Automatic detection of flooded areas on ENVISAT/ASAR images using an object-oriented classification technique and an active contour algorithm.

Transcription

1 Auomaic deecion of flooded areas on ENVISAT/ASAR images using an objec-oriened classificaion echnique and an acive conour algorihm. R. Heremans 1, A. Willekens 2, D. Borghys 1, B. Verbeeck 2, J. Valckenborgh 2, M. Acheroy 1, C. Perneel 1 1 Royal Miliary Academy Signal and Image cener, Renaissancelaan 30, B-1000 Brussel Belgium 2 Vlaamse Landmaaschappij, OC GIS-Vlaanderen, Guldenvlieslaan 72, B-1060 Brussel Belgium Absrac Two echniques for exracing flooded areas ou of RADAR-imagery in a ime-efficien way are presened in his paper. The resuls of he objec-oriened classificaion echnique, based on he commercial ecogniion sofware, and he acive conour echnique, are boh obained on 2 ENVISAT ASAR images; one recorded during a flood period in Flanders on January he 2 nd of 2003 and one during a non-flooded region on June he 26 h of he same year. In boh echniques he ne flooded resul is based on he subracion of he exising waer bodies (i.e. lake river canal...) obained from he non-flooded reference image, from he image recorded during he flooded period. INTRODUCTION Floods in Flanders are a regularly recurring even. Floods can ofen cause remendous economic damage. To reduce he damage from flood people need o be well informed. For is flood managemen policy, he Flemish waer adminisraion (Afdeling Waerbouwkundig Laboraorium en Hydrologisch Onderzoek (WLH) and Afdeling Waer) has already developed compuer models of he mos imporan sreams under is auhoriy. Wih hese models he Flemish waer adminisraion ries o imiae he floods and o predic heir geographical exen. For a good validaion of hese model i is essenial ha a correc delineaion of he flooded areas is available. The Flemish waer adminisraion (Afdeling Waer) has also developed an inundaion daabase of he naural flooding areas and he recenly flooded areas (NOG/ROG daa base) in Flanders from 1988 o This daabase is an imporan insrumen for he policy of regional planning and he operaional waer sysem managemen. This daabase has been buil up wih informaion from local auhoriie Flemish adminisraions and consulancy agencies. Now, he problem arises o keep his inundaion daabase up o dae. To ensure he producion of validaion maerial for exising flood models and o ensure he acualisaion of he inundaion daabase, he presen projec aims o develop an operaional processing chain o exrac flooded areas from RADAR-imagery. The mehods were esed on ENVISAT/ASAR images (C-band, VVpolarized, spaial resoluion of 30 m and a pixel spacing of 12.5 m). Once he flooded areas are exraced, he delineaions of he flooded areas are pu on he geo-poral websie Geo-Vlaanderen on he inerne so ha local waer managers can manually add some addiional informaion o he inundaion daabase. In his paper we sar by briefly inroducing he preprocessing seps performed on he original ASAR images. Aferwards he principles used o georeference boh images are reviewed, followed by he main par, which explains he objec-oriened and acive conour echniques. In he las secion he geoporal websie is presened, which conains he exraced flooded resuls. PRE-PROCESSING CHAIN Since all SAR image or more generally all images produced by a coheren image sysem like laser, sonar, ulrasound, conain a noise-like componen called speckle, a firs sep o reduce his image degrading effec is underaken. The acual denoisesar program [1-2] used here was developed a he Ghen universiy. The mehod is based on a conex-based locally adapive wavele shrinkage. The idea is o esimae he saisical disribuions of he wavele coefficiens represening mainly noise and represening useful edges. In paricular, i was noed ha in SAR inensiy image he magniudes of he wavele coefficiens represening mainly noise follow an exponenial disribuion while he magniudes of he wavele coefficiens represening mainly useful signal follow a Gamma disribuion. This informaion is used o find a hreshold ha allows o disinguish he useful signal from he noise. Prior knowledge abou possible edge configuraions is inroduced using a Markov Random Field. Fig. 1 shows he ASAR-image of January 2 nd before (a) and afer (b) he speckle reducion.

2 (a) (b) Fig. 1: The original ASAR image from January shown a he region of Leuven before (a) and afer (b) he speckle reducion. REGISTRATION The SAR images were already convered o ground coordinaes and roughly geocoded by he image provider. Since he errain is relaively fla, we ried o apply an affine ransformaion for he regisraion. The ASAR image and 4 opographical maps from NGI were used o pinpoin he 8 GCP which were chosen o obain he affine ransformaion parameers. The GCP's were chosen such ha he mappings on boh he opological map as well as on he radar image were clearly visible. To show he qualiy of he georeferencing, one of he georeferenced ASAR images is shown in Fig. 2 wih he sree-ne superposed. FLOOD EXTRACTION While in many projecs he deecion and mapping of flooded zones are produced by manual phooinerpreaion i is expeced ha he disincion beween flooded zones and non-flooded zones is less ime consuming and more objecive, when auomaic exracion echniques or algorihms are used on radarimagery. To idenify flooded areas on radar-imagery, wo main echniques can be disinguished, i.e. pixel-based classificaion echniques and objec-oriened classificaion echniques. In he following wo analyse i.e. he objec-oriened algorihm and he acive conour algorihm, a combinaion of boh echniques is used on he ENVISAT ASAR images. Boh algorihms are essenially based on he fac ha flooded regions in a SAR images appear as black area since waer surface or more general any fla surface, behaves as a specular surface for microwave radiaion ha is emied sideways by he sensor. The resuls obained by boh algorihms will be compared and evaluaed a he end. Objec-Oriened Algorihm Fig. 2: Qualiy of he georeferencing resul. The sreene is superposed on he ASAR image. While a vas majoriy of remoe sensing applicaions sill rely on radiional pixel based classificaion mehods he demand for conex-based algorihms and objec-oriened image processing echniques is increasing. The commercial ecogniion sofware promoes his new perspecive. In conras o radiional pixel-based image processing mehod he basic processing unis of objec oriened

3 image analysis are image objecs or segmen and no single pixels. Even he classificaion acs on image objecs. The mehodical principles of objec-oriened image processing in ecogniion consis of wo basic domains: he segmenaion and he classificaion. Thu ecogniion firs performs a segmenaion of he imagery. Image segmenaion is a boom up regionmerging echnique saring wih one-pixel objecs. In numerous subsequen sep smaller image objecs are merged ino bigger ones. This resuls in an exracion of image objec primiives a differen resoluions. The segmenaion algorihm does no only rely on he pixel value bu also on he spaial coninuiy of he resuling objecs. In presen sudy he flood image and reference image were segmened wih a scale parameer value of 50, a color crierion value of 0.7, a shape crierion value of 0.3, a smoohness field value of 0.9 and a compacness field value of 0.1. The second key domain of ecogniion s engine is is knowledge base classificaion sysem, which makes i possible o include many oher aribues addiional o he specral informaion ha is provided wihin he image. Examples are shape informaion, exure informaion, relaions o neighbouring objecs and a good deal more. The cornersone of ecogniion s knowledge base classificaion of image objecs is he so-called classhierarchy. This class-hierarchy conains he classificaion rules o which he image will be classified. Each class is defined by a class-descripor and class descripions are defined using a neares neighbour or fuzzy membership funcion. When using he fuzzy neares neighbour classifier, individual image objecs are marked as ypical represenaives of a clas and hen he res of he scene is classified. Membership funcions are a simple mehod o ranslae an arbirary feaure value ino a membership degree beween 0 and 1, indicaing he membership of a class. Membership funcions are especially suied o inroducing exising knowledge or conceps ino he classificaion. In presen sudy, he homogeneous image objec primiives as obained in he segmenaion process were classified ino flooded and non-flooded areas using wo successive classificaions. In a firs classificaion, he class dark one objecs was inroduced in order o exrac all image objecs from he flood image having a mean value of less han 210. The class dark one objecs will conain no only flooded areas bu also oher areas having a low backscaering surface (e.g. exising waer bodie airpor river ). The classificaion has been execued by a membership funcion. In a second classificaion, a sub-class flooded area has been inroduced o separae he flooded areas form he oher dark oned objecs. For flooded areas a significanly decrease in pixel values is expeced beween he reference image and he flood image, whereas he pixel values of he oher dark oned areas remain more or less he same. Therefore, a second membership funcion has been inroduced o classify all objec classified as dark oned objecs in he firs classificaion and having a decrease in mean value bigger han 70 beween he reference image and he flood image, as being flooded areas. The resul of he objec oriened classificaion echnique using ecogniion is shown in Fig. 3. Fig. 3: Ne flood resul shown by he polygons on he ASAR image (Jan 2003) a he Schulens lake in Flanders using he Objec-Oriened Algorihm (ecogniion). Acive Conour Algorihm A region-based algorihm, recenly developed by Chesnaud e al. [3], will be inroduced o delineae flooded regions in Flanders. While in his acive conour algorihm an operaor was needed o draw he iniial polygon roughly on op of he objec of ineres, we will presen here a way how hese iniial polygons can be found auomaically using mahemaical morphology ools. Once he iniial polygons are given o he algorihm, an ieraive minimizaion procedure will be capable of compuing polygonal approximaions of he objec boundaries. Iniial Polygons- Mahemaical Morphology: To obain he iniial polygon which are he seeds for he main acive conour algorihm, he following chain of pixel based operaions are performed: Binary image on basis of hreshold Mahemaical Morphology Erosion (square 5x5) Mahemaical Morphology Dilaaion (square 3x3) Region2Objec ObjApproximaion The speckle-reduced image is ransformed ino a binary image based on a hreshold value of 140. All pixels wih a value lower han 140 (i.e. 18 in erms of an unsigned char hreshold) are replaced o one, all pixels wih a value greaer or equal o 140 are replaced o zero. In he second sep an erosion of he flooded areas (value one in he

4 binary image) is performed on he binary image wih a square srucuring elemen of 5x5 pixels large. This erosion is followed by a dilaaion wih a square srucuring elemen of 3x3 pixels large. The size of he srucuring elemen of he dilaaion is chosen smaller han for he erosion, such ha he resul of he wo operaions deliver objecs ha are compleely conained inside he flooded regions of ineres. Aferward hose objecs are vecorized ino approximaed polygon such ha he number of nodes in each individual polygon is decreased decenly. The only reason for his approximaion sep is o speed up he convergence in he ieraive acive conour algorihm, which is based on he replacemen of a randomly chosen node of he polygon. Acive Conour Mehodology: Assume ha he observed scene s { s( x, y) ( x, y) 1, N x [ 1, N y ]} is composed of wo regions; he flooded region or arge region {yx and he background region {yx, where w denoes a binary window funcion ha defines he shape of he arge objec o be found. This means ha w ( x, y) is equal o one for a pixel (x,y) wihin he arge and zero elsewhere. The purpose of he segmenaion is now o esimae he mos likely shape of w corresponding wih he arge in he scene. A polygonal shape w is chosen such ha he log-likelihood funcion is imized where and b are he parameers of he characerized probabiliy densiy funcion for respecively he arge and background regions. Under he assumpion ha he probabiliy densiy funcions for he arge and he background region are Gaussian disribued wih 2, = and b, b, represening he mean and variance from respecively he arge and background region. The imizaion of he loglikelihood funcion can be represened by: b = J ( w, s ) = N ( w)log ˆ ( w) N ( w) log ˆ b b ( w) (1) where only he relevan window dependen erms are menioned. Regularizaion of he conour: In he formalism described above he minimizaion of J ( w, s) corresponds wih one specific inundaion defined as he inerior of a polygon. This polygon is characerized by a finie number of nodes. Remark ha his resuling polygon can have sharp and very disconinuous boundaries. Since his is no favourable for inundaed regions he following regularizaion erm U in, based on he elasic energy used in he classical snake model is added o he previous equaion: J ) + ( w, ) = (1 ) J ( w, s U in (2) 2 Here λ (defined beween 0 and 1) represens a parameer, which allows one o balance beween he inernal and exernal energy. The inernal energy U in is defined as: N = nodes U in d i i = 1 where d i represens he disance beween node number i and he cener of he segmen defined by node i-1 and node i+1. N nodes defines he number of nodes of he polygon. By puing λ=0, no regularizaion is aken ino accoun. Ieraive Minimizaion Procedure: The minimizaion of J ( w, ) is performed using a sochasic ieraive algorihm which has been described in [4] and consiss of ieraing he following seps: 2 (3) 1) Calculae he energy J ( w, ) on he basis of he iniial/presen polygon. a) Choose randomly one node of he polygon and replace i randomly over a pixel disance L1 x and L1 y. Here L1 x and L1 y are inegers randomly chosen beween [-d L1,d L1 ], 0 wih L1 IN a fixed parameer. b) Accep he replacemen if i has lowered he energy J ( w, ), replace i back oherwise. c) Tes if he convergence crieria are reached. This convergence crieria depends on anoher fixed parameer N L1 represening he hreshold number of unsuccessful node replacemen ries. This means ha he ieraive procedure is sopped if he number of consecuive unsuccessful ries o replace a node reaches N L1. 2. Add a node o he presen polygon each ime he disance beween wo consecuive nodes is bigger han d wih j 1, 2 and resar a 1. Lj ( j 1) The final polygon is found afer repeaing he cycle menioned above hree imes. Remark ha he free parameers Li, N Li,and λ Li wih i 1,2, 3 can vary from one Level i o anoher. The reason why nodes are

5 added o he segmenaion resul of a cerain level is simply o refine he final resul. I is clear ha a complex shape will never be described by a polygon wih for insance only 4 nodes. Anoher imporan poin in he algorihm is ha he number of nodes added o he resuling polygon of a cerain level depends of he resoluion needed on he final image. Therefore wo addiional free parameer d L1 2 and d L2 3 were inroduced, indicaing ha each ime a node has o be added o he resuling polygon of level 1, or 2, if he disance following a cerain segmen exceeds respecively d L1 2 or d L2 3. A summary of all free parameer influencing he final resul is given in able 1 wih heir corresponding values. Parameer Level 1 Level 2 Level 3 λ Li N Li d Lj ( j 1) Table I: Tuning parameers of he acive conour algorihm wih heir corresponding values. Subracion of non-flooded from he flooded resul: When he acive conour algorihm is performed on he image aken during he flooded period and he one aken during he non-flooded period, boh vecorized resuls are replaced on raser and subraced from each oher. This subracion is performed such ha only he flooded areas remain and no all kinds of permanen waer bodies like lake canal waer spor area river... The disadvanage is ha a reasonably recen image of a non-flooded period has o be available and ha he whole acive conour algorihm has o be performed once more over i. A zoom of he final flood resul around he Schulen lake is shown in Fig. 4. Fig. 4: Ne flood resul shown by he polygons on he ASAR image (Jan 2003) a he Schulens lake in Flanders using he Acive Conour Algorihm. Comparison boh mehods: When comparing he resul obained wih he Objec-Oriened Algorihm wih ha of he Acive Conour Algorihm (Fig. 3 versus Fig. 4), one sees ha he Objec-Oriened algorihm ypically delineaes more precisely he black regions in he image, while he Acive Conour algorihm ends o find he bigges region keeping he variance in color low. This means ha boh resuls are supplemenary since he firs mehod deecs essenially only he waer areas while he second mehod also oleraes for insance waer areas wih some srucure in i, like ree bushe ec... GEO-PORTAL The geo-poral websie Geo-Vlaanderen is an exising websie ha has been developed and made operaional by he suppor cener GIS-Flanders. This websie provides an ineracive and dynamic way of informing a broad public abou exising geo-referenced governmenal hemaic daa. From his sie differen hemaic geo-porals can be consuled. Currenly exising and fully operaional hemaic geo-porals are for insance he geo-poral proeced landscapes, he geoporal regional zoning map, he geo-poral srees of Flanders, he geo-poral geographical saisics, he geo-poral naure areas A hemaic geo-poral flooded areas is now under developmen. Once he flooded areas are exraced from he ENVISAT image, he delineaions of he flooded areas can be pu on he geoporal websie Geo-Vlaanderen on he inerne so ha local waer managers can manually add some addiional informaion (e.g. deph of waer, source of flooding, dae of flooding, phoo ) for a cerain locaion o he inundaion daabase. The websie Geo-Vlaanderen can be consuled on hp:// CONCLUSIONS AND PERSPECTIVES

6 The wo mehods presened here, i.e. he objecoriened mehod based on ecogniion and he acive conour mehod, boh are capable of finding, on an auomaic basi he delineaions of he floods. The complemenary beween boh mehods will be furher invesigaed. In he near fuure a mehodology will be worked ou such ha an operaional ool as auomaic as possible is obained. Therefore he mehod for georeferencing he images needs o be changed. The idea here is o use he already consruced road deecor on he radar images and o find he ransformaion parameers of he affine ransformaion, which opimises his road deecor resul wih a vecorized waerway- and/or a roadmap. ACKNOWLEDGMENTS The auhors would like o hank Dr. Aleksandra Pizuricia for providing he Image Denoising code, which was applied in he analysis o reduce he speckle. We also would like o hank he people from he Signal and Image Cener involved in he consrucion of he C++ library. The research presened in his paper is funded by he Belgian Governmen, Federal Science Policy Office, in he frame of he STEREO Program (projec nr. SR/20/22: Floodmap: The Developmen of an Operaional Sysem o Suppor Flanders Flood Prevenion Policy ). Some of he used algorihms were developed in he frame of sudy F00/07 of he Belgian MoD. REFERENCES [1] A. Pizurica, W. Philip I. Lemahieu and M. Acheroy, Despeckling sar images using waveles and a new class of adapive shrinkage esimaors, in Proc. IEEE Conf. On Image Proc. (ICIP), Thessaloniki, Greece, Oc [2] A. Pizurica, "Image Denoising using Waveles and Spaial Conex Modelling," PhD hesi Universiy of Ghen, Faculy of Applied Science Deparmen of Telecommunicaions and Informaion Processing, June [3] C. Chesnaud, P. Réfrégier and V. Boule, "Saisical Region Snake-Based Segmenaion Adaped o Differen Physical Noise Models", IEEE Transacions on Paern Analysis and Machine Inelligence, vol. 21, No. 11, pp , Nov [4] O. Germain and P. Réfrégier, Opimal snake-based segmenaion of a random luminance arge on a spaially disjoin background. Opical leer vol. 2, pp , 1996.