AUTOMATIC DETECTION AND CLASSIFICATION OF DAMAGED BUILDINGS, USING HIGH RESOLUTION SATELLITE IMAGERY AND VECTOR DATA

Transcription

1 AUTOATIC DETECTION AND CLASSIFICATION OF DAAGED BUILDINGS, USING HIGH RESOLUTION SATELLITE IAGERY AND VECTOR DATA F. Samadzadegan, H. Rastves* Dept. of Surveyng and Geomatcs Engneerng, Engneerng Faculty, Unversty of Tehran, Tehran, Iran- (samadz, Commsson VIII, WG VIII/ KEY WORDS: Earthquae, Hgh Resoluton Satellte Imagery, Damage ap, Genetc Algorthm, Fuzzy Inference System ABSTRACT: Recevng rapd, accurate and comprehensve nowledge about the condtons of damaged buldngs after earthquae stre and other natural hazards s the bass of many related actvtes such as rescue, relef and reconstructon. Recently, commercal hgh-resoluton satellte magery such as IKONOS and QucBrd s becomng more powerful data resource for dsaster management. In ths paper, a method for automatc detecton and classfcaton of damaged buldngs usng ntegraton of hgh-resoluton satellte mageres and vector map s proposed. In ths method, after extractng buldngs poston from vector map, they are located n the pre-event and post-event satellte mages. By measurng and comparng dfferent textural features for extracted buldngs n both mages, buldngs condtons are evaluated through a Fuzzy Inference System. Overall classfcaton accuracy of 74% and appa coeffcent of 0.63 were acqured. Results of the proposed method, ndcates the capablty of ths method for automatc determnaton of damaged buldngs from hgh-resoluton satellte mageres.. INTRODUCTION Recevng rapd, accurate and comprehensve nowledge about the condtons of damaged area after earthquae stre s the bass for many related actvtes such as rescue, relef and reconstructon. In practce, lac of nformaton about the new condtons, may cause many problems durng the process of dsaster management. Preventon of natural dsasters s rarely acheved wth today s technology and nowledge. However, t s possble to avod or to reduce the mpacts of dsasters wth effectve dsaster management strateges. Geo-nformaton scence can provde concrete support for dsaster management actvtes n terms of effcency and speed up the data management, manpulaton, analyss, output and value of better decsons (ontoya, 00). There are several data resources for gettng nformaton about the damaged area, such as optcal and mcrowave satellte magery, LIDAR, aeral photography and vdeo magery (tom et al., 000). Remotely sensed data can provde valuable nformaton for dsaster management studes. Usng those data n post-dsaster response s very useful, especally for the hard-ht and dffcult-to-access areas (Vu et. al., 006). nmal feldwors (ncreasng safety), contnuous coverage area, dgtal processng and quanttatve results, are advantages of use of remote sensng technology for post-earthquae damage assessment whch are not affected by the dsaster. Recently, commercal hgh-resoluton satellte magery such as IKONOS and QucBrd, whch can acqure mageres wth 4m and.4m spatal resoluton n multspectral mode and m and 0.6m n panchromatc mode respectvely, s becomng more powerful data resource for dsaster management. Optcal remote sensng mages n many studes (atsuoa, 005; Chrou et al., 00; Gusella et al., 003; Huyc et al., 005; atsuoa et al., 005; LIU et al., 003; sumer et al, 004; Guler et al., 003; Shnozuaet al., 000; Sato and Spence, 004) were appled for damage assessment of earthquae. In the followng secton, the methodologcal approach for damage assessment usng satellte mageres s descrbed. ETHODOLOGY OF BUILDINGS DAAGE ASSESSENT Several automatc methods have been practced n order to detect damaged buldngs after an earthquae usng satellte magery. Generally, these methods can be categorzed n Image to Image and ap to Image strateges.. Image to Image The Image to Image strategy s based on comparson between pre-event and post-event mages. In these methods, after regstraton of mages, buldngs n both mages are extracted and compared wth each other. Dfferent pxel-based or obectorented change detecton algorthms are some of appled method n ths strategy. These methods were used by several researches for automatc damage assessment (Olgun, 000; Gusella et al., 005; atsuoa et al., 005; LIU et al., 003).. ap to Image In the ap to Image strategy, after geo-referencng the map and the post-event mage, locatons of all buldngs on the mage s specfed. Then, by extractng and computng spectral, textural and structural features for each canddate buldng, stuaton of the buldng s nspected. Therefore, havng nformaton about poston of each buldng usng vector map s the man advantage of ths strategy. On the other hand, loss of nformaton about the textural and spectral stuaton of buldngs, before destructon, whch can be useful for evaluaton of extracted features from after mage, seems to be the dsadvantage of ths strategy. Ths strategy was used n 45

2 The Internatonal Archves of the Photogrammetry, Remote Sensng and Spatal Informaton Scences. Vol. XXXVII. Part B8. Beng 008 many damage assessment studes (Emre sumer et al, 004; Yanamura et al., 003; Guler et al., 003). 3. PROPOSED ETHOD FOR DAAGE AP GENERATION In the frst strategy, havng pre- event spectral nformaton about the buldngs, and usng vector data to specfy buldngs locaton on the mage n the next strategy, are the advantages of both mentoned strateges. In ths paper, a new strategy s proposed for damage map generaton usng hgh-resoluton satellte mageres and vector data. Fgure presents the flowchart of the proposed strategy. As shown n the Fgure, the proposed strategy utlzes vector map and both pre-event and post-event mages of damaged area. In ths secton, man steps of the proposed method are descrbed. 3. Pre-processng To prepare data to be appled n the proposed method, preprocessng s performed. The pre-process s restrcted to mage enhancement algorthms such as hstogram equalzaton and hstogram matchng, and geo-referencng step.. Fgure. The flowchart of the proposed method 3. Engne After pre-processng, whereas mages were geo-referenced to the map, each selected buldng could be extracted from the mages. In ths research, extracted buldngs are evaluated usng textural features. In order to defne optmum features, by usng some tranng buldngs whch are nown, optmum features are selected by applyng Genetc Algorthm. Through nspectng the optmum features, the buldngs stuaton usng fuzzy nference system s defned. In the followng, all these steps are descrbed. 3.. Texture Analyss: In ths research, textural features of: features of st Order Statstcal, features of Haralc, features of Gabor, features of Fractal and 5 features of Varogram have been selected. Snce, these features were selected n prevous researches of earthquae damage assessment, they were selected. st Statstcal Features: In texture analyss, mean and varance of gray value are used as st statstcal textural feature (Jähne et al., 999). In ths case, the mean and standard devaton pxels gray value for canddate buldng are consdered as st statstcal features of extracted buldng. Haralc Features: Two-dmensonal co-occurrence (graylevel dependence) matrces, proposed by Haralc n 973, are generally used n texture analyss because they are able to capture the spatal dependence of gray-level values wthn an mage (Haralc et al., 973). A D co-occurrence matrx, P, s an n x n matrx, where n s the number of gray-levels wthn an mage. The matrx acts as an accumulator so that ] counts the number of pxel pars havng the ntenstes and. Pxel pars are defned by a dstance and drecton whch can be represented by a dsplacement vector d=(dx,dy), where dx represents the number of pxels moved along the x-axs, and dy represents the number of pxels moved along the y-axs of an mage slce. So, Haralc features for extracted buldng area on both before and after mages are measured. The formulas of these features are showed n Table. Feature Formula Entropy ] log ] Energy P [ ] Contrast ( ) ] 46

3 The Internatonal Archves of the Photogrammetry, Remote Sensng and Spatal Informaton Scences. Vol. XXXVII. Part B8. Beng 008 ] Homogenety + Sumean ( ] + ]) Varance (( μ ) ] + ( μ ) ]) Correlaton axmum Probablty ID N ( μ )( μ) ] σ ax( ]) ], ( ) Cluster Tendency ( + μ) ] Table. Some texture features extracted from gray level cooccurrence matrces. Gabor Features: For a gven mage I(x, y) wth sze P Q, ts dscrete Gabor wavelet transform s gven by a convoluton (Tuceryan, 998): G * ( x, y ) = I ( x s, y t ) ψ m n s t mn ( s, t ) Where, s and t are the flter mas sze varables, ψ s the complex conugate of ψ mn * mn () and m and n specfy the scale and orentaton of the wavelet respectvely, wth m = 0,,,-, n = 0,,, N-. Here, the mean and standard devaton of the magntude of the transformed coeffcents are used to represent the homogenous texture feature of a buldng. Semvarogram Features: Semvarance calculatons can be performed for texture analyss. The semvarogram s calculated from the raster mages usng dgtal numbers (DN)(Chca-olmo, 004). Table shows some extracted features from semvarogram. Feature Formula Smple γ ( = Varogram { DN + } = adogram γ ( = DN + = Radogram γ ( = DN + = h ) Cross γ ( h ) = { DN + h )}* h ) = varogram { DN + h )} h ) Pseudo-cross γ ( h ) = { DN + h )}* varogram h ) = { DN ( x ( x + h )} Table. Some texture features extracted from varogram Fractal Features: andelbrot proposed fractal geometry and s the frst one to notce ts exstence n the natural world (andelbort, 983). The fractal dmenson gves a measure of the roughness of a surface. Intutvely, the larger the fractal dmenson, the rougher the texture s (Jähne et al., 999). In ths paper, the mean and standard devaton of calculated fractal dmenson for a buldng s pxels are consderng as fractal textural features for extracted buldng. 3.. Optmum Feature Selecton Genetc Algorthm solves the problem of fndng good chromosomes by manpulatng the materal n the chromosomes blndly wthout any nowledge about the type of problem they are solvng. The only nformaton they are gven s an evaluaton of each chromosome they produce. Ths evaluaton s used to bas the selecton of chromosomes so that those wth the best evaluatons tend to reproduce more often than those wth bad evaluaton (Goldberg, 989). In ths case, chromosomes are textural features vector that have genes representatve of features. True value for any gene means the presence of correspondng feature n the feature vector. Evaluaton functon of the algorthm s overall accuracy of the maxmum lelhood classfcaton of tranng buldngs. Thus, the ftness crteron s to maxmze overall accuracy of classfcaton. axmum Lelhood classfcaton s performed for each feature vector n the each teraton and resulted overall accuracy s used as GA cost functon. The teraton s termnated when all chromosomes show same features vector. The fnal obtaned features vector s selected as optmum features. Encodng of feature vector nto an n-bt chromosome strng s showed by Fgure. Canddate Chromosomes Feature Feature Feature Feature (n-) Feature n Fgure. Encodng of feature vector nto a n-bt chromosome strng 3..3 Damage Detecton System After selectng optmum textural features, destructed buldngs stuatons are evaluated. Accordng to exstences of ambguty and vague n determnaton of buldngs destructon, a Fuzzy Inference System s used. Fuzzy nference s the process of formulatng the mappng from a gven nput to an output usng fuzzy logc. Fuzzy sets, that proposed by zadeh (zadeh, 965), and fuzzy operators are the subects and verbs of fuzzy logc. Usually the nowledge nvolved n fuzzy reasonng s expressed as rules n the form: If x s A Then y s B Where, x and y are fuzzy varables and A and B are fuzzy values. The f-part of the rule "x s A" s called the antecedent or premse, whle the then-part of the rule "y s B" s called the consequent or concluson. Statements n the antecedent (or consequent) parts of the rules may well nvolve fuzzy logcal connectves such as AND and OR. In the proposed Fuzzy Inference System, dfferences between optmum textural features, extracted from pre-event and postevent mages, for canddate buldng are consdered as nput lngustc varables and buldng labels ("Undamaged to Neglgble Damaged", oderate Damaged, Heavly 47

4 The Internatonal Archves of the Photogrammetry, Remote Sensng and Spatal Informaton Scences. Vol. XXXVII. Part B8. Beng 008 Damaged and Destructed ) are assgned as output lngustc varables (Fgure 3). Optmum Feature Optmum Feature Optmum Feature Fuzzy Inference System Order of Damage Fgure 3. Damage assessment operaton usng fuzzy nference system 4. EXPERIETNS AND RESULTS The proposed method n ths study s evaluated usng before and after December 6 th 003 earthquae QucBrd multspectral mages of Bam, Iran, acqured on September 30 th 003 (pre-event scene) and January 3 rd 004 (post-event scene) and relevant :000 vector map of the cty. From avalable mages, a 900*500 pxels area was selected as test area. Fgure 4 shows the data set after pre-processng step. (c) Fgure 4. Data set. (a) pre-event Image after preprocessng (b) post-event Image after pre-processng (c)extracted buldng layer form vector map For the purpose of optmum feature selecton, the GA s appled. In ths research, after 8 teratons of appled GA mean form st statstcal, mean from Gabor, mean from fractal, Radogram from sem-varogram, Entropy, Homogenety, Sum ean, Cluster Tendency and varance from Haralc features are selected as optmum features. After extractng the buldngs layer from vector map, 37 buldngs were defned n the test area. 4 buldngs were removed from 37 lst of buldngs because of ther neglgble sze. Other 33 buldngs were classfed usng a Fuzzy Inference System nto Undamaged to Neglgble Damaged, oderate Damaged, Heavly Damaged and Destructed classes. The membershp functons for the varable of the fuzzy system based on the some buldngs, whch they stuated by expert operator, are defned. Two nput varables and the only output varable s depcted n Fgure 5. Also, Table 3 shows some sample rules n the used Fuzzy system. (a) Very Low Low edum Hgh Cluster Tendency Low edum Hgh ean Gabor Neglgble Damaged (a) (b) 48

5 The Internatonal Archves of the Photogrammetry, Remote Sensng and Spatal Informaton Scences. Vol. XXXVII. Part B8. Beng 008 oderate Damaged Heavly Damaged Destructed Buldng Confuson matrx was calculated usng 00 randomly selected buldngs as reference dataset whch were stuated by vsual observatons. Overall accuracy of 74% and appa coeffcent of 0.63 were acqured for our classfcaton. Resulted confuson matrx s presented n Table 6. Also, resulted user accuracy, producer accuracy and overall accuracy s llustrated n Fgure 7. (b) Fgure 5. some membershp functons (a) membershp functon of nput varables (b) membershp functon of output varable If (Spectralean s Low) and (Gaborean s Low) and (Fractalean s Low) and (HaralcEntropy s Low) and (HaralcVarance s Low) and (HaralcClusterTendency s Low) and (HaralcSumean s Low) and (HaralcHomogenty s Low) then (BuldngLabel s UnDamaged) If (Spectralean s Hg and (Gaborean s Hg and (Fractalean s Hg and (HaralcEntropy s Hg and (HaralcVarance s Hg and (HaralcClusterTendency s Hg and (HaralcSumean s Hg and (HaralcHomogenty s Hg then (BuldngLabel s Destructon) Table 3. some sample rule n Fuzzy Inference system Proposed ethod Confuson Class Class Class 3 Class 4 OE PA atrx Class Class Class Class CE UA Reference Data Table 5. confuson matrx. (Class)Neglgble to Undamaged. (Class)oderate Dameged. (Class 3)Heavly Damaged. (Class4) Destructed. The results of the confuson matrx, ndcates the good capablty of ths method to separate Undamaged to Neglgble Damaged and Destructed classes from other classes. Fnal damage map generated usng ths method s depcted n fgure 6 and number of buldngs assgned to each of these classes s showed n Table4. Number of Buldngs Percentage Undamaged to Neglgble Damaged 8.30 oderate Damaged Heavly Damaged Destructed Summaton Table 4. Number of buldngs accordng to each class Fgure 7. Resulted user accuracy, producer accuracy and overall accuracy 5. CONCLUSION AND REARKS In ths study, we presented a new method for generatng damage map through texture analyss on pre-vent and postevent hgh resoluton satellte mageres. In the test area, a total of 33 buldngs were analyzed to measure ther condtons. The results are qute encouragng. The overall accuracy and appa coeffcent of 74.4% and 0.63 were computed. Obtaned results prove the ablty of hgh resoluton satellte mageres n assessment of earthquae destructon. Usng Genetc Algorthm n optmum feature selecton and Fuzzy Inference System to handle exstng ambguty n decson mang s regarded as man capabltes of ths method. Consderng mportance of data fuson, t s proposed to use all avalable data sources to mae better decsons. Besdes, because of capablty of textural features n damage assessment, t s recommended to consder other features to be used. Fgure 6. Resulted damage map for test area 49

6 The Internatonal Archves of the Photogrammetry, Remote Sensng and Spatal Informaton Scences. Vol. XXXVII. Part B8. Beng 008 REFERENCES Chca-Olmo. and Abarca-Hernández. F., 004. Varogram Derved Image Texture for Classfyng Remotely Sensed Images., Remote Sensng Image Analyss: Includng the Spatal Doman, 93. Chrou, L., Andre, G., Gullande, R., and Bahoen, F., 00. Earthquae Damage Assessment usng Hgh Resoluton Satellte Imagery, Proceedngs of the 7th Natonal US Conference on Earthquae Engneerng, Boston. Earthquae Damage Assessment for Bam, Iran, Usng Very Hgh Resoluton Satellte Imagery, nd Internatonal Worshop on Remote Sensng for Post-Dsaster Response, Unversty of Bologna Goldberg, D.E., 989. Genetc Algorthms n Search, Optmzaton & achne Learnng, Addson-Wesley Longman. Guler,.A., Turer,., 003., Detecton of the Earthquae Damaged Buldngs from Post-Event Aeral Photographs Usng Perceptual Groupng., ISPRS Conference, Commsson III, WG III/4 Gusella, L., Btell G., ognol, A., Huyc, C.K., Adams, B.J., 004, Obect Orented Approuch to Post-Earthquae Damage Assessment for Bam, Iran, Usng Very Hgh Resoluton Satellte Imagery, nd Internatonal Worshop on Remote Sensng for Post-Dsaster Response, Unversty of Bologna Gusella, L., Huyc, C.K., Adams, B.J., Cho, S., Chung, H., 005. Damage Assessment wth Very-Hgh Resoluton Optcal Imagery Followng the December 6, 003 Bam, Iran Earthquae, Unversty of Bologna Haralc, R.., Shanmugam, K., Dnsten, I., 973. Textural features for mage classfcaton., IEEE Trans. Systems, an Cybernet., 3, Jähne, B. (et al., eds.)., 999. Handboo on Computer Vson and Applcatons,, volume., pp , Academc Press, Boston, USA. LIU Ja-hang, SHAN Xn-an, YIN Jng-yuan., 003. Automatc recognton of damaged town buldngs caused by earthquae usng remote sensng nformaton: Tang the 00 Bhu, Inda, earthquae and the 976 Tangshan, Chna, earthquae as examples., ACTA SEISOLOGICA SINICA, Vol.7 No.6 (686~696) andelbrot, B. B., 983. The Fractal Geometry of Nature, Freeman, San Francsco. ATSUOKA,. and VU T.T. and YAAZAKI, F., 005. Automatc Damage Detecton And Vsualzaton Usng Hgh resoluton Satellte data for Post-Dsaster Assessment. tom H., Yamaza F., & atsuoa,., 000. Automated Detecton of Buldng Damage due to Recent Earthquaes Usng Aeral Televson Images. ontoya, L., 00. Gs and Remote Sensng n Urban Dsaster anagement. Paper presented at the 5th AGILE Conference on Geographc Informaton Scence, Palma. Olgun, E., 000. Izmt (Turey) Earthquae, August 7,999 and the Applcaton of Change Detecton Technques for Damage Assessment Usng Spot4 Satellte Images. Sato, K and Spence, R., 004. Usng Hgh-resoluton Satellte Images for Post Earthquae Buldng Damage Assessment: A Study Followng the 6..0 Guurat Earthquae, Earthquae Spectra. Shnozua,., Lee, G., Chen, Z and Yen, C., 000. Applcatons of Remote Sensng, The Ch-Ch Tawan Earthquae of September, 999: Reconnassance Report, CEER , ultdscplnary Center for Earthquae Engneerng Research, Unversty at Buffalo, pp SÜmer, E., & TÜrer,., 004. Earthquae Damage Detecton Usng Watershed Segmentaton and Intensty- Gradent Orentaton Approaches., aster Thess submtted to the Graduate School of Natural and Appled Scences., ddle East Techncal. Zadeh, L.A., (965)., Fuzzy Sets., Informaton and Control 40