 1 2 3 4km




Information content of pixels and objects

• Color• (Texture)

• Color statistic• Form• Size• Texture• Context

Pixel Objects


Hierarchical (multi scale) segmentation

Goal:• Generation of objects

at different scalelevels

• fine• medium• coarse

Objects of different scalesmay be classified within a single project




Scale Factor

Scale Factor 10 Scale Factor 50 Scale Factor 200


Object hierarchy at a multi scale segmentation

Objects form a networkEvery object knows its

Super object

Neigbour object

Sub object




Object hierarchy in a multi scale segmentation

Every image analysis task has its specific scale


Combination of different data sources

Aerial images

Satellite images

Thematic raster data

Vektor data

Combined analysis of different data sourceswith different scales




Classification methods inE- Cognition

Multi spectral classification• Definition of training sites• Nearest Neighbor classification•„ Click and Classify“

Knowledge based classification• Fuzzy-Logic rules• Membership functions

and a combination of both methods


Workflow of an object oriented multi scale classification with E- Cognition

Raster- and Vector-data

Automatic generation of anobject hierarchy

Knowledge basedclassification

Resultclassified objects which maydirectly be incorporated in a GIS

Knowledge basedset of rules




Example Quickbird Scene 03.11.2007 – North of Hanoi

• Goals• Simple land cover classification with 5 – 8 classes• Demonstration of multiscale segmentation

• Classification schema

Only for classificationat coarser levels


Image Segmentation – Adoption of parameters to structure of landscape (~parcel structure)

Scale Factor = 10




Image Segmentation – Adoption of parameters to structure of landscape (~multi parcel structure)

Scale Factor = 40, compactness 0.6


Image Segmentation – Adoption of parameters to structure of landscape (~ village structure)





Image Segmentation – Adoption of parameters to structure of landscape (~ landscape structure)



Classification Rules

•Combination of unique spectral classes and fuzzy logic rules




Analysis of classification functions

• Due to the wide range of classification functions, a thorough visual analysis of the best classification options is necessary

Standard deviation of neigbourpixels of band 1


Classification Results with different scale factors

•Quickbird Scene 4.12.2006




Subset of Quickbird Scene (Band combination 4–3–2)


Classification Result Scale Factor 10






Problems of Classification

• Due to insufficient ground knowledge only broad spectral classeswere introduced

• Villages and urban land cover is spectrally difficult to determine• Urban land cover becomes bigger at larger scale factors, due to

the classification rules• Small plots, green backyards are classified as dense vegetation• „Wet“ Bare soil of rice fields may be classified as shallow, bright

water




Conclusions of object orientied classification

• Best tool for high resolution data• Ancillary GIS-data may be included for analysis• Do not use all of the possibilities of the software, because you will

never finish• There is no true or precise classification, it is always a question

of definition (classification schema, analysis rules, image resolution …) and scale

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