A ROBUST CHANGE DETECTION METHODOLOGY FOR TOPOGRAPHICAL APPLICATIONS. Booth Str. Ottawa, Ontario K1A 0E9 Canada

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1 A ROBUST CHANGE DETECTION METHODOOGY FOR TOPOGRAPHICA APPICATIONS G.A. ampropoulos a Tng u a and C. Armenas b a A.U.G. Sgnals td. St. Clar Avenue West th floor Toronto Ontaro M4V K7 Canada lamprotlu@augsgnals.com b Centre for Topographc Informaton Geomatcs Canada Natural Resources Canada 65 Booth Str. Ottawa Ontaro KA 0E9 Canada Commsson ThS - 3 KEY WORDS: Dstrbuted Processng Change Detecton Feature Extracton and Classfcaton. ABSTRACT: In ths paper several classfcaton methods are presented and the results are compared. The defnton of layer and the method to create t are then ntroduced. Based on layer a multple level change detecton algorthm s proposed whch gves the detals of the changes n each regon and s demonstrated to be an easy effectve and relable method. Expermental results are provded usng RADARSAT mages whch have been regstered wth the automated regstraton algorthm of A.U.G. Sgnals that s currently avalable under the dstrbuted processng system INTRODUCTON Change detecton s the process of dentfyng dfferences n the state of an object or phenomenon by observng t at dfferent tmes. It s useful n such dverse applcatons as land use change analyss montorng of shftng cultvaton assessment of deforestaton crop stress detecton and so on. It s essental for studyng changes on the earth s surface. Such changes may determne the rate of change for dsaster management (e.g. floodng) ce montorng earthquae predcton and montorng urban plannng etc. Remotely sensed data are now able to estmate changes wth very hgh accuracy. The accuracy s proportonal to the mage resoluton.e. the hgher the resoluton of the mages used the hgher the accuracy of the change detecton. There are several sensors used for change detecton. SAR sensors offer the advantage of provdng addtonal phase nformaton that may be used for change detecton. Ths s due to the fact that the pxels are complex numbers. When the pxel-to-pxel phase nformaton s beng used we say that ths change detecton process s based on nterferometry. When only the ampltude of the mages s used ths process s called photogrammetrc change detecton. Change detecton may be appled drectly on mages by usng only the pxel ampltude or both the magntude and phase or transformed pxel values. The well-nown change detecton technques are mage dfferencng mage ratong mage regresson Prncpal Component Analyss (PCA) wavelet decomposton change vector analyss and so on. In topographc change detecton for example f we want to study changes n a regon where the water level changes we are nterested n studyng only the changes between the two regons (land or water) [ ]. Hence all land pxels may be assgned one value and all the water pxels another value. In ths case study of changes s much easer and all unnecessary mage land or water nformaton has been elmnated through an mage segmentaton transformaton. To detect the changes for each regon classfcaton should be performed frst. There exst many classfcaton methods. In ths paper we used three methods whch are thresholdng fuzzy C-mean and decson tree. The remander of the paper s organzed as follows. A detaled topographc change detecton method based on regon classfcaton s descrbed n Secton. The defnton of layer s ntroduced n Secton 3. Secton 4 dscusses the dstrbuted computng technque. Some smulatons are gven n Secton 5. In Secton 6 the conclusons of the paper are drawn.. REGON CASSFCATON Regon classfcaton s a wdely used method for extractng nformaton on surface land cover from remotely sensed mages. The resultng cartography s helpng decson maers n dfferent research felds. There exst a lot of mage

2 classfcaton methods. Our change detecton approach that wll be proposed n secton III s a nd of post-classfcaton method so the classfcaton s a very mportant step. In ths paper the classfcaton methods we used are: thresholdng fuzzy C-means (FCM) and decson trees.. Thresholdng Consderng a grayscale mage t s possble to do the classfcaton by applyng the thresholdng technque usng the map hstogram. Thresholdng permts the dstncton of relevant topographc nformaton such as the laes rvers wetlands wooded areas esers roads etc. from contours and grd lnes. The map thresholdng classfcaton technque s based on the fact that dfferent textures have dfferent mean gray values on the map. Ths technque s defned as follows. If a pxel represents the texture of nterest we set ts value to n the new classfed mage and all the other pxels are set to 0 such as for g r ( x y) g j f ( x y) = 0 for r ( x y) < g and r ( x y) > g j where f ( x y ) s the pxel value n the new classfed mage and r ( x y ) s the orgnal pxel value. g and g j are gray values used as thresholds. Normally we are nterested n more than one regons. In ths case dfferent values wll be assgned to f ( x y ) for dfferent regons to dstngush them. The most approprate threshold values have to be determned by the operator snce these values may vary accordng to the prntng and scannng specfcs. Tae a loo at Fgure n whch there are two RADARSAT mages taen n and 997. These mages were provded by the Defence Research and Development Canada (DRDC)-Ottawa. These mages were regstered by the automatc regstraton algorthm of A.U.G. Sgnals td that s avalable through the dstrbuted computng at Roughly there are two regons n these mages: water and land. We can easly see the dfferences of water levels due to floodng of the rver n. We tae out the regons we are nterested from Fgure and plot them n Fgure whch are the sub-mages of the orgnal ones. To apply the thresholdng method to fnd the exact water and land regons we have to determne the threshold frst. Pc up some small regons wth nown classes (water or land) from the two mages. The pxels n these regons are used as the tranng data. The hstogram of these tranng data wll be plotted. Snce there are totally two regons n the mages the hstogram s bmodal. The lowest pont between the two ampltude peas n the hstogram can be set as the threshold. If there are N regons needed to be classfed the hstogram should have N peas. The thresholds should be set as the lowest ponts between every two consecutve ampltude peas n the hstogram. Fgure 3 gves the classfcaton results of these two mages usng ths thresholdng method. Furthermore f we want to classfy these mages n more detals nstead of water and land there are three regons: deep water shallow water and land. Usng the above thresholdng classfcaton method the results are gven n Fgure 4 where the blac regons represent the deep water grey ones are the shallow water and the whte regons stand for the lands.. Fuzzy C-Means Fuzzy clusterng has been proved that very well suted to deal wth the mprecse nature of geographcal nformaton ncludng remote sensng data. Accordng to the fuzzy clusterng framewor each cluster s a fuzzy set and each pxel n the mage has a membershp value assocated to each cluster rangng between 0 and measurng how much the pxel belongs to that partcular cluster [3]. There have been many dfferent famles of fuzzy clusterng algorthms proposed n the last decade. The one used n ths wor s the Fuzzy C-Means algorthm (FCM) whch s an teratve technque based on the mnmzaton of a generalzed group sum of squared error objectve functons [4] [5]. J m (U v; x) = c n = = (u ) m x v where the real number m s a weghtng exponent on each fuzzy membershp wth m <. c s the total number of clusters and n s the total number of pxels n the mage beng classfed. v = (v v vc ) are geometrc cluster prototypes. U = {u } s a c n matrx where the element of U u satsfes u [0] and c = u = for all. Fgure : Two regstered RADARSAT mages Fgure : sub-mages of the mages n Fgure. Mnmzaton of J m s based on the sutable selecton of U and v usng an teratve process through the followng steps Determnng values for c M error (e) and loop counter t=. Creatng a random c n membershp matrx U. Computng cluster centers. n v(t ) = = n (u (t ) ) m x = (u (t ) ) m

3 where s represent sub-band mages acqured from statonary wavelet transform. 4. Updatng the membershp matrx U. c ( + ) x m v t U = j= x v ( t+ ) ( t ) 5. Stop f U U e otherwse ncrease t and go to step 3. < The FCM algorthm s proved to be very well sutable for remote sensng mage segmentaton. But at the same tme t exhbts senstvty to the ntal guess wth regard to both speed and stablty and also shows senstvty to nose. Fgure 4 and 7 are the two- and three-regon classfcaton results for the mages n Fgure 4 usng ths FCM method..3 Decson Trees Another common approach to classfcaton s to use decson trees. The decson tree tself s a set of decson rules that descrbe each group's patterns learned from these gven examples. The decson tree algorthm used here s the "Quc Unbased Effcent Statstcal Trees" (QUEST). The algorthm s descrbed n [6] and the performance of ths algorthm compared wth other classfcaton methods can be found n [7]. Applyng the QUEST to the orgnal mages n Fgure 4 to dscrmnate regons of land and water Fgure 5 gves the classfcaton results. The three-regon results are plotted n Fgure 8. We must note before applyng the above classfcaton technques denosng method should be appled to the orgnal mages. In ths paper we use the wavelet denosng method combned wth smple nonlnear specle reducton flters (.e. medan flters). At frst we apply medan flterng to the orgnal mages. Medan flterng s a wdely used nonlnear process useful n reducng mpulses or salt-and-pepper nose. It s also useful n preservng edges n an mage whle reducng random nose. The wavelet denosng method s then appled. Wavelet transform s a useful tool for the tme-frequency analyss of sgnals. From the vewpont of sgnal processng wavelet analyss represents a sgnal by ts components n a seres of ndependent frequency channels (scales). By analyzng the behavor of the sgnal n each scale we can fnd the features of the sgnal or dscrmnate dfferent parts (such as the nose and the useful sgnal) of the combned sgnal. Mallat s [] research ndcated that the local maxmums of the wavelet transform of nose and sgnal have dfferent varaton rules wth the change of the scale. So denosng by wavelet method can be realzed by observng these local maxmums at each scale. A commonly used wavelet denosng method proposed by Donoho [] regards the wavelet coeffcent below a threshold as nose and set them to be zero. The results of classfcatons should be then fltered usng medan and seve flters to remove nose and all polygons that are smaller than a gven mnmum sze measured n pxels. The level of flterng must be chosen adequately to both eep small or solated feature map lnes and remove enough grd lnes and contours that may reduce the feature vsblty. Comparng the classfcaton results usng these three dfferent technques t s easy to fnd that the classfed mages n Fgure 3 and 6 usng thresholdng method are the clearest. The FCM algorthm s very nose senstve. The mages n Fgure 4 and 7 present a lot of salt-and-pepper nose. Snce n ths example the mages are sngle band the decson tree method s very smlar to the thresholdng method. By analyzng the tranng data a tree s structured wth the pxel value beng the only splt varable for each node. It s le usng the sample data to fnd the threshold and then do the thresholdng classfcaton. The performance of the decson tree method depends on the accurateness of the sample data and s more senstve to the addtve nose than the thresholdng technque. Among these three methods the FCM algorthm s the most automatc one whch doesn t need the tranng data but at the same tme gves the worst results. For the mult-spectral hyper-spectral or mult-polarzed mages classfcaton may be done usng matched flter [3] [5-7] or matched subspace flter. 3. CREATION OF AYERS ayers can be defned as mages contanng part of the nformaton of the orgnal mage. For example for a multband mage each band can be vewed as a layer. The mean of all the bands can be also vewed as a layer. Applyng the Prncple Component Analyss (PCA) to the mult-band mage the mages generated by the prncple components are also the layers of the orgnal mage. Another example of layers s applyng the orthogonal decomposton to the orgnal mage the resultng orthogonal components are the layers of the mage. Sayng a set of layers are complete means the orgnal mage can be fully generated usng ths set of layers. The layers are generated based on the user s need. Each layer should contan only part of the nformaton of nterested. Normally compared wth the orgnal mage each layer contans less nformaton so t s easer to perform the calculatons transformatons based on layers. Furthermore n some cases only parts of the layers are useful such as n mage fuson by PCA. For the topographc change detecton we are nterested n the regon changes for dfferent tme so the layers we used n ths paper are based on the regon classfcatons. Each layer contans only one regon of the orgnal mage. In Fgure 6 each mage contans three regons that are land shallow water and deep water. These regons should be extracted one by one to generate the layers. Fgure 9 shows the correspondng layers of both mages. The mages n red are the layers of the mage taen n and the layers taen n are plotted n green. (a) and (d) are the layer-of-land wth land represented n red/green. In (b) and (e) except the regons of shallow water all the others are n blac. So they are the layer-of-shallow water. Smlarly (c) and (f) are the layers-ofdeep water.

4 4. TOPOGRAPHIC CHANGE DETECTION 4. Change Detecton Based on Regon Classfcaton Topographc change detecton s studyng changes on the surface of the earth. Satellte mages are used to perform topographc detecton at very hgh accuracy. In ths paper we present a topographc change detecton method that apples the automatc update algorthm presented n []. Namely for a two level classfcaton problem we consder an mage + S where S = are compact regons of the mage represented by contours. The contours enclose pxels that correspond to the same regon. When a change occurs two groups of pxels are changng regon. Those that move from regon S to regon S are named as addtons (A) whle the others that change from regon S to regon S are called deletons (D). The total change C n the mage I s expressed as the summaton of addtons and deletons C=A+D. Namely where the subscrpt and - are the tme ndex whch represent the current and prevous tme respectvely. The advantage of ths method s detals of the changes are provded. In a log of applcatons we are not only nterested n where the changes happen but also how are the changes. In the followng we wll extend ths concept to multple regon cases and automatc update of nformaton. In a dstrbuted processng system ths mechansm may be programmed to eep updates of changes of classfcaton regons or other features over tme. For the mages have multple level classfcaton we are nterested n the changes n each regon.e. addton and deleton. Assume we have M nterested regons whch are presented n M layers where the regon-ofnterest n the layer R A D I I D A R = = + S + S S S =. M s denoted as R. The pxels n R have values and all the other pxels are set to zeros. The basc dea s comparng the pars of layers of dfferent tmes one by one. Namely fnd the addton and deleton for each. For each par of layers the regon-of-nterest R s exactly the S n our prevous dscusson and the other part havng zero values s () the S. In ths way f we use to denote the th layer at tme I the common regon of nterest wll be where the operator represents the element-by-element * multplcaton of two matrces and represents a regon whch s composed of the pxels whose values are ones n *. In ths way the addton of R therefore wll be and the deletons s S S () (3) (4) The total change for the th regon wll be C R = A + D R = = + ( ) However f we perform frame-to-frame subtracton we wll obtan and we have (7) C = C'. From the above we can see n a two level classfcaton problem the total change may be expressed through the absolute value of a frame-to-frame dfferencng. In practce we are nterested n more detals of the changes such as addtons and deletons. Our proposed formulatons gve these detals. Apply the above procedures to each par of layers. Step by step the addton and deleton for every class wll be detected sequentally. ( 4. Change ) Detecton Based on Pxel evel Characterstcs The ablty to preserve the pxel characterstcs from frame to frame when change detecton s performed s essental f multple classfcaton nferences are derved from the changes. In ths case mage classfcaton process s carred out on the change detecton results. Two methods have been studed for change detecton on mages wth multple classfcaton regons.e. the prncpal component analyss and the wavelet method. 4.3 Matched flterng and change detecton. Change detecton may be appled usng matched flters. Matched flters tend to suppress clutter and emphasze the changes of nterest. When matched flters are appled the change detecton performance ncreases. Matched flterng for change detecton s normally appled to multspectra and/or multpolarzed mages [3] [5]-[7]. 5. EXAMPE et s consder the two mages n Fgure 4. Ther layers are presented n Fgure 9. We apply the proposed mult-level change detecton method to the par of layers {(a) (d)} {(b) (e)} and {(c) (f)} respectvely. The result s dsplayed n Fgure 0 where the red regons represent deletons green ones stand for addtons and the yellow means no change happens. Fgure 0 clearly gves the detals of change n each regon. It s easy to fnd from Fgure 0 (c) because of the floodng n some regons of shallow water and land n the mage of become deep water (the red regon n (c)). For the same reason n (a) the green regons are the parts that are changed from shallow and deep water n to land n. Usng ths method avods need for strct radometrc calbraton. We can choose the approprate classfcaton scheme. The most mportant s t desgnates the types of changes occurrng. It s smple relable and effectve.. (5) (6)

5 Fgure 3: Two level regon classfcaton results usng thresholdng method. The blac areas represent water. Fgure 6: Three level regon classfcaton results usng thresholdng method. Fgure 4: Two level regon classfcaton results usng FCM method. The blac areas represent water. Fgure 7: Three level regon classfcaton results usng FCM method. Fgure 5: Two level regon classfcaton results usng decson tree method. The blac areas represent water. Fgure 8: Three level regon classfcaton results usng the decson tree method. 6. CONCUSION In ths paper several classfcaton methods are frst presented and the results are compared. We then ntroduce the defnton of layer and how to create t. Based on the layer a multple level change detecton algorthm s proposed whch gves the detals of the changes n each regon and s demonstrated to be an easy effectve and relable method. Expermental results are provded usng RADARSAT mages. (a) layer of land () (b) layer of shallow water () (c) layer of deep water ()

6 (d) layer of land () (e) layer of shallow water () (f) layer of deep water () Fgure 9: The layers of the mages n Fgure 4 generated usng the thresholdng classfcaton method (a) changes of land (b) changes of shallow water (c) changes of deep water Fgure0: Change detecton results of the regons of land shallow water and deep water. Yellow no change green addton red deleton blac regon of no nterest References: 9. V. Anastassopoulos and G.A. ampropoulos 995 ``Optmal CFAR Detecton n Webull Clutter ' IEEE Transactons on Aerospace and Electronc Systems IEEE Transactons on Aerospace and Electronc Systems Volume 3 Issue No. pp January. 0. Anastassopoulos G.A. ampropoulos A. Drosopoulos and M. Rey 999 "Hgh Resoluton Radar Clutter Statstcs" IEEE Transactons on Aerospace and Electronc Systems Vol. 35 No. pp Jan.. S. Mallat and W.. Hwang Sngularty detecton and processng wth wavelets. IEEE Transactons on Informaton Theory Vol. 38 No. pp D. Donoho Denosng by soft-thresholdng IEEE Transactons on Informaton Theory Vol. 4 No. 3 pp H. J. Zmmermann 99 Fuzzy set theory and ts applcatons Kluwer Academc Boston. 4. J. C. Bezde R. Ehrlch and W. Full 984 "FCM: the Fuzzy c-means clusterng algorthm" Computer and Geoscences vol. 0 pp J. C. Bezde and S. K. Pal 99 Fuzzy Models for Pattern Recognton IEEE Press. 5. W. Y. oh and Y. S. Shh 997 Splt selecton methods for classfcaton trees Statstcs Snca 7 pp T. S. m W. U. oh and U. S. Shh 000 A comparson of predcton accuracy complexty and tranng tme of thrty-three old and new classfcaton algorthm Machne earnng 40 pp Armenas C. educ F. Cyr I. Savopol F. Cavayas F. 003 A comparatve analyss of scanned maps and magery for mappng applcatons ISPRS Journal of Photogrammetry & Remote Sensng G. ampropoulos Y. A. Bardas J. Chan H. McNarn and B. ow 003 Web-Based Dstrbuted Processng Tools for Crop Classfcaton Usng CGDI Databases Proc. of SPIE's 48th Annual Meetng 003 (AM03) Vol. 503 San Dego CA Manolas D.G. and Shaw G.A.; 00 Detecton Algorthms for Hyperspectral Imagng Applcatons IEEE Sgnal Process. Mag. Vol. 9 No. January pp. 9-43V. 4. ampropoulos Y. A. Bardas A.U.G. Sgnals td. (Canada); B. ow 00 Web-based automatc multsensor mage regstraton usng the CEONet SPIE Proceedngs vol R. Cloude and E. Potter 996 "A revew of target decomposton theorems n radar polarmetry" IEEE Trans. Geoscence remote Sensng Vol. 34 No. pp Feb. 6. W.. Cameron N. Youssef and.k. eung 996 "Smulated polarmetrc sgnatures of prmtve geometrcal shapes" IEEE trans. on Geoscence Remote Sensng Vol. 34 No. 3 pp March. 7. R. Touz and F. Charbonneau 00 "Characterzaton of Target Symmetrc Scatterng Usng Polarmetrc SARs" IEEE Transactons on Geoscence and Remore Sensng Vol. 40 No. pp Nov. 8. G.A. ampropoulos V. Anastassopoulos and J.F. Boulter 997 Constant False Alarm Rate Detecton Of Pont Targets Usng Dstrbuted Sensors Optcal Engneerng Journal Vol. 37() February.