ISPRS Journal of Photogrammetry and Remote Sensing 104 (2015) 203–212

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ISPRS Journal of Photogrammetry and Remote Sensing

journal homepage: www.elsevier .com/ locate/ isprs jprs

A fully automated TerraSAR-X based flood service

http://dx.doi.org/10.1016/j.isprsjprs.2014.07.0140924-2716/� 2014 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.

⇑ Corresponding author. Tel.: +49 8153 28 3034.E-mail addresses: sandro.martinis@dlr.de (S. Martinis), jens.kersten@dlr.de

(J. Kersten), andre.twele@dlr.de (A. Twele).

Sandro Martinis ⇑, Jens Kersten, André TweleGerman Aerospace Center (DLR), German Remote Sensing Data Center (DFD), Muenchner Str. 20, 82234 Oberpfaffenhofen, Germany

a r t i c l e i n f o

Article history:Available online 7 September 2014

Keywords:SARRemote sensingFloodsDisasterClassificationFuzzy logic

a b s t r a c t

In this paper, a fully automated processing chain for near real-time flood detection using high resolutionTerraSAR-X Synthetic Aperture Radar (SAR) data is presented. The processing chain including SAR datapre-processing, computation and adaption of global auxiliary data, unsupervised initialization of the clas-sification as well as post-classification refinement by using a fuzzy logic-based approach is automaticallytriggered after satellite data delivery. The dissemination of flood maps resulting from this service is per-formed through an online service which can be activated on-demand for emergency response purposes(i.e., when a flood situation evolves). The classification methodology is based on previous work of theauthors but was substantially refined and extended for robustness and transferability to guarantee highclassification accuracy under different environmental conditions and sensor configurations. With respectto accuracy and computational effort, experiments performed on a data set of 175 different TerraSAR-Xscenes acquired during flooding all over the world with different sensor configurations confirm therobustness and effectiveness of the proposed flood mapping service. These promising results have beenfurther confirmed by means of an in-depth validation performed for three study sites in Germany,Thailand, and Albania/Montenegro.� 2014 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier

B.V. All rights reserved.

1. Introduction

Optical as well as radar satellite remote sensing have proven toprovide essential large-scale information on flood situations. Thenear-real time provision of detailed information on inundationextent and its spatio-temporal evolution is essential for supportingflood relief efforts. Due to the independence of microwave signalsfrom weather and illumination, radar based methods are particu-larly suitable for a systematic flood monitoring strategy.

Commonly, commercial and non-commercial entities providecrisis information extracted from remote sensing data in mapproducts upon request by, e.g. relief organizations or political deci-sion makers. The provision of EO data and value-adding servicesfor emergency management is typically organized via differentnational and international initiatives and response mechanismssuch as the International Charter Space and Major Disasters(http://www.disasterscharter.org) or the Copernicus EmergencyManagement Service (http://emergency.copernicus.eu). For a morecomprehensive list see Serpico et al. (2012). Even if the responsetimes of these organizations are encouraging, the time span

between data delivery and product dissemination could signifi-cantly be reduced in some situations by avoiding manual imageprocessing and thematic analysis as well as the generation ofcustomized maps by GIS experts.

Fortunately, the number of automatic image processing algo-rithms to derive flooding from SAR data has increased since thelaunch of the high resolution Synthetic Aperture Radar (SAR)satellite systems TerraSAR-X, Radarsat-2 and the Cosmo–SkyMedconstellation (CSK) for the timely provision of crisis informationduring inundation events (Martinis et al., 2009; Pulvirenti et al.,2012; Matgen et al., 2011; Schumann et al., 2010). Thesealgorithms have in common that they make use of automaticthresholding algorithms for the initialization of the classificationprocess: Schumann et al. (2010) and Pulvirenti et al. (2012) com-pute a threshold value from global gray level histograms of SARdata using Otsu’s method (Otsu, 1979) which derives a criterionmeasure to evaluate the between-class variance of water andnon-water areas. Matgen et al. (2011) perform thresholding bymodeling the flood class using a non-linear fitting algorithm underthe gamma distribution assumption. Martinis et al. (2009) presentan automatic tile-based thresholding approach which solves theflood detection problem in large size TerraSAR-X data even withsmall a priori class probabilities by applying the KI thresholdingapproach (Kittler and Illingworth, 1986) on selected image tiles,

204 S. Martinis et al. / ISPRS Journal of Photogrammetry and Remote Sensing 104 (2015) 203–212

which are likely to represent a bimodal distribution of the classesto be separated. Thresholding can be combined with various imageprocessing approaches proposed in the literature for improving theextraction of the inundation extent from SAR data, e.g. object-based (Martinis et al., 2009; Martinis and Twele, 2010; Masonet al., 2012; Pulvirenti et al., 2012), texture-based (Ahtonen et al.,2004; Pradhan et al., 2014; Zhang and Lu, 2005), region growing-based (Giustarini et al., 2013; Mason et al., 2010 and Masonet al., 2012; Matgen et al., 2011; Martinis et al., 2009), and fuzzylogic-based (Martinis and Twele, 2010; Pierdicca et al., 2008;Pulvirenti et al., 2011 and Pulvirenti et al., 2013) classification algo-rithms. A comprehensive literature review of water and flood map-ping approaches can be found in Martinis (2010).

Even if relevant crisis information can be extracted automati-cally, in most cases a certain amount of user interaction is neededfor data pre-processing, the collection and adaptation of auxiliarydata useful for classification refinement as well as the preparationand dissemination of the crisis information to end users.

Despite the increasing number of fully automated image pro-cessing algorithms, only few operational SAR-based flood servicesexist to date. As one of the first services, the Fast Access to Imageryfor Rapid Exploitation (FAIRE) service hosted on the EuropeanSpace Agency’s (ESA) Grid Processing on Demand (G-POD) systemprovides automatic SAR pre-processing and change detectioncapabilities which can be triggered on demand by a user via aweb-interface. The application is currently being extended withmapping capabilities which are based on a comparison of SAR dataacquired during flood situations with corresponding archive/refer-ence data acquired at normal water levels. Recently, a prototypeautomated processing algorithm for medium resolution surfacewater mapping based on systematic and global-scale ENVISATASAR acquisitions has been presented (Westerhoff et al., 2013).The proposed technique, which is embedded in an online service,classifies open water areas according to the probability of eachimage element to be covered by water according to 2-D histogramtraining data sets built from archive data in the incidence-back-scatter domain. Due to the failure of the ENVISAT satellite in April2012 the continuous monitoring is interrupted until the launch ofEuropean Space Agency’s Sentinel-1 C-Band SAR satellite. A furtherexample is the Fully Automatic Aqua Processing Service (FAAPS)which aims to develop a future operational service deliveringNRT flood extent maps generated from ESA satellite data. Themethodology is based on a priori knowledge about an area takenfrom a multi-temporal time series and digital elevation informa-tion (Schlaffer et al., 2012).

In this paper an on-demand flood mapping service based onhigh resolution TerraSAR-X data is presented. In comparison tomost other applications in flood disaster mapping the thematicflood processor is integrated into a fully automatic processingchain starting with the automatic download of the delivered SARdata and ending with the dissemination of the flood mappingresults via a web-interface. Therefore, no user input is needed forSAR data pre-processing, collecting and adapting auxiliary datauseful for the thematic refinement as well as disseminating the cri-sis information. It is expected that the service enhances the valueof remote sensing during flood management activities by minimiz-ing the time delay between data delivery and product dissemina-tion. The data produced by this service may also supportapplications in hydrology, where information about the floodextent is systematically assimilated into hydrologic and hydraulicmodels (e.g. Hostache et al., 2009; Dung et al., 2011). The core ofthe service is a tile-based thresholding procedure, which originallyhas been developed for extracting low backscattering open floodsurfaces in SAR amplitude data in a fully automatic and time-efficient manner (Martinis et al., 2009). In this contribution, thismethod is adapted to SAR data radiometrically calibrated to sigma

naught (r0, (dB)) and enhanced in robustness to work on dataacquired with various system parameters and under different envi-ronmental conditions. A fuzzy logic-based algorithm integratingSAR backscatter information and different globally available auxil-iary data sources into the classification process is used in combina-tion with a region growing approach for a refinement of theclassification accuracy.

The fully automated processing chain is described in detail. Itincludes the following steps: pre-processing of the TerraSAR-Xdata, computation and adaption of global auxiliary data (digitalelevation models, topographic slope information, and referencewater masks), unsupervised initialization of the classification,post-classification refinement and dissemination of the crisis infor-mation via a web-interface. The robustness and effectiveness of theproposed flood mapping service is demonstrated by applying thecomplete workflow (see Fig. 1) to flood events in Germany 2011,Thailand 2011, and Albania/Montenegro.

2. Methodology

According to Fig. 1, the workflow of the proposed service can bedivided into four main components: pre-processing, thematic anal-ysis, storage as well as dissemination of results. In Section 2.1 thepre-processing of TerraSAR-X imagery and global auxiliary dataused for the radiometric calibration of the input SAR data and forthe post-classification refinement is described. Section 2.2 presentsthe automatic thematic analysis of the pre-processed input SARdata using a tile-based thresholding procedure and a subsequentfuzzy logic-based post-classification step.

2.1. Pre-processing

2.1.1. Automated data download and reprojectionInput data for the proposed service are enhanced ellipsoid cor-

rected (EEC) TerraSAR-X amplitude imagery of different acquisitionmodes (Staring Spotlight, Spotlight, StripMap, ScanSAR, WideScanSAR), which are delivered via ftp server.

In order to ensure immediate data processing, the data down-load is triggered automatically after reception of the deliveryemail. After completion of the download to the local file systemthe data is unzipped and the corresponding file structure issearched for all relevant files, i.e., the SAR data, the metadata fileand optionally the Geocoded Incidence Angle mask (GIM). TheGIM is an add-on to the EEC product which provides informationabout the local incidence angle for each image element of the geo-coded SAR scene and about the presence of shadow and layoverareas (Infoterra, 2008). If the GIM does not exist initially, it is gen-erated automatically during the following pre-processing steps.

In order to ensure equivalent coordinate systems for all relevantdata products, the delivered TerraSAR-X images are reprojected togeographical coordinates (lat/lon) with respect to the WGS84system if required. This target system is also used for the globalauxiliary data products.

2.1.2. Adaption and computation of auxiliary dataTwo types of global auxiliary data are included into the process:

reference water masks and digital elevation models (DEMs). Atfirst, the SRTM water body mask (SWBD) (SWBD, 2005) for allareas between 54�S and 60�N with a resolution of 30 m as wellas the Global Raster Water Mask (MOD44W) (Carroll et al., 2009)at 250 m spatial resolution for all regions which are not coveredby SWBD are extracted and resampled using nearest neighborresampling for each SAR scene. These data sets are used for thedistinction between permanent water bodies and flooded areas.Secondly, the ASTER Global Digital Elevation Model Version 2

Fig. 1. Workflow of the fully automatic flood processing chain based on TerraSAR-X data.

S. Martinis et al. / ISPRS Journal of Photogrammetry and Remote Sensing 104 (2015) 203–212 205

(GDEM V2) (METI and NASA, 2011) with a pixel size of one arcsecond is employed for a refinement of the flood mask. The sameterrain information is used for the optional computation of a GIMin the pre-processing step. While the reference water mask isresampled to the SAR pixel size during its extraction from the glo-bal dataset, resampling of the DEM is conducted after the slope andoptional GIM computation (see Section 2.1.3). This is performedbecause the pixel size of TerraSAR-X scenes is smaller than thatof the DEM (�30 m) leading to slope values of zero in the resam-pled DEM for all target pixels covering the same source DEM pixel.

2.1.3. Slope and GIM computationThe slope information sl(x,y) in degrees for each pixel (x, y), i.e.,

the local steepness of the terrain, is used as input for the thematicanalysis and is computed from the DEM according to Burrough andMcDonell (1998):

slðx;yÞ ¼ arctan

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiDxðx;yÞn � rx

� �2

þDyðx;yÞn � ry

� �2s0

@1A � 180

pð1Þ

where Dx and Dy are the result of a standard Sobel edge filterapplied on the DEM taking into account n = 8 pixel values, and rx,ry is the pixel resolution of the DEM in x and y direction.

The local incidence angle h for each pixel (x, y), i.e., the GIM, isrequired for the radiometric calibration of a SAR scene to sigmanaught r0 and is computed using the following formula:

hðx;yÞ ¼ hp � hs ð2Þ

with the local beam incidence angle hp in the plane and the localslope hs in direction of the sensor beam. According to Etzrodt(2002) the range extent of a SAR scene, the incidence angles hn

and hf in near and far range as well as the satellite altitude hsat

are assumed constant for a given scene. hs and hp are obtained by

hp ¼ arctantanhn � hsat þ d � rx

hsat

� �ð3Þ

hs ¼ arctan cosb � Dxðx;yÞn � rx

� �þ sinb �

Dyðx;yÞn � ry

� �� �ð4Þ

where b describes the rotation angle of the SAR scene due to thesatellite orbit with respect to the north direction of the coordinatesystem and d is the number of image pixels in range. The directionof rotation (clockwise or counterclockwise) depends on the lookand orbit direction of the satellite. Eq. (4) is therefore true in caseof ascending left and descending right looking scenes. In case ofascending right or descending left looking scenes, the sum inEq. (4) becomes a difference.

206 S. Martinis et al. / ISPRS Journal of Photogrammetry and Remote Sensing 104 (2015) 203–212

2.1.4. SAR data pre-processingThe pre-processing of the TerraSAR-X amplitude data comprises

a radiometric calibration of the data to normalized radar cross sec-tion r0 (dB) to account for incidence angle linked SAR backscattervariations in range direction and to reduce topographic effects.According to Infoterra (2008) the radar brightness b0

dB (dB) can bederived from the digital numbers (DNs) by applying the calibrationfactor ks

b0db ¼ 10 � log10ðks � jDNj2Þ: ð5Þ

The scattering coefficient r0db, for each pixel can then be derived

using the local incidence angle h(x,y)

r0db ¼ b0

db þ 10 � log10ðsinhðx;yÞÞ: ð6Þ

In order to derive positive values for all following processingsteps, r0 is rescaled to a value range of [0, 400]. Finally, a medianfilter of kernel size 3x3 is applied on the rescaled pixel values forthe purpose of speckle reduction and pulse or spike noise removal.

2.2. Thematic analysis

2.2.1. Automatic tile-based thresholdingDue to its effectiveness and simplicity, thresholding represents

the most common way for initializing the extraction of floodaffected regions in SAR data during rapid mapping activities. Thisis performed by labeling all image elements with a backscattervalue lower than a defined threshold to the class ‘‘water’’. Theapplicability of thresholding procedures for water detection usingSAR data depends on the contrast between water and non-waterareas. The results are satisfactory for relatively calm water sur-faces, which can be regarded as specular reflectors with low back-scatter values. In contrast, the surrounding terrain usually exhibitshigher signal return due to increased surface roughness. Increasedsurface roughness of water caused by waves, precipitation as wellas diffuses and double bounce reflection in vegetated areas reducesthe capability to detect water surfaces. However, as these phenom-ena rarely occur, thresholding is usually capable of extracting mostparts of the flood plain. The comparison of the detected water sur-face with reference water masks or with pre-event remote sensingdata sets allows separating between flooded and standing waterareas. Different post-processing steps are usually conducted forimproving the initial classification result, e.g. pixel-based(Pulvirenti et al., 2012; Giustarini et al., 2013; Matgen et al.,2011; Mason et al., 2012) and object-based (Martinis et al., 2009)region growing, multi-contextual Markov image modeling(Martinis et al., 2011; Martinis and Twele, 2010) as well as theintegration of auxiliary data sets, such as digital elevation models(e.g. Pierdicca et al., 2008; Pulvirenti et al., 2011; Mason et al.,2012; Martinis et al., 2009) and land cover information (e.g.Pierdicca et al., 2008; Pulvirenti et al., 2011) into the classificationapproach.

Only few approaches can be found in the literature for the auto-matic extraction of water-related threshold values based on imagestatistics (Martinis et al., 2009; Schumann et al., 2010; Matgenet al., 2011; Pulvirenti et al., 2012). Threshold values are oftendefined by visually inspecting the image histograms or by usingmanual trial-and-error procedures (e.g. Henry et al., 2006;Townsend, 2001). Empirically defined default threshold valuesare used, e.g. in Künzer et al. (2013) for coarse scale flood mappingbased on ENVISAT ASAR-WSM imagery in the Mekong delta or inWendleder et al. (2011) for global water body detection based onTanDEM-X VV-polarized StripMap data. This may lead to satisfyingresults when deriving flooding from SAR data over only one AOIwith stable environmental conditions or when using data acquiredwith similar system parameters. According to our findings working

toward a fully automated TerraSAR-X based flood processor usingglobal data acquired with different sensor configurations (i.e.,polarization, beam mode (Staring Spotlight, Spotlight, StripMap,ScanSAR, Wide ScanSAR), and incidence angle), these approachesare not applicable. Therefore, optimal threshold values are scenedependent and thus need to be derived for each scene individually.

For the unsupervised initialization of the flood processor a para-metric tile-based thresholding procedure is applied (Martinis et al.,2009). This approach was originally developed to automaticallydetect the inundation extent in SAR amplitude data with evensmall a priori probabilities of the class-conditional densities ofthe class flood within the histogram of the entire SAR scene in atime-efficient manner. During flood-related rapid mapping activi-ties of DLR’s Center for Satellite Based Crisis Information (ZKI) thismethod has proven its effectiveness for SAR data of different wave-lengths (X-, C- and L-Band). In this contribution, this method isenhanced in robustness and adapted to SAR data radiometricallycalibrated to r0 (dB).

The initialization consists of the following processing steps:image tiling, tile selection and sub-histogram based thresholdingof a small number of tiles of the entire SAR image Y selectedaccording to the probability of the tiles to contain a bi-modalmixture distribution of the classes to be separated.

The tiling is based on statistical hierarchical relations betweenparent and child objects in a bi-level quadtree structure. Image Yis divided into quadratic non-overlapping sub-images Yn of sizec2 on level S+ (Fig. 2a). Each tile is then split into four quadraticsub-tiles of size (c/2) on level S�.

First, a number N0 of tiles Yn0 is selected which offer the highest(>95% quantile) standard deviation rþl�ðYnÞ on S+ of the mean valuesl�ðYnÞ of the respective sub-tiles on S� (Fig. 2a). This criterion servesas a measure of the degree of variation within the data and cantherefore be used as an indicator of the probability that the tilesare characterized by spatial inhomogeneity and contain more thanone semantic class. The selected tiles Yn0 should have a mean valuelþðYn0 Þ

lower than the mean lYnof all tiles on S+. This ensures that

tiles at the boundary between ‘‘water’’ and ‘‘no water’’ areas areselected. In the case that N0 is zero, the tile size on the parent S+

and child level S� is halved to c/2 and c/4, respectively, to guaran-tee a successful tile selection also in data with a low flood extent orwith smaller dispersed water bodies.

Out of the total set of N0 a limited number N00 of sub-images Yn00

is finally chosen for threshold computation by selecting tiles whichoffer the highest standard deviation rþl�ðYnÞ on S+ of the mean val-ues l�ðYnÞ and which have a mean value lþðYn0 Þ

lower than the meanof tiles Yn0 .

The Kittler and Illingworth minimum error thresholdingapproach (Kittler and Illingworth, 1986) is used to derive localthreshold values sn using a cost function which is based on model-ing the sub-histograms (Fig. 2b) of each tile as bi-modal Gaussianmixture distributions.

One global threshold sg is obtained by computing the arithmeticmean of the local thresholds. The standard deviation rs of thederived threshold values can be used as an indicator for a success-ful thresholding. If rs exceeds an empirically derived criticalthreshold sr (e.g. 5.0 dB) a (sub-) histogram merging strategy isapplied by computing sg directly from a merged histogram whichis a combination of the distributions of the individual tiles Yn00 . Ifthe tile selection or the derivation of a reasonable threshold valuefails (in this study we assume a maximum possible threshold of�10 dB), either no water areas exist in the covered region, thewater extent is very small or water bodies do not appear as darkbackscatter regions due to e.g. wind-induced roughening of thewater surface or protruding vegetation leading to volume or dou-ble bounce scattering of the radar signal. In that case the thresholdis approximated by the following equation which expresses the

Fig. 2. Schematic illustration of (a) the automatic tile-based thresholding procedure, (b) a sub-histogram of a selected tile and (c) the corresponding fuzzy standard Z functionfor backscatter r0.

S. Martinis et al. / ISPRS Journal of Photogrammetry and Remote Sensing 104 (2015) 203–212 207

linear relationship between the global threshold value sg (dB) sep-arating water and non-water areas and the scene center incidenceangle hc (�):

sg ¼ �0:0624 � hc � 13:874 ð7Þ

This correlation was derived empirically by analyzing a testdata set of 175 TerraSAR-X HH scenes acquired in different modes(Staring Spotlight, Spotlight, StripMap, ScanSAR, Wide ScanSAR)and incidence angles (20–55�) during flood events. The regressiondescribes the backscatter decrease over calm water areas withincreasing incidence angle.

2.2.2. Fuzzy logic based post-classificationA fuzzy logic based algorithm is used for post-classifying the

initial labeling result derived by the application of sg to image Y.Fuzzy logic (Zadeh, 1965) is a valuable tool for combining ambigu-ous information sources by accounting for their uncertainties asopposed to only relying on crisp data sets. Within the last yearsfuzzy logic has increasingly been used for improving flood moni-toring algorithms. Martinis and Twele (2010) apply fuzzy theoryfor the quantification of the uncertainty in the labeling of eachimage element in flood possibility masks. The proposed methodcombines marginal posterior entropy-based confidence maps withspatio-temporal relationships of potentially flooded double bounc-ing vegetation to open water areas. A pixel- and object-based fuzzylogic approach for flood detection based on Pierdicca et al. (2008) isdescribed in Pulvirenti et al. (2011) and Pulvirenti et al. (2013),respectively, integrating theoretical electromagnetic scatteringmodels, simplified hydrologic assumptions and context informa-tion. Based on available pre-disaster data their semi-automaticalgorithm has the capability to detect not only open water areas,but also flooded regions beneath vegetation.

Within this work a fuzzy set is built consisting of four elements:SAR backscatter (r0), digital elevation (h) and slope (s) informationas well as the size (a) of water bodies. The elements of the fuzzy setare defined by standard S and Z membership functions (Pal andRosenfeld, 1988), which express the degree of an element’s mem-bership mf to the class water. The degree of membership is defined

by real numbers within the interval [0, 1], where 0 denotes mini-mum and 1 maximum class membership (Fig. 2c). The member-ship degree strongly depends on the position of the crossoverpoint xc (i.e., the half width of the fuzzy curve), which is definedby the fuzzy thresholds x1 and x2.

The fuzzy threshold values for each element are either deter-mined according to statistical computations or are set empirically.Incorrectly labeled water regions are commonly caused by classify-ing objects with a low surface roughness and therefore low back-scatter characteristics similar to calm water surfaces, such asroads, ‘‘smooth’’ agricultural crop land or radar shadow. Digital ele-vation models (ASTER GDEM V2) are integrated into the post-clas-sification step to improve the classification accuracy throughsimple hydrological assumptions, i.e., by reducing the membershipdegree of an image element in dependence of the height above themain water area by applying the standard Z membership function.The open water surface is derived by applying the global thresholdsg to image Y.

The fuzzy thresholds are defined as

x1½h� ¼ lhðwaterÞ ð8Þ

and

x2½h� ¼ lhðwaterÞ þ f r � rhðwaterÞ ð9Þ

where lh(water) and rh(water) are the mean and standard deviation ofthe elevation of all initially derived water objects.

Using this fuzzy set, the number of look-alike areas in regionssignificantly higher in elevation than the mean of the water areasis reduced, e.g. in mountainous terrain. The factor fr is defined by:

f r ¼ rhðwaterÞ þ 3:5 ð10Þ

This function was integrated to reduce the influence of the DEM inareas of low topography. The minimum value of fr is defined as 0.5.

The standard Z function is used for describing the membershipdegree to open water areas according to the radar backscatter(Fig. 2c). Full membership is assigned to image elements with abackscatter lower than the fuzzy threshold

Fig. 3. Radiometrically calibrated TerraSAR-X data (upper row), fuzzy maps (mid row), and final classification results (lower row) for different test areas. Squares mark thevalidation areas in Germany and Thailand, which are visualized in Fig. 4.

208 S. Martinis et al. / ISPRS Journal of Photogrammetry and Remote Sensing 104 (2015) 203–212

x1½r0 � ¼ lr0ðsg Þ ð11Þ

where lr0ðsg Þ is the mean backscatter of the initial flood classifica-tion result by applying sg to Y. No membership degree [0] isassigned to pixels greater than

x2½r0 � ¼ sg : ð12Þ

Topographic slope information derived from globally availabledigital elevation data is integrated as a third element in the fuzzysystem by using the Z membership function with parametersx1[sl] = 0�, x2[sl] = 15�; Using this auxiliary information layer, waterlook-alikes in areas of steep terrain are removed.

The S membership function is applied to the size a of the waterbodies to reduce the number of dispersed small areas of lowbackscatter, which are commonly related to water look-alike areas.No membership degree is further assigned to elements with a sizelower x1[a] = 250 m2, maximum grade to elements with a sizegreater x2[a] = 1000 m2.

The corresponding fuzzy elements are combined into one com-posite fuzzy set by computing the average of the membershipdegrees of each pixel. In case that one single fuzzy element has amembership of zero, also the membership degree of the compositefuzzy set is assigned by a membership degree of zero. The floodmask is created through a threshold defuzzification step, whichtransforms each image element with a membership degree >0.6into a crisp value, i.e., a discrete label.

The transient shallow water zone between open flood watersurfaces and non-flooded regions is commonly characterized bysuccessively increasing backscatter levels, mainly resulting fromhigher signal return of emerging vegetation. In order to integrateimage elements of these areas and to increase the spatial homoge-neity of the detected flood plain a region growing step is per-formed. The preliminary extracted water bodies of thedefuzzified classification result are used as seeds for dilating thewater regions. The water areas are progressively enlarged until atolerance criterion is reached. Only image elements located in

the neighborhood of the flood areas are scanned. Thus, the risk ofdetecting water look-alikes distant from initially labeled water sur-faces is avoided. As region growing tolerance criterion a relaxedfuzzy threshold with a membership degree of >0.45 is chosen, i.e.only image elements with a fuzzy membership degree between0.45 and 0.6 neighboring pixels of the class flood are considered.Therefore, the region growing step is controlled by both the SARbackscatter information in combination with auxiliary data(topographic slope, elevation, and size of water bodies).

Finally, the GIM is integrated into the classification process toeliminate open water look-alikes in areas affected by radar layoverand shadowing effects. To differentiate between flooded areas andstanding water bodies, the classification result is compared to aglobal reference water mask (SRTM Water Body Data or MOD44Wdata) as described in Section 2.1.

The final flood mask and satellite footprints are stored in a data-base in raster and vector format, respectively, and are visualizedthrough a web-based user interface. The processing is based on aframework of Web Processing Services standard-compliant to theOpen Geospatial Consortium (OGC).

3. Experimental results

3.1. Study area and data-set

The complete processing chain was tested using a data set of175 different TerraSAR-X scenes acquired during flood situationsall over the world with different sensor configurations. Out ofthese test data set six radiometrically calibrated TerraSAR-Xscenes as well as the corresponding output fuzzy masks and finalclassification results of inundation events in Germany 2011,Thailand 2011, Albania/Montenegro 2013, Nepal 2008, USA2011, and Namibia 2009 are visualized in Fig. 3. In all SAR datasets open water areas appear dark due to specular reflection ofthe incident radar signal in comparison to diffuse scatteringtypical for land surfaces.

Fig. 4. Subsets of pre-processed TerraSAR-X data (upper row), validation masks (mid row), and classification results (lower row) for the study area in Germany (left column)and Thailand (right column). Center coordinates of the AOIs: 51.375�N, 12.008�E (Germany); 14.166�N, 100.751�E (Thailand).

S. Martinis et al. / ISPRS Journal of Photogrammetry and Remote Sensing 104 (2015) 203–212 209

Two subset regions of the TerraSAR-X data acquired in Germanyand Thailand (see Fig. 4) and the entire region of a TerraSAR-Xscene acquired in Albania/Montenegro are used for validating thethematic flood processor.

The area of interest (size: 910 * 710 m) in Germany covers aflood situation of January 2011 at River Saale near Schkopau, amunicipality in Saxony-Anhalt. The TerraSAR-X EEC StripMapscene (dimension: 22,727 * 16,727 pixels) was acquired on January17, 2011 (16:52 UTC) in HH-polarization with a pixel spacing of2.75 m in range and azimuth direction.

In order to carry out an experimental analysis aimed at assess-ing the performance of the proposed approach, the classificationresult is compared with a reference map created by visual interpre-tation and manual digitalization of digital aerial images (0.5 mspatial resolution), which were acquired at the same day of thesatellite overpass using color infrared (CIR) photography.

The HH-polarized StripMap scene of Thailand (dimension:21,666 * 14,500 pixels) was acquired on November 06, 2011(23:16 UTC) over an area situated at about 30 km northeast ofBangkok, and shows a subset (940 * 750 m) of a large-scale long-lasting flood situation in the Chao Phraya watershed with a pixel

spacing of 3.0 m in range and azimuth direction. A reference floodmask generated by visual interpretation and manual digitalizationof an IKONOS-2 scene with a pixel spacing of 0.83 m of November04, 2011 (03:54 UTC) is used for validating the TerraSAR-X floodmask. The time-offset relative to the SAR data is �67 h. However,due to the stable flood conditions verified with consecutive satel-lite overpasses, no critical change in the flood extents was observa-ble between the SAR and optical data sets.

The third study area is located in Albania and Montenegro. A HH-polarized TerraSAR-X ScanSAR scene (dimension: 15,333 *13,757 pixels, pixel spacing: 8.25 m) was acquired on March 20,2013 (16:32 UTC) covering inundations in the district of Shkoder/Albania and in the northern part of Lake Scutari/Montenegro. Inorder to show the performance of the proposed method on a largescale and to point out the influence of the fuzzy logic based post-pro-cessing step on the final classification result, a validation mask wascreated for the entire N0 = 22 SAR scene. As no high resolution opticalreference data were available for the entire scene, the validationmask was derived by applying an object-based semi-automatic algo-rithm on the SAR data in combination with manual classification andvisual comparison with data provided by Google-Earth.

Table 1Parameters of the automatic tile-based thresholding approach.

N N0 N00 s1 s2 s3 s4 s5 sg sr

Germany 2394 22 5 �13.6 �14.0 �17.3 �17.2 �17.6 �15.9 1.96Thailand 2035 51 5 �14.3 �14.7 �13.7 �13.7 �14.3 �14.1 0.82Albania 1365 43 5 �20.0 �18.4 �21.0 �18.5 �18.4 �19.2 1.20

Table 2Parameters of fuzzy logic based post-classification.

x1[r0] x2[r0] x1[h] x2[h] x1[sl] x2[sl] x1[a] x2[a]

Germany �29.9 �15.9 84.0 110.0 0.0 15.0 50 1000Thailand �22.69 �14.1 1.0 17.0 0.0 15.0 50 1000Albania �31.26 �19.2 �67.0 110.0 0.0 15.0 50 1000

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3.2. Results

In this section, the effectiveness of the proposed automaticapproach of tile-based thresholding and subsequent fuzzy logic-based post-classification refinement is evaluated.

According to an initial split size of c = 400 pixel the pre-processed SAR scenes of Germany, Thailand and Albania/Montenegroare tiled into 2394, 2035 and 1365 sub-scenes on the parent level(see Table 1), whereof an amount of N0 = 22, N0 = 51 and N0 = 43 tiles,respectively, fulfil the tile selection criterion as described in Section2.2.1. To restrict the computational effort five sub-scenes out ofN0 are finally selected according to the probability of the tiles tocontain a bi-modal mixture distribution of the classes water andnon-water for threshold computation on both test data sets usingthe KI thresholding algorithm. The histogram of each tile selectedis modeled successfully by statistical parameterization of localbi-modal class-conditional density functions and reliable thresh-olds are derived using minimum error thresholding. The localthresholds of each scene are marked by a standard deviation srsignificantly lower the critical value of 5.0 dB (Table 1). Therefore,the subsequent execution of a (sub-) histogram merging strategyis avoided by directly computing the arithmetic mean of locallyderived thresholds. Reasonable global threshold values of�15.9 dB (Germany), �14.1 dB (Thailand) and �19.2 dB (Albania/Montenegro) are derived for both TerraSAR-X scenes, which areused for the initialization of the post-classification process.

The post-classification refinement is based on fuzzy logic whichis used for combining SAR backscatter information with globalancillary data (see Section 2.1.2). The threshold values of the fourfuzzy elements SAR backscatter (r0), digital elevation (h) and slope

Table 3Accuracy matrices for the final labeling result of the AOI in Thailand and Germany(OA = Overall Accuracy, PA = Producer Accuracy, UA = User Accuracy).

Reference data

Classes Flood No flood Row total UA (%)

ThailandFlood 44,731 656 45,387 98.55No flood 9130 23,468 32,598 71.99Column total 53,861 24,124 77,985PA (%) 83.05 97.27 OA (%) 87.45

Khat (%) 73.22

GermanyFlood 51,608 4123 55,731 95.37No flood 2505 20,892 23,397 83.52Column total 54,113 25,015 79,128PA (%) 92.60 89.29 OA (%) 91.62

Khat (%) 80.28

(sl) information as well as the size (a) of water bodies are listed inTable 2 for all data sets.

The effectiveness of the proposed fully automatic algorithm isevaluated on a local scale by comparing the classification resultsof the Germany and Thailand scene with reference maps derivedby manual digitalization of the flood extent in validation data(Fig. 4). In Table 3 the accuracy assessment of the final classifica-tion results of the test data sets of Germany and Thailand is illus-trated. The results show encouraging overall accuracies (OA) of�87.5% for Thailand and �91.6% for Germany, respectively. Theproducer accuracy (PA) of the class flood differs by more than9.0% between the area in Germany (92.6%) and Thailand (83.1%).The user accuracy (UA) of the class flood reaches values of morethan 95.0% in both scenes. The PA refers to the probability that aflood area on the ground is classified as such, while the UA refersto the probability that a pixel labeled as flooded in the classifica-tion result belongs to this class in reality.

Due to low terrain variations within the small AOIs in Germanyand Thailand the advantage of the fuzzy logic based post-process-ing step cannot be stressed out. Therefore, the initial thresholdingresult as well as the final classification result of the TerraSAR-Xdata of Albania/Montenegro is compared with a validation maskgenerated for the whole SAR scene. The respective classificationresults are visualized in Fig. 5. The accuracy assessment is shownin Table 4. Due to radar shadowing effects occurring in the moun-tainous region in the north eastern part of the SAR scene a lot ofwater look alikes exists, which are characterized by similar back-scatter values as open water areas. Therefore, the classificationaccuracies of the initial thresholding result are very low (UA:20.92%, PA: 51.73%). The fuzzy logic based post-classification stepsignificantly enhances the final flood mask (UA: 82.41%, PA: 83.66).

The computational effort of the whole processing chain asshown in Fig. 1 is approx. 30 min for the TerraSAR-X Scene of Thai-land on an Intel Xeon CPU (6 Cores) with 2.4 GHz and 12 GB ofRAM (64 Bit OS).

3.3. Discussion

As can be seen in the Germany and Thailand test areas (Fig. 4),errors in waterline positions are mainly due to emergent vegeta-tion, which prohibit the specular reflection of the SAR data onthe smooth water surface and enhance the SAR signal return dueto volume scattering and/or double bounce reflection betweenthe water surface and lower sections of the vegetation layer. Thisgenerally leads to an underestimation of the flood extent. Misclas-sifications in flood plains interspersed with vegetation areincreased due to shadowing, foreshortening and layover effectsresulting from the side-looking geometry of the SAR sensors.

The fuzzy logic based post-classification step significantlyenhances the final classification result. This improvement mainlystems from an integration of terrain information (elevation andslope) into the classification process which drastically reducesthe number of look-alikes. The processing of 175 TerraSAR-X testscenes further confirms the observation that the incorporation ofterrain information in a fuzzy logic based post-processingapproach substantially increases classification accuracy, particu-larly in areas with a pronounced topographic variability.

Fig. 5. Initial thresholding result (left) and final classification result (right) of the TerraSAR-X ScanSAR scene of Albania/Montenegro. Center coordinate: 42.130�N, 19.306�E.

Table 4Classification accuracies of the initial thresholding and final labeling result for the AOIin Albania/Montenegro.

Initial thresholding Final classification

Flood No flood Flood No flood

UA (%) 20.92 98.56 82.41 99.72PA (%) 51.73 94.75 83.66 99.70OA (%) 93.62 99.43

S. Martinis et al. / ISPRS Journal of Photogrammetry and Remote Sensing 104 (2015) 203–212 211

The algorithm generally performs very well in rural areas. Inurban areas the detectability is reduced due to enhanced signalreturns with backscatter values greater the extracted threshold.These are related to multiple bounce effects on anthropogenicstructures and strong contributions from side-lobes of strongreflectors. Also SAR-specific illumination phenomena adverselyaffect the appearance and ability to detect flooding. Due to theside-looking nature of SAR, areas might not be visible due to shad-owing caused by buildings. The likelihood to detect inundations inurban areas generally increases with decreasing incidence angleand increasing distance between urban structures in range direc-tion. However, non-flooded roads and other smooth man-madesurfaces generally also appear dark due to specular reflection.Therefore, they can hardly be separated from calm water surfacesof urban floods. The restrictions of SAR in detecting water in urbanareas are a common problem which is widely discussed in the lit-erature (Martinis, 2010; Mason et al., 2010; Giustarini et al., 2013).Supervised methods are necessary to improve the classificationaccuracy in urban areas by combining SAR backscatter with highresolution digital surface models (Mason et al., 2012).

Problems occasionally occur due to artifacts in the ASTER GDEMV2. Especially noise in regions of normal water bodies (e.g. rivers)result in high slope values within plain surfaces which may lead toan increased missed alarms rate of water. To solve this problem,regions classified as non-water are labeled as standing water ifthese areas are flagged as water within the reference water masks.In the future it is intended to replace the ASTER GDEM V2 by theglobal TanDEM-X DEM with a spatial resolution of 12 m.

4. Conclusion and outlook

In this work, an on demand TerraSAR-X based flood service ispresented. The fully automated processing chain includes thedownload and pre-processing of TerraSAR-X data, computation

and adaption of global auxiliary data (reference water masks,digital elevation models, topographic slope information, and Geo-coded Incidence Angle mask), unsupervised class initialization,post-classification refinement and dissemination of the floodmasks via a web-based user interface.

The thematic processor features a tile-based thresholding pro-cedure, which had been proposed by Martinis et al. (2009) forautomatic flood extent mapping on SAR amplitude imagery. In thisstudy this approach was adapted to SAR data radiometrically cali-brated to r0 and enhanced in robustness to work also on data withvery low flood a priori probability of the class-conditional densityof the class flood within the global SAR histogram. Several criteriawere integrated to reduce risk of a failure of the processor, e.g. incase that no tiles are selected for threshold computation or no rea-sonable threshold value can be derived, caused e.g. by the absenceof water areas on the SAR scene or by a backscatter increase ofwind-roughened open water areas. A fuzzy logic-based algorithmcombining SAR backscatter information with different globallyavailable auxiliary data sources as well as a subsequent regiongrowing approach is used for post-classification of the initial thres-holding result.

Analyzing a data set of 175 different TerraSAR-X scenesacquired during flooding all over the world and in-depth validationof two inundation events in Germany, Thailand and Albania/Montenegro with overall accuracies of �91.6%, �87.5% and�99.4% confirm the effectiveness of the proposed thematic proces-sor, especially in rural areas.

The proposed processing chain currently employs global ancil-lary data sets, which are characterized by a spatial resolution sig-nificantly coarser than the pixel spacing of TerraSAR-X data.Future work will focus on the integration of upcoming up-to-dateglobal data sets of enhanced spatial resolution and accuracy in theprocessing chain to improve pre-processing quality and classifica-tion accuracy. The integration of e.g. the global TanDEM-X DEMand TanDEM-X Water Indication Mask (WAM) (Wendleder et al.,2011) with a spatial resolution of 12 m will be a significantimprovement in comparison to the ASTER GDEM V2 and theSWBD. We will also focus on integrating land-cover informationinto the fuzzy sets, to modify fuzzy threshold values in complexscenarios such as urban areas or deserts, where e.g. sand dunesare often characterized by backscatter characteristics similar tocalm open water bodies.

Currently, the flood mapping service is activated on-demand incase of emergencies. In future it is intended to run the service

212 S. Martinis et al. / ISPRS Journal of Photogrammetry and Remote Sensing 104 (2015) 203–212

systematically within DLR’s data and information managementsystem (DIMS).

Even if the complete processing chain is developed forTerraSAR-X data, the thematic processor has already proven itseffectiveness for other types of SAR data of different wavelengths(CosmoSky-Med, RADARSAT-1/2, ENVISAT ASAR, and ALOS/PAL-SAR). Current work focuses on adapting the complete processingchain to upcoming satellite systems such as European SpaceAgency’s satellite mission Sentinel-1, which will be a system oftwo C-Band SAR sensors enabling a systematic disaster monitoringwith high spatial and temporal resolution.

Acknowledgements

The authors thank Frank Friedrich from the State Office forFlood Protection and Water Management of Saxony-Anhalt forthe provision of aerial photography of the flood situation inSaxony-Anhalt. The authors are grateful to three anonymousreviewers for their helpful comments.

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