Flood detection hands-on tutorial - Earth Online -...

André Twele, Sandro Martinis29/10/2009

2Center for Satellite Based Crisis Information (ZKI)

German Aerospace Center (DLR)

Hands-on training and tutorial

Flood classification using TerraSAR-X data

Test cases: Namibia floods, March/April 2009

Nepal floods, August 2008USA/Newport floods, March/April 2008




Overview

Hands-on training and tutorial:

Methods for flood detection

Tools used in this hands-on training

Data pre-processing

Pixel-based water mask generation using ENVI/IDL

Segment-based water mask generation using Definiens





ThresholdingMost popular image processing method Assigning all elements of a SAR image < T to class “flood”Most common: Manual trial-and-error procedures (e.g. using histogram minima) lack of objectivity and traceabilityIn histograms of large SAR data, local gray level differences may not be distinguished due to small class a priori probabilities Noisy classification results





Active contour models (snakes)Dynamic curvilinear contour to search the image space until it settles on feature boundaries.

Snake algorithm courtesy of Dr. David Mason (Univ. of Reading), Matthew S. Horritt and JRC-IES.

Horritt, M.S. (1999): A statistical active contour model for SAR image segmentation. Image Vis. Comput., 17, 213-224.

In most cases: manual initialization of each water region of homogeneous image statistics expanding to delineate a region containing similar spectral and texture information.Mapping of homogeneous rough water surfaces.Parameter-adjustment by trial-and-error.Time consuming





Object based classificationPartitioning of an image into homogeneous disjoined regions according to user defined homogeneity criteria. Classification by spectral, texture and shape parameters as well as contextual information.

Object properties:

• Mean brightness: 12.3

• Standard deviation: 2.5

• Size: 12,800 m²

• GLCM Entropy: 4.1

Segmented TerraSAR-X StripMap scene





Object based classificationIntegration of multi-scale information into the labeling process.Classification accuracy usually higher than pixel-based methods.Segmentation is relatively slow, high memory requirements.

Multi-scale segmented TerraSAR-X StripMap scene

Increasing scale





Various other methodsMarkov Image ModellingNeural NetworksSupport Vector MachinesWavelets…

Auxiliary dataNormal water levelPrevious flood extentDigital elevation models (detection of flooded vegetation, removing of look-alike areas at higher elevation)…




Tools used in this exercise

Automatic flood detection tool (AFD) in ENVI/IDLautomaticpixel basedcontext-based post-classification

Rapid mapping of flooding (RaMaFlood) in Definiens Architectobject basedautomatic/semi-automaticinteractive




Automatic Flood Detection tool (AFD) in ENVI/IDLAutomatic split-based

thresholding procedure

Image splitting

Split selection

Sub-histogram modeling

Global threshold computation

Split-threshold derivation by„minimum error thresholding“

Thresholding

SW-England: TS-X SM 4 (25/07/2007)

Martinis, S., Twele, A., and Voigt, S.: Towards operational near real-time flood detection using a split-based automatic thresholding procedure on high resolution TerraSAR-X data, Nat. Haz. Earth Syst. Sci., 9, 303-314, 2009.




Automatic split-basedthresholding procedure

Image splitting

Split selection




Thresholding

Threshold

Non-flood

Water


100 125

Automatic Flood Detection tool (AFD) in ENVI/IDL






Automatic split-basedthresholding procedure

Image splitting

Split selection




Thresholding







(i-1, j-1)

(i+1, j+1)(i-1, j+1)

(i-1, j)

(i, j+1)

(i, j) (i+1, j)

(i+1, j-1)(i, j-1)

Post-classificationmethods

Markov RandomField (MRF)

Definition of a MMU

Fuzzy regiongrowing

Iterative context based classificationby the use of Maximum A Posteriori estimation over a local neighborhood

Objects with an area (in pixel) lowera Minimum Mapping Unit (MMU) areremoved from the water mask

Region growing starting from initiallyclassified water objects untilpredefined stopping conditions

0

0.2

0.4

0.6

0.8

1

1 2 3 4 5 6 7 8 9 10

Number of cycles

Ener

gy m

inim

isat

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in re

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[%]





Image splitting by defined chess-size

Number of splits

Split-selection by combining local and global statistics

Independent Split Based Analysis: Local thresholding of the sub-histogram of each selected split

Joint Split Based Analysis: Global thresholding of one histogram built from the sub-histograms of selected splits

Combination of the local thresholds derived by ISBA

Change out directory. Default: location of input dataSelf explanatory

Degree of homogeneity

Hard threshold Region growing starting from flood areas ≤hard threshold. Fuzzy: Export of flood maskwith probability values. Defuzzy: Export of defuzzified flood mask.

Elimination of small flood areas




Object based mapping with RaMaFlood in Definiens

User interface of Definiens ArchitectSelection of required actionsCan be accomplished automatically or interactivelyMain emphasis: Triscale-Segmentation and Triscale Initial Classification




Object based mapping with RaMaFlood in DefiniensTransfer of the thresholding results of L2 and L3 to L1

L1 L2 L3

Restriction of the application of the threshold at L1 to flood neighborhood objects

Advantage: Combining the image information at different scales Homogenous water surfaces with fine details in a local neighborhood of the initially found water objects.




Object based mapping with RaMaFlood in Definiens

User interface of Definiens ArchitectSelection of required actionsCan be accomplished automatically or interactivelyMost important actions: Triscale-Segmentation and Triscale Initial ClassificationVarious post-processing methods




TerraSAR-X data structure

Information on orbit type(MGD/GEC/EEC), resolution variant(SE/RE), beam mode (SL/SM/SC), polarization (S/D) and acquisition date

Image data in GeoTIFF format

Quicklooks of image in different resolutions. BROWSE.tif can be displayed with anyimage viewer.

MAP_PLOT.png: Map plot of the geolocationof the image and addiditonal acquisitionparameters.




EXERCISE 1

Derive the watermask of TerraSAR-X Spotlight data during flooding in Namibia 2009 using the

AFD-tool

Data:...\Data\Flood detection\Namibia_SL\...\IMAGE_HH_SRA_spot_077.tif




Flood detection at local scale

Flooding of Caprivi, Namibia/AngolaHeavy rains in Angola and parts of Nambia's Western Province led to flooding in Namibia's north and north-eastern parts. On March 18, 2009 emergency was declared in theconcerned areas, where floods havecaused large-scale destruction to homes, schools, health facilities, mahangu and maize fields and infrastructure.




Data pre-processing in ENVI

Task: Diplay SAR data in ENVIFile Open Image File filename.tifDouble-click on „Band 1“





Task: Select image subset if necessarysmaller filesize, processing less time-consumingSubset should contain both land and water areasBasic Tools Region Of Interest ROI toolROI Tool ROI_Type RectangleSelect image subset by drawing the rectangle thenright-click to create the region of interest (ROI) ROI Tool File Subset Data via ROIs select SAR data Select Input ROI in list Define Output Filename: filename_sub.tif

Note: When two identical subsets for two datasets need to be created (only neededin exercise 4), the ROI must be „transferred“ from one image to the other. See the next slide for instructions.





Task: Transfer/reconcile the ROI between two imagesRequired to subset two datasets to the same map extentPrerequisite: a ROI and subset for one image has already been created(see previous slide)Basic Tools Region Of Interest ROI tool Reconcile ROISelect ROI of previous image in listSet Values from Source/Destination FilesSpecify image from which the first ROIhas been drawn and press OKThe ROI should now be also visiblein the second image and can be usedto subset it (see previous slide)

Note: This task is only needed for Exercise 4





Task: Rescaling of TerraSAR-X data from 16bit to 8bit smaller filesize, processing less time-consuming no water-related information loss

All DNs > 255 are set to 255.All water features (which usually have DNs< 255) keep their original brightness value.Rescaled data are automatically named as filename_8bit.tif




Data pre-processing in ENVI (optional)

Filter Adaptive Enhanced LeeDefine InputSelect an appropriate window-size (e.g. „3“ or „5“)Define Output: filename_EL_window-size.tif

Task: Speckle filtering with Enhanced Lee filterless high-frequency noise, water features appear more homogenous, less„salt and pepper“-structures, particularly when performing pixel-basedclassifications




Task: Water mask extraction using DLR‘s AFD-tool in ENVI/IDL advantages: fast, fully automatic, optimal threshold is found in most cases disadvantages: only pixel-based - other non-water features with low

backscatter values may initially be wrongly classified as „water“ Several post-classification methods for improving the water mask

Location of AFD-tool in ENVI/IDL menu

Select SAR image to be classified






Image splitting by defined chess-size

Self explanatory

Selection of splits containing both flood and non-flood regions according to the defined parameter range

Independent Split Based Analysis: Local thresholding of the sub-histogram of each selected split

Joint Split Based Analysis: Global thresholding of one histogram built from the sub-histograms of selected splits

Combination of the local thresholds derived by ISBA to one global threshold by the mean or median algorithm

Change out directory. Default: location of input dataSelf explanatory

Degree of homogeneity

Hard threshold Region growing starting from flood areas ≤hard threshold. Fuzzy: Export of flood maskwith probability values. Defuzzy: Export of defuzzified flood mask.

Elimination of small flood areas





Task: Run AFD with 3 different postclassification methodsPostclassification No postclassification (labeling by a hard threshold)When the classification is finished, a watermask appears in the available band list. After displaying/inspecting it, close it by right clicking on its name („Close selected file“). Thenrename the default name of the output mask (Watermask_name_YYYYMMDD_unsup.tif) in Windows Explorer according to the method used!

Postclassification Fuzzy Region Growing Export mask type: Defuzzy Minimum mapping unit > 0 (e.g. 50)Please close the file after processing/display, then rename the default name of the outputmask!

Postclassification Markov Random Field (ICM) Neighborhood: 8-clique Maximum number of iterations: 2

Note: MRF is time consuming! The computational demand increases withincreasing iteration number and increasing neighborhood size.

Please close the file after processing/display, then rename the default name of the outputmask!

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2

3




Inspect results in ENVI

Task: Inspect classification results

display SAR image and watermasksside by side (4 separate viewers)

right click in one image and activate„link display“ and „geographic link“

left click in one image to toggle between two layers

Which postclassification method gives the best result? Are there any misclassified areas, i.e. „water“ classified as „no water“ orthe opposite? If yes, what kind of features are misclassified?




Post-classification in ENVI

Task: Post-classification enhancement of the best watermaskClumping: Used to clump adjacent similar classified areas together, i.e. add more spatial coherency. Example: Fill small “islands” in water areas.Classification Post Classification Clump Classes Select watermask Select class „Water“ Select appropriate „operator size“, e.g. 5/5 or 7/7 Compare results before/after clumping in two separate viewers

watermask before/after clumping




Post-classification in ENVI

Task: Post-classification enhancement of the best watermaskSieve: Used to remove isolated classified pixels. Example: Remove pixels wrongly classified as “water” which are located outside of larger water areas.Classification Post Classification Sieve Classes Select clump file Select class „Water“ Any groups of pixels smaller than “Group Min Threshold” will be removed from the class “Number of neighboring pixels” determines when neighbouring pixels are considered as a class group Compare results before/after sieving in two separate viewers

watermask before/after sieving

gray: groups of pixels removed from class “water” (group threshold = 65)




EXERCISE 2

Derive a watermask from TerraSAR-X ScanSARdata acquired during the floods in Namibia 2009

using the AFD-tool (see Exercise 1)

Data:...\Data\Flood detection\Namibia_SCR\...\IMAGE_HH_SRA_scan_014.tif




Flood detection at regional scale

Severe flooding in northern Namibia

Similar to the year before, severe floodsaffected northern Namibia due to heavylocal rains and inflow of water fromnorthern regions during late Februaryand March 2009. Due to the complexdrainage pattern in the region satelliteobservation is an important tool to provide an overview of the large areaflood situation, which is of very patchyand persistent character in the areasaffected. Analysis a sequence of satelliteimagery taken between 20th Februaryand 6th March over the northern Omusatiregion show that water levels were risingin the flood area and partly surpassedthe levels of March 2008.




EXERCISE 3 (optional)

Derive a watermask from TerraSAR-X Spotlight data acquired during floods in Nepal 2008 using

the AFD-tool (see Exercise 1)

Data:...\Data\Flood detection\Nepal_SL\...\IMAGE_HH_SRA_spot_079.tif

and...\Data\Flood detection\Nepal_SCR\...\IMAGE_HH_SRA_scan_008.tif




Flood detection at local and regional scale

Embankment breach: Floods in India/Nepal

On 18th August, 2008 the Koshi River breached an embankment causing flooding in the Sunsari district as well as further South in the Supaul district of Bihar which is situated 7 kilometres downstream in India. It also inundated the districts of Madhepura, Araria, Purnea and Saharsa. After the breach, theRiver Koshi, which separates India fromNepal, changed its course resulting in morethan 250,000 destroyed homes in 1,600 villages and a total of 3 million affectedpeople. Damaged roads and flooded railwaytracks have hampered relief efforts.




EXERCISE 4

Derive a floodmask of the Newport flood event in 2008 by the mapping of water extent during thedisaster and at normal water conditions using

RaMaFlood

Data:...\Data\Flood detection\Newport_SM_1\...\IMAGE_HH_SRA_strip_006.tif

and...\Data\Flood detection\Newport_SM_2\...\IMAGE_HH_SRA_strip_006.tif




flood normal water level

Flood detection at regional scale




Task: Repeat preprocessing steps described in Exercise 1 (page: 21-25)

Create spatial subset using the same ROI for both datasets which containsboth land and water areas

Rescaling to 8bit

Speckle filtering





Water mask extraction in Definiens Developer advantages: accuracy usually higher than pixel-based classification,

inclusion of object features/topology disadvantages: relatively slow, high memory requirements, larger scenes

must be processed in several subsets – otherwise segmentation might crash

Task: Create New WorkspaceDefiniens Developer menu: File New Workspace

Task: Create New ProjectDefiniens Developer menu: File New ProjectSelect image file (filename.tif): InsertDefine subset (optional): Subset SelectionSet „no data“ value: No Data Global No Data value = 0

Segment based water mask extraction in Definiens

Due to high processing demand, pleaseselect a subset of <4,000*4,000 pixels!




Task: Open ProjectSwitch to view 1Double-click on the project nameSwitch back to view 2

Task: Open Action LibraryLibrary Open Action LibrarySelect directory „RaMaFlood_x“


Selectable processing groups

Process parameters and description

Data/classification viewer




Selection of processing modulesTask: Open processing group by clicking on button or group nameSelect and add action

Note: Not all processing actions are required! Some are optional, othersexperimental. See recommendations on the following pages.





Selection of processing modulesNote: Not all processing actions are required! Some are optional, others experimental.

Only select the following processing modules:


Note: If classification results are notsatisfactory, the action „Reset“ may be used to return to the initially segmented image!




Selection of processing modules


Selected processing actions

Unselected processing action

Pro

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orde

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Processing steps

Make sure that view 2 is selected

Highlight an action, use green arrows to run the individual action(middle arrow) or execute the whole solution (right arrow)

Choose sensor type (currently, TerraSAR-X and Radarsat-2 are implemented)

Segmentation of the image in three hierarchical scale levels. Only first objectscale needs to be defined (default: 15), the two levels are automaticallycomputed. „Color“ can usually be left at the default value.





Processing steps

Result of segmentation can be displayed for each layer separately

Display options can be changed in the menu bar (e.g. show/hide outlines of segmentation)





Processing steps

Pre-classification using a user-defined threshold.Different options for threshold-definition (see also the description):

1. Change to view 4 and select segments with an elevated backscatter level thatstill belong to a water surface. Inspect „Image Object Information“ and use the valuedisplayed as „Layer 1 Mean“ as the threshold.

2. Change to view 4 and select Object features Layer Values Mean Layer 1. Choose the threshold according to the feature range


make sure button is selected




Processing steps

3. Automatically compute the threshold value by the use of Only apply on 8bit-images due to high computational demand!

4. Use AFD-tool to find optimal threshold (which is displayed at the end of the process).


If you are not content with the result, run Reset Delete classification and repeatthe classification by the use of a new threshold value.




Processing steps

In case not all potential water objects are captured, other objects with a slightlyhigher threshold and a common boarder to already classified objects can beadded through the actions „Classification of Neighbouring Objects - Part 1/2“See description for details.

This processing action is optional.





Processing steps

Use this processing action to remove small objects fully surrounded by water(i.e. small „islands“) leading to a water surface which is spatially more coherent.





Processing steps

Use this processing action to remove smaller objects classified as „water“ thathave no common boarder to other water objects from the classification. Theseare typically areas of radar shadow or other objects with low backscattervalues that are situated outside the main water surface. The slider is used to define the size (in m2) under which objects are being removed. The object sizeis displayed in View 4 (Image Object Information Shape Area).





Processing steps

Use this processing action to manually classify „non-water“ objects as „water“or the other way round. Click button to display additional editing tools.Use polygon selection button to select more than one image object at once, then right click to „Classify selection“.

Use the buttons to switch between the SAR data and the classificationresult Detect misclassified objects.





Processing steps

When executing the „Export TIF“ or Export SHP“ action, the water mask issaved in the directory of the original image data.

Name: Watermask_tif-L1_YYYYMMDD_sup2.tif orWatermask_shp-L1_YYYYMMDD_sup2.shp

Rename the water mask according to the imaging date (change YYYYMMDD).





Processing steps

Note: If classification results are not satisfactory, the action „Reset“ may beused to return to the initially segmented image (run “Delete Classification”)!If only “Delete classification” is run, the segmentation is kept intact.

See the description for further details.

End of processing – repeat this exercise for both datasets in order to derive thewater levels at normal water level conditions and during the flood situation.





Display of the floodmask in ENVI

Load the watermask of both dates in ENVIAvailable Band List dialog: RGB ColorCreate Color composite from the two masksNote: The two raster files must be of the same size.

Normal water level

Floodmask

Land areas at both dates

Water at date 2 and not at date 1

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