TECHNIQUES FORSIMULATING FLOOD HYDROGRAPHS ANDESTIMATING FLOOD
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Transcript of Flood detection hands-on tutorial - Earth Online -...
Center for Satellite Based Crisis Information (ZKI)German Aerospace Center (DLR) 1
Flood detection using TerraSAR-X dataHands-on tutorial
André Twele and Sandro MartinisGerman Remote Sensing Data Center (DFD)German Aerospace Center (DLR)
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
André Twele, Sandro Martinis29/10/2009
3Center for Satellite Based Crisis Information (ZKI)
German Aerospace Center (DLR)
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
André Twele, Sandro Martinis29/10/2009
4Center for Satellite Based Crisis Information (ZKI)
German Aerospace Center (DLR)
Methods for flood detection
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
André Twele, Sandro Martinis29/10/2009
5Center for Satellite Based Crisis Information (ZKI)
German Aerospace Center (DLR)
Methods for flood detection
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
André Twele, Sandro Martinis29/10/2009
6Center for Satellite Based Crisis Information (ZKI)
German Aerospace Center (DLR)
Methods for flood detection
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
André Twele, Sandro Martinis29/10/2009
7Center for Satellite Based Crisis Information (ZKI)
German Aerospace Center (DLR)
Methods for flood detection
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
André Twele, Sandro Martinis29/10/2009
8Center for Satellite Based Crisis Information (ZKI)
German Aerospace Center (DLR)
Methods for flood detection
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)…
André Twele, Sandro Martinis29/10/2009
9Center for Satellite Based Crisis Information (ZKI)
German Aerospace Center (DLR)
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
André Twele, Sandro Martinis29/10/2009
10Center for Satellite Based Crisis Information (ZKI)
German Aerospace Center (DLR)
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.
André Twele, Sandro Martinis29/10/2009
11Center for Satellite Based Crisis Information (ZKI)
German Aerospace Center (DLR)
Automatic split-basedthresholding procedure
Image splitting
Split selection
Sub-histogram modeling
Global threshold computation
Split-threshold derivation by„minimum error thresholding“
Thresholding
Threshold
Non-flood
Water
SW-England: TS-X SM 4 (25/07/2007)
100 125
Automatic Flood Detection tool (AFD) in ENVI/IDL
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.
SW-England: TS-X SM 4 (25/07/2007)
André Twele, Sandro Martinis29/10/2009
12Center for Satellite Based Crisis Information (ZKI)
German Aerospace Center (DLR)
Automatic split-basedthresholding procedure
Image splitting
Split selection
Sub-histogram modeling
Global threshold computation
Split-threshold derivation by„minimum error thresholding“
Thresholding
Automatic Flood Detection tool (AFD) in ENVI/IDL
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.
André Twele, Sandro Martinis29/10/2009
13Center for Satellite Based Crisis Information (ZKI)
German Aerospace Center (DLR)
Automatic Flood Detection tool (AFD) in ENVI/IDL
(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
ion
in re
l. to
cyc
le 1
[%]
André Twele, Sandro Martinis29/10/2009
14Center for Satellite Based Crisis Information (ZKI)
German Aerospace Center (DLR)
Automatic Flood Detection tool (AFD) in ENVI/IDL
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
André Twele, Sandro Martinis29/10/2009
15Center for Satellite Based Crisis Information (ZKI)
German Aerospace Center (DLR)
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
André Twele, Sandro Martinis29/10/2009
16Center for Satellite Based Crisis Information (ZKI)
German Aerospace Center (DLR)
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.
André Twele, Sandro Martinis29/10/2009
17Center for Satellite Based Crisis Information (ZKI)
German Aerospace Center (DLR)
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
André Twele, Sandro Martinis29/10/2009
18Center for Satellite Based Crisis Information (ZKI)
German Aerospace Center (DLR)
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.
André Twele, Sandro Martinis29/10/2009
19Center for Satellite Based Crisis Information (ZKI)
German Aerospace Center (DLR)
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
André Twele, Sandro Martinis29/10/2009
20Center for Satellite Based Crisis Information (ZKI)
German Aerospace Center (DLR)
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.
André Twele, Sandro Martinis29/10/2009
21Center for Satellite Based Crisis Information (ZKI)
German Aerospace Center (DLR)
Data pre-processing in ENVI
Task: Diplay SAR data in ENVIFile Open Image File filename.tifDouble-click on „Band 1“
André Twele, Sandro Martinis29/10/2009
22Center for Satellite Based Crisis Information (ZKI)
German Aerospace Center (DLR)
Data pre-processing in ENVI
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.
André Twele, Sandro Martinis29/10/2009
23Center for Satellite Based Crisis Information (ZKI)
German Aerospace Center (DLR)
Data pre-processing in ENVI
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
André Twele, Sandro Martinis29/10/2009
24Center for Satellite Based Crisis Information (ZKI)
German Aerospace Center (DLR)
Data pre-processing in ENVI
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
André Twele, Sandro Martinis29/10/2009
25Center for Satellite Based Crisis Information (ZKI)
German Aerospace Center (DLR)
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
André Twele, Sandro Martinis29/10/2009
26Center for Satellite Based Crisis Information (ZKI)
German Aerospace Center (DLR)
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
Automatic Flood Detection tool (AFD) in ENVI/IDL
André Twele, Sandro Martinis29/10/2009
27Center for Satellite Based Crisis Information (ZKI)
German Aerospace Center (DLR)
Automatic Flood Detection tool (AFD) in ENVI/IDL
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
André Twele, Sandro Martinis29/10/2009
28Center for Satellite Based Crisis Information (ZKI)
German Aerospace Center (DLR)
Automatic Flood Detection tool (AFD) in ENVI/IDL
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!
1
2
3
André Twele, Sandro Martinis29/10/2009
29Center for Satellite Based Crisis Information (ZKI)
German Aerospace Center (DLR)
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?
André Twele, Sandro Martinis29/10/2009
30Center for Satellite Based Crisis Information (ZKI)
German Aerospace Center (DLR)
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
André Twele, Sandro Martinis29/10/2009
31Center for Satellite Based Crisis Information (ZKI)
German Aerospace Center (DLR)
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)
André Twele, Sandro Martinis29/10/2009
32Center for Satellite Based Crisis Information (ZKI)
German Aerospace Center (DLR)
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
André Twele, Sandro Martinis29/10/2009
33Center for Satellite Based Crisis Information (ZKI)
German Aerospace Center (DLR)
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.
André Twele, Sandro Martinis29/10/2009
34Center for Satellite Based Crisis Information (ZKI)
German Aerospace Center (DLR)
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
André Twele, Sandro Martinis29/10/2009
35Center for Satellite Based Crisis Information (ZKI)
German Aerospace Center (DLR)
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.
André Twele, Sandro Martinis29/10/2009
36Center for Satellite Based Crisis Information (ZKI)
German Aerospace Center (DLR)
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
André Twele, Sandro Martinis29/10/2009
37Center for Satellite Based Crisis Information (ZKI)
German Aerospace Center (DLR)
flood normal water level
Flood detection at regional scale
André Twele, Sandro Martinis29/10/2009
38Center for Satellite Based Crisis Information (ZKI)
German Aerospace Center (DLR)
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
Data pre-processing in ENVI
André Twele, Sandro Martinis29/10/2009
39Center for Satellite Based Crisis Information (ZKI)
German Aerospace Center (DLR)
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!
André Twele, Sandro Martinis29/10/2009
40Center for Satellite Based Crisis Information (ZKI)
German Aerospace Center (DLR)
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“
Segment based water mask extraction in Definiens
Selectable processing groups
Process parameters and description
Data/classification viewer
André Twele, Sandro Martinis29/10/2009
41Center for Satellite Based Crisis Information (ZKI)
German Aerospace Center (DLR)
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.
Segment based water mask extraction in Definiens
André Twele, Sandro Martinis29/10/2009
42Center for Satellite Based Crisis Information (ZKI)
German Aerospace Center (DLR)
Selection of processing modulesNote: Not all processing actions are required! Some are optional, others experimental.
Only select the following processing modules:
Segment based water mask extraction in Definiens
Note: If classification results are notsatisfactory, the action „Reset“ may be used to return to the initially segmented image!
André Twele, Sandro Martinis29/10/2009
43Center for Satellite Based Crisis Information (ZKI)
German Aerospace Center (DLR)
Selection of processing modules
Segment based water mask extraction in Definiens
Selected processing actions
Unselected processing action
Pro
cess
ing
orde
r
André Twele, Sandro Martinis29/10/2009
44Center for Satellite Based Crisis Information (ZKI)
German Aerospace Center (DLR)
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.
Segment based water mask extraction in Definiens
André Twele, Sandro Martinis29/10/2009
45Center for Satellite Based Crisis Information (ZKI)
German Aerospace Center (DLR)
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)
Segment based water mask extraction in Definiens
André Twele, Sandro Martinis29/10/2009
46Center for Satellite Based Crisis Information (ZKI)
German Aerospace Center (DLR)
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
Segment based water mask extraction in Definiens
make sure button is selected
André Twele, Sandro Martinis29/10/2009
47Center for Satellite Based Crisis Information (ZKI)
German Aerospace Center (DLR)
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).
Segment based water mask extraction in Definiens
If you are not content with the result, run Reset Delete classification and repeatthe classification by the use of a new threshold value.
André Twele, Sandro Martinis29/10/2009
48Center for Satellite Based Crisis Information (ZKI)
German Aerospace Center (DLR)
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.
Segment based water mask extraction in Definiens
André Twele, Sandro Martinis29/10/2009
49Center for Satellite Based Crisis Information (ZKI)
German Aerospace Center (DLR)
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.
Segment based water mask extraction in Definiens
André Twele, Sandro Martinis29/10/2009
50Center for Satellite Based Crisis Information (ZKI)
German Aerospace Center (DLR)
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).
Segment based water mask extraction in Definiens
André Twele, Sandro Martinis29/10/2009
51Center for Satellite Based Crisis Information (ZKI)
German Aerospace Center (DLR)
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.
Segment based water mask extraction in Definiens
André Twele, Sandro Martinis29/10/2009
52Center for Satellite Based Crisis Information (ZKI)
German Aerospace Center (DLR)
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).
Segment based water mask extraction in Definiens
André Twele, Sandro Martinis29/10/2009
53Center for Satellite Based Crisis Information (ZKI)
German Aerospace Center (DLR)
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.
Segment based water mask extraction in Definiens
André Twele, Sandro Martinis29/10/2009
54Center for Satellite Based Crisis Information (ZKI)
German Aerospace Center (DLR)
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