Chesnel, Anne-Lise (2008) Damage assessment on buildings due to major disasters using very high resolution satellite multimodal images. PhD thesis Informatique temps réel, Robotique et Automatique, CEP - Centre Energétique et Procédés, ENSMP p.150.

Full text available as: - Chesnel_These.pdf ( 28635 Kb )

Licence: Copyright

Abstract

In a disaster aftermath, it is required to know rapidly the severity of the damage on buildings. Presently, this damage assessment is manually conducted through a visual comparison of satellite images. Automatic methods are immature; their performances being seldom quantified, they are not used by operational.

We propose a standard protocol to quantify the performances of the damage assessment methods. It is based on reference databases obtained from five various disaster cases. The protocol allows to quantify the performances of a method and to compare its results to other ones.

Having this assessment protocol, we propose a damage assessment method from a pair of panchromatic very high spatial resolution satellite images and a set of objects of interest defined in the reference image. The developed method has to lead to satisfying and reproducible results using images acquired with different modalities, and to be automated as much as possible. The damage on buildings are quantified from the amplitude of the changes on their roof. To compare the latter, they have to be registered. The geometric registration of very high resolution (VHR) images is an unsolved difficult problem; a new method that is adapted to our problem is developed and assessed. It generally leads to satisfying results for our application. Then change features are extracted. Two correlation coefficients and some textural features obtained by filtering are extracted, and a damage degree is attributed to each building through a supervised classification method. The impact of the differences in image modality on the performances of our method is assessed. The proposed method is fast, can be applied mostly generally, and is robust along with the use of VHR images with different spatial resolution or acquired with different sensors; the influential parameter is the B/H of the images pair.

References

[7sur7, 2006] 7sur7 (2006). ”Le Hezbollah capture 2 soldats israéliens”. 7sur7 du 12 juillet 2006.

Available online at : http ://www.7sur7.be/hlns/cache/fr/det/art_230975.html (accessed 15 May 2007).

[AbdelSayed et al., 1995] AbdelSayed, S. ; Ionescu, D. ; et Goodenough, D. (1995). ”Matching and registration method for remote sensing images”. In Proceedings of the International Geoscience and Remote Sensing Symposium, 1995, volume 2, pages 1029–1031.

[Adams, 2004] Adams, B. (2004). ”Improved Disaster Management Through Post-earthquake Building Damage Assessment Using Multitemporal Satellite Imagery”. In XXth ISPRS Congress, 12–23 July 2004 Istanbul, Turkey, page 611.

[Adams et al., 2003] Adams, B. ; Huyck, C. ; Eguchi, R. ; Yamazaki, F. ; et Estrada, M. (2003). ”Postearthquake reconnaissance using satellite imagery : Boumerdes case study”. In The Boumerdes, Algeria, Earthquake of May 21, 2003, pages 1–8. Oakland : EERI.

[Adams et al., 2004] Adams, B. ; Huyck, C. ; Mansouri, B. ; Eguchi, R. ; et Shinozuka, M. (2004). ”Application of High-Resolution Optical Satellite Imagery for Post-Earthquake Damage Assessment : The 2003 Boumerdes (Algeria) and Bam (Iran) Earthquakes”. Research Progress and Accomplishments 2003-2004, Buffalo : MCEER. Available online at : http://mceer.buffalo.edu/publications/resaccom/04-SP01/12_Eguchi.pdf (accessed 19 August 2008).

[AFPS, 2004a] AFPS (2004a). ”Le séisme de Bam (Iran) du 26 décembre 2000 – Rapport de mission”.

[AFPS, 2004b] AFPS (2004b). ”Le séisme de Boumerdès (Algeria) du 21 mai 2003 – Rapport de mission”.

[Akcay et Aksoy, 2007] Akcay, H. G. et Aksoy, S. (2007). ”Morphological Segmentation of Urban Structures”. In IEEE Urban Remote Sensing Joint Event, Paris, France, pages 1–6.

[Allenbach et al., 2005] Allenbach, B. ; Andreoli, R. ; Battiston, S. ; Bestault, C. ; Clandillon, S. ; et Fellah, K. (2005). ”Rapid EO Disaster Mapping Service : Added Value, Feedback and after 4 Years of Charter Actions”. In IEEE Proceedings of International Geoscience And Remote Sensing Symposium 2005, volume 6, pages 4373–4378.

[Althof et al., 1997] Althof, R. ; Wind, M. ; et Dobbins, J. (1997). ”A rapid and automatic image registration algorithm with subpixel accuracy”. IEEE Transactions on Medical Imaging, 16(3), pp. 308–316.

[André et al., 2001] André, G. ; Chiroiu, L. ; et Guillande, R. (2001). ”Evaluation et cartographie de dommages par imagerie satellitaire SPOT-5 : simulation sur la ville de Bhuj, séisme de Gujarat, Inde (26 janvier 2001)”. In SPOT-5 - Towards new application seminar, Toulouse, France.

[André et al., 2003] André, G. ; Chiroiu, L. ; Mering, C. ; et Chopin, F. (2003). ”Building destruction and damage assessment after earthquake using high resolution optical sensors. The case of the Gujarat earthquake of January 26, 2001”. In IEEE Proceedings of International Geoscience and Remote Sensing Symposium 2003, volume 4, pages 2398–2400.

[Arciniegas et al., 2007] Arciniegas, G. ; Bijker, W. ; Kerle, N. ; et Tolpekin, V. (2007). ”Coherence and Amplitude-Based Analysis of Seismogenic Damage in Bam, Iran, Using ENVISAT ASAR Data”. IEEE Transactions on Geoscience and Remote Sensing, 45.

[Baatz et Schäpe, 2000] Baatz, M. et Schäpe, A. (2000). Multiresolution Segmentation – an optimization approach for high quality multi-scale image segmentation”. In Strobl, B. et XII, G. A. G. I., editors, Beiträge zum AGIT-Symposium Salzburg 2000, Karlsruhe, Herbert Wichmann Verlag, pages 12–23.

[Baltsavias, 2004] Baltsavias, E. (2004). ”Object extraction and revision by image analysis using existing geodata and knowledge : current status and steps towards operational systems”. ISPRS Journal of Photogrammetry and Remote Sensing, 58(3–4).

[Baraldi et Parmiggiani, 1995] Baraldi, A. et Parmiggiani, F. (1995). ”An investigation of the textural characteristics associated with gray level cooccurrence matrix statistical parameters”. IEEE Transactions on Geoscience and Remote Sensing, 33(2), pp. 293–304.

[Barnea et Silverman, 1972] Barnea, D. et Silverman, H. (1972). ”A Class of Algorithms for Fast Digital Image Registration”. IEEE Transactions on Computing, 21(2), pp. 179–186.

[Barrodale et al., 1993] Barrodale, I. ; Skea, D. ; Berkley, M. ; Kuwahara, R. ; et Poeckert, R. (1993). ”Warping digital images using thin plate splines”. Pattern Recognition, 26(2), pp. 375–376.

[Beatson et Newsam, 1992] Beatson, R. et Newsam, G. (1992). ”Fast evaluation of radial basis functions”. Computers Mathematical Applications, 24(12), pp. 7–19.

[Benediktsson et al., 2003] Benediktsson, J. ; Pesaresi, M. ; et Amason, K. (2003). ”Classification and feature extraction for remote sensing images from urban areas based on morphological transformations”. IEEE Transactions on Geoscience and Remote Sensing, 41(9), pp. 1940–1949.

[Bhaskaran et al., 2001] Bhaskaran, S. ; Forster, B. ; et Neal, T. (2001). ”Integrating airborne hyperspectral sensor data with GIS for hail storm post-disaster management”. In 22nd Asian conference on remote sensing, 5-9 November 2001, Singapore.

[Bhattacharya et Sinha, 1997] Bhattacharya, D. et Sinha, S. (1997). ”Invariance of stereo images via theory of complex moments”. Pattern Recognition, 30(9), pp. 1373–1386.

[Bishop, 1996] Bishop, C. M. (1996). Neural networks for pattern recognition. Oxford University Press, Oxford, UK.

[Bitelli et al., 2004] Bitelli, G. ; Camassi, R. ; Gusella, L. ; et Mongnol, A. (2004). ”Image change detection on urban area : the earthquake case”. In XXth ISPRS Congress, Istanbul, Turkey, page 692.

[Bookstein, 1989] Bookstein, F. L. (1989). ”Principal Warps : Thin-Plate Splines and the Decomposition of Deformations”. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11(6), pp. 567–585.

[Bovolo et Bruzzone, 2007a] Bovolo, F. et Bruzzone, L. (2007a). ”A split-based approach to unsupervised change detection in large-size multitemporal images : Application to tsunami-damage assessment”. IEEE Transactions on Geoscience and Remote Sensing, 45(6), pp. 1658–1670.

[Bovolo et Bruzzone, 2007b] Bovolo, F. et Bruzzone, L. (2007b). ”A theoretical framework for unsupervised change detection based on change vector analysis in the polar domain”. IEEE Transactions on Geoscience and Remote Sensing, 45(1), pp. 218–236.

[Bovolo et al., 2007] Bovolo, F. ; Bruzzone, L. ; et Marconcini, M. (2007). ”An Unsupervised Change Detection Technique Based on Bayesian Initialization and Semi-Supervised SVM”. In Proceedings of the IEEE International Geoscience and Remote Sensing Symposium 2007.

[Bredensteiner et Bennett, 1999] Bredensteiner, E. et Bennett, K. (1999). ”Multicategory Classification by Support Vector Machines”. Computational Optimizations and Applications, 12(1-3), pp. 53–79.

[Brivio et al., 1992] Brivio, P. ; Ventura, A. D. ; Rampini, A. ; et Schettini, R. (1992). ”Automatic Selection of Control Points from Shadow Structures”. International Journal of Remote Sensing, 13(10), pp. 1853–1860.

[Brown, 1992] Brown, L. (1992). ”A survey of image registration techniques”. ACM Computing survey (CSUR), 24(4), pp. 325–376.

[Bruzzone et Cossu, 2003] Bruzzone, L. et Cossu, R. (2003). ”An adaptive approach to reducing registration noise effects in unsupervised change detection”. IEEE Transactions on Geoscience and Remote Sensing, 41(11), pp. 2455–2465.

[Bruzzone et Prieto, 2000] Bruzzone, L. et Prieto, D. (2000). ”Automatic analysis of the difference image for unsupervised change detection”. IEEE Transactions on Geoscience and Remote Sensing, 38(3), pp. 1171–1182.

[Burbridge et Zhang, 2003] Burbridge, S. et Zhang, Y. (2003). ”A neural network based approach to detecting urban land cover changes using Lansat TM and Ikonos imagery”. In Proceedings of the 2nd IEEE GRSS/ISPRS Joint Workshop on Data Fusion and Remote Sensing over Urban Areas, Berlin, Germany, pages 157–161.

[Burges, 1998] Burges, C. (1998). ”A Tutorial on Support Vector Machines for Pattern Recognition”. Data Mining and Knowledge Discovery, 2(2), pp. 121–167.

[C. Tison et Maître, 2004] C. Tison, F. T. et Maître, H. (2004). ”Un premier pas vers l’extraction de MNS urbains en interférométrie RSO à haute résolution par fusion”. Revue Française de Photogrammétrie et de Télédétection, (176), pp. 3–13.

[Campbell et al., 2000] Campbell, C. ; Cristianini, N. ; et Smola, A. (2000). ”Query Learning with Large Margin Classifiers”. In Proceedings of the Seventeenth International Conference on Machine Learning, pages 111–118, San Francisco, CA, USA. Morgan Kaufmann Publishers Inc.

[Canny, 1986] Canny, J. (1986). ”A computational approach to edge detection”. IEEE Transactions on Pattern Analysis and Machine Intelligence, 8, pp. 679–698.

[Carr, 1996] Carr, J. (1996). ”Spectral and textural classification of digital images”. Computer and Geosciences, Elsevier Science Ltd, UK, 22(8), pp. 849–865.

[Carr et Miranda, 1998] Carr, J. et Miranda, F. (1998). ”The semivariogram in comparison to the co-occurrence matrix for classification of image texture”. IEEE Transactions on Geoscience and Remote Sensing, 36(6), pp. 1945–1952.

[Chen et Hutchinson, 2005] Chen, Z. et Hutchinson, T. C. (2005). ”Urban damage estimation using statistical processing of satellite images : 2003 Bam, Iran earthquake”. Proceedings SPIE, 5667, pp. 289–300.

[Chica-Olmo et Abarca-Hernandez, 2000] Chica-Olmo, F. et Abarca-Hernandez, F. (2000). ”Computing geostatistical image texture for remotely sensed data classification”. Computers and Geosciences, Elsevier Science Ltd, UK, 26(4), pp. 373–383.

[Chiroiu, 2003] Chiroiu, L. (2003). ”High resolution satellite imagery - new perspectives for the earthquake risk management”. In Workshop on Application of remote sensing technologies for disaster response, University of California, Irvine.

[Chiroiu, 2005] Chiroiu, L. (2005). ”Damage assessment of the 2003 Bam, Iran, earthquake using Ikonos imagery”. Earthquake Spectra, 21(S1), pp. S219–S224.

[Chiroiu et André, 2001] Chiroiu, L. et André, G. (2001). ”Damage assessment using high resolution satellite imagery : application to 2001 Bhuj, India, Earthquake”. In Proceedings of the 7th National Conference on earthquake engineering : urban earthquake risk, Oakland USA : EERI. Available online at : http ://www.riskworld.com/Nreports/2001/Bhuj,India,earthquake2001.PDF (accessed on 19 August 2008).

[Coggins et Jain, 1985] Coggins, J. et Jain, A. (1985). ”A spatial filtering approach to texture analysis”. Pattern recognition letters, 3(3), pp. 195–203.

[Cook et al., 2001] Cook, M. ; Peterson, B. ; Dial, G. ; Gibson, L. ; Gerlach, F. ; Hutchins, K. ; Kudola, R. ; et Bowen, H. (2001). ”IKONOS technical performance assessment”. In SPIE proceedings series of the Algorithms for multispectral, hyperspectral, and ultraspectral imagery VII conference, Orlando FL, volume 4381, pages 94–108. Society of Photo-Optical Instrumentation Engineers, Bellingham, WA, INTERNATIONAL (1988).

[Coppin et al., 2004] Coppin, P. ; Jonckheere, I. ; Nackaerts, K. ; Muys, B. ; ; et Lambin, E. (2004). ”Digital change detection methods in ecosystem monitoring : a review”. International Journal of Remote Sensing, 25(9), pp. 1565–1596.

[Cord et Declercq, 2001] Cord, M. et Declercq, D. (2001). ”Three-dimensional building detection and modeling using a statistical approach”. IEEE Transactions on Image Processing, 10(5), pp. 715–723.

[Crammer et Singer, 2002] Crammer, K. et Singer, Y. (2002). ”On the algorithmic implementation of multiclass kernel-based vector machines”. Journal of Machine Learning Research, 2, pp. 265–292.

[Dai et Khorram, 1997] Dai, X. et Khorram, S. (1997). ”Development of a feature-based approach to automated image registration for multitemporal and multisensor remotely sensed imagery”. In Proceedings of the IEEE International Geoscience and Remote Sensing Symposium 1997, volume 1, pages 243–245.

[Dai et Khorram, 1998] Dai, X. et Khorram, S. (1998). ”The effects of image misregistration on the accuracy of remotely sensed change detection”. IEEE Transactions on Geoscience and Remote Sensing, 36(5), pp. 1566–1577.

[Deer, 1995] Deer, P. (1995). ”Digital change detection techniques : Civilian and military applications”. In International Symposium for Spectral Sensing Research.

[Dell’Acqua et al., 2006] Dell’Acqua, F. ; Trianni, G. ; Gamba, P. ; et Houshmand, B. (2006). ”Statistical analysis of SAR data to improve damage mapping at block scale”. In 4(th) International Workshop on Remote Sensing for Disaster Response, Cambridge, UK.

[Dou et al., 2003] Dou, A. ; Zhang, J. ; et Tian, Y. (2003). ”Retrieve seismic damages from remote sensing images by change detection algorithm”. In Proceedings of the IEEE International Geoscience and Remote Sensing Symposium 2003, volume 4, pages 2407–2409.

[Eguchi et al., 2005] Eguchi, R. ; Huyck, C. ; et Adams, B. (2005). ”An Urban Damage Scale based on Satellite and Airborne Imagery”. In Proceedings of the 3rd International Workshop on Remote Sensing for Post-Disaster Response, Chiba, Japan.

[Eguchi et al., 2003] Eguchi, R. ; Huyck, C. ; Adams, B. ; Mansouri, B. ; Houshmand, B. ; et Shinozuka, M. (2003). ”Resilient disaster response : Using remote sensing technologies for postearthquake damage detection”. Research Progress and Accomplishments 2001-2003, Buffalo : MCEER, pages 125–138. Available online at : http://mceer.buffalo.edu/publications/resaccom/03-sp01/09eguchi.pdf (accessed on 19 August 2008).

[Ehlers et Fuller, 1991] Ehlers, M. et Fuller, M. (1991). ”Region-based Matching For Image Integration In Remote Sensing Databases”. In IEEE Proceedings of International Geoscience and Remote Sensing Symposium 1991, volume 4, pages 2231–2234.

[Fellah et al., 2003] Fellah, K. ; Stock, L. C. ; Axes, F. ; Bach, H. ; Ebel, U. ; Grabak, O. ; et Fraipont, P. D. (2003). ”Towards an operational EO service for flood monitoring”. In IEEE Proceedings of International Geoscience and Remote Sensing Symposium 2003, volume 2, pages 1299–1301.

[Firouzabadi et Ramachandran, 2003] Firouzabadi, P. et Ramachandran, S. (2003). ”Change information extraction through image processing techniques”. Proceedings SPIE, 4886, pp. 528–533.

[Flusser, 1992] Flusser, J. (1992). ”An Adaptative Method for Image Registration”. Pattern Recognition, 25(1), pp. 45–54.

[Flusser et Suk, 1998] Flusser, J. et Suk, T. (1998). ”Degraded Image Analysis : An Invariant Approach”. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(6), pp. 590–603.

[Fonseca et Costa, 1997] Fonseca, L. et Costa, M. (1997). ”Automatic Registration of Satellite Images”. In Proceedings of the IEEE Symposium on Computer Graphics and Image Processing, volume 10, pages 219–226.

[Friedland et al., 2007] Friedland, C. ; Adams, B. ; et Levitan, M. (2007). ”Results of neighborhood level analysis of structural storm surge damage to residential structures”. In 5(th) International Workshop on Remote Sensing Applications to Natural Hazards, Washington DC, USA.

[Gamba et al., 2007a] Gamba, P. ; Dell’Acqua, F. ; et Odasso, L. (2007a). ”Object-oriented building damage analysis in VHR optical satellite images of the 2004 tsunami over Kalutara, Sri Lanka”. In Proceedings of the Urban Remote Sensing Joint Event, 2007, pages 1–5.

[Gamba et al., 2007b] Gamba, P. ; Dell’Acqua, F. ; et Trianni, G. (2007b). ”Rapid damage detection in the Bam area using multitemporal SAR and exploiting ancillary data”. IEEE Transactions on Geoscience and Remote Sensing, 45(6), pp. 1582–1589.

[Gamba et al., 2007c] Gamba, P. ; Dell’Acqua, F. ; Trianni, G. ; et Stasolla, M. (2007c). ”GIS and Remote Sensing for Disaster Assessment in Urban Areas”. In Proceedings of the Urban Remote Sensing Joint Event, 2007, pages 1–5.

[Gevers et Smeulders, 1999] Gevers, T. et Smeulders, A. (1999). ”Color-based object recognition”. Pattern Recognition, 32(3), pp. 453–464.

[Gianinetto et al., 2006] Gianinetto, M. ; Villa, P. ; et Lechi, G. (2006). ”Postflood Damage Evaluation Using Landsat TM and ETM+ Data Integrated With DEM”. IEEE Transactions on Geoscience and Remote Sensing, 44(1), pp. 236–243.

[Girard et al., 1998] Girard, S. ; Guerin, P. ; Maitre, H. ; et Roux, M. (1998). ”Building detection from high-resolution color images”. Proceedings of SPIE International Society for Optical Engineering, Image and Signal Processing for Remote Sensing IV Symposium, Barcelona, Spain, 3500, pp. 278–289.

[Goshtasby, 1985] Goshtasby, A. (1985). ”Description and Discrimination of Planar Shapes Using Shape Matrices”. IEEE Transactions on Pattern Analysis and Machine Intelligence, 7(6), pp. 738–743.

[Goshtasby, 1986] Goshtasby, A. (1986). ”Piecewise linear mapping functions for image registration”. Pattern Recognition, 19(6), pp. 459–466.

[Goshtasby, 1987] Goshtasby, A. (1987). ”Piecewise cubic mapping functions for image registration”. Pattern Recognition, 20(5), pp. 525–533.

[Goshtasby, 1988] Goshtasby, A. (1988). ”Image registration by local approximation methods”. Image and Vision Computing, 6(4), pp. 255–261.

[Goshtasby et Stockman, 1985] Goshtasby, A. et Stockman, G. (1985). ”Point Pattern Matching Using Convex Hull Edges”. IEEE Transactions on Systems, Man and Cybernetics, 15, pp. 631–637.

[Govindu et al., 1998] Govindu, V. ; Shekhar, C. ; et Chellappa, R. (1998). ”Using geometric properties for correspondence-less image alignment”. In Proceedings of the Fourteenth International Conference on Pattern Recognition, volume 1, pages 37–41.

[Greidanus et al., 2005] Greidanus, H. ; Dekker, R. ; Caliz, J. ; et Rodrigues, A. (2005). ”Tsunami damage assessment with satellite radar”. In URSI 2005 Commission F Symposium on Microwave Remote Sensing of the Earth, Oceans, Ice, and Atmosphere, Ispra, Italy.

[Greiwe et Ehlers, 2005] Greiwe, A. et Ehlers, M. (2005). ”Combined Analysis of Hyperspectral and High resolution Image Data in an Object oriented Classification Approach”. In Proceedings of the 5th International Symposium Remote Sensing of Urban Areas (URS 2005), Tempe, AZ, USA (CD proceedings).

[Grimson, 1982] Grimson, W. (1982). ”A Computational Theory of Visual Surface Interpolation”. Royal Society of London Philosophical Transactions Series B, 298, pp. 395–427.

[Growe et Toenjes, 1997] Growe, S. et Toenjes, R. (1997). ”A Knowledge Based Approach to Automatic Image Registration”. In Proceedings of the 1997 International Conference on Image Processing (ICIP ’97), volume 3, page 228, Washington, DC, USA. IEEE Computer Society.

[Gruen, 1998] Gruen, A. (1998). Automatic Extraction of Man-Made Objects from Aerial and Space Images (II). Birkhauser Boston.

[Grünthal et al., 2001] Grünthal, G. ; Musson, R. ; Schwarz, J. ; et Stucchi, M. (2001). L’Echelle Macrosismique Européenne 1998, volume 19. Cahiers du Centre Européen de Géodynamique et de Séismologie, Luxembourg.

[Guermeur, 2007] Guermeur, Y. (2007). ”SVM Multiclasses, Théorie et Applications”. Habilitation à Diriger des Recherches, HAL - CCSD. Available online at : http://tel.archives-ouvertes.fr/tel-00203086/en/ (accessed 19 August 2008).

[Guindon, 1997] Guindon, B. (1997). ”Computer-based aerial image understanding : A review and assessment of its application to planimetric information extraction from very high resolution satellite images”. Canadian Journal of Remote Sensing, 23(1), pp. 38–47.

[Guo et Yasuoka, 2002] Guo, T. et Yasuoka, Y. (2002). ”Snake-based approach for building extraction from high-resolution satellite images and height data in urban areas”. In Proceedings of 23rd Asian Conference on Remote sensing.

[Gusella et al., 2005] Gusella, L. ; Huyck, C. ; Adams, B. ; Cho, S. ; et Chung, H. (2005). ”Damage Assessment with Very-High Resolution Optical Imagery Following the December 26, 2003 Bam, Iran Earthquake”. In Proceedings of the 3rd International Workshop on Remote Sensing for Post-Disaster Response, Chiba, Japan.

[Habib et al., 2004] Habib, A. ; Al-Ruzouq, R. ; et Kim, C. (2004). ”Semi-automatic registration and change detection using multi-source imagery with varying geometric and radiometric properties”. In Proceedings of the XXth ISPRS Congress, 12-23 July 2004 Istanbul, Turkey, pages 445–450.

[Haithcoat et al., 2001] Haithcoat, T. ; Song, W. ; et Hipple, J. (2001). ”Building footprint extraction and 3-D reconstruction from LIDAR data”. In IEEE/ISPRS Joint Workshop on Remote Sensing and Data Fusion over Urban Areas, pages 74–78.

[Haralick et al., 1973] Haralick, R. ; Shanrnugan, K. ; et Dinstein, I. (1973). ”Textural features for image classification”. IEEE Transactions on System, Man and Cybernetics, SMC-3(6), pp. 610–621.

[Haverkamp, 2004] Haverkamp, D. (2004). ”Automatic Building Extraction from Ikonos Imagery”. In Proceedings of the ASPRS 2004, Annual Conference.

[Haverkamp et Poulsen, 2003] Haverkamp, D. et Poulsen, R. (2003). ”Change detection using Ikonos imagery”. In Proceedings of the ASPRS 2003, Annual Conference and Technology exhibition, Anchorage, Alaska.

[Hisada et al., 2005] Hisada, Y. ; Shibayama, A. ; et Ghayamghamian, M. R. (2005). ”Building damage and seismic intensity in Bam City from the 2003 Iran, Bam, Earthquake”. Bulletin of the Earthquake Research Institute, University of Tokyo, 79(3/4), pp. 81–93.

[Holm, 1991] Holm, M. (1991). ”Towards Automatic Rectification Of Satellite Images Using Feature Based Matching”. International Geoscience and Remote Sensing Symposium, 1991, 4, pp. 2439–2442.

[Hotelling, 1936] Hotelling, H. (1936). ”Relations between two sets of variates”. Biometrika, 28, pp. 321–377.

[Hsieh et al., 1996] Hsieh, J.-W. ; Liao, H.-Y. M. ; Fan, K.-C. ; et Ko, M.-T. (1996). ”A Fast Algorithm for Image Registration without Predetermining Correspondences”. In Proceedings of the 1996 International Conference on Pattern Recognition (ICPR ’96), volume I, page 765, Washington, DC, USA. IEEE Computer Society.

[Hsieh et al., 1992] Hsieh, Y. ; McKeown, D. ; et Perlant, F. (1992). ”Performance Evaluation of Scene Registration and Stereo Matching for Artographic Feature Extraction”. IEEE Transactions on Pattern Analysis and Machine Intelligence, 14(2), pp. 214–238.

[Hsu et Lin, 2002] Hsu, C.-W. et Lin, C.-J. (2002). ”A comparison of methods for multi-class support vector machines”. IEEE Transactions on Neural Networks, 13(2), pp. 415–425.

[Huyck et al., 2005] Huyck, C. ; Adams, B. ; Cho, S. ; Chung, H.-C. ; et Eguchi, R. (2005). ”Towards rapid citywide damage mapping using neighborhood edge dissimilarities in very high-resolution optical satellite imagery – Application to the 2003 Bam, Iran, earthquake”. Earthquake Spectra, 21(S1), pp. S255–S266.

[Im et al., 2005] Im, J. ; Jensen, J. ; et Tullis, J. (2005). ”Development of a remote sensing change detection system based on neighborhood correlation image analysis and intelligent knowledgebased systems”. In IEEE Proceedings of International Geoscience and Remote Sensing Symposium 2005, volume 3, pages 2129–2132.

[Inglada, 2002] Inglada, J. (2002). ”Similarity measures for multisensor remote sensing images”. In IEEE Proceedings of International Geoscience and Remote Sensing Symposium 2002, volume 1, pages 104–106.

[Inglada et Giros, 2004a] Inglada, J. et Giros, A. (2004a). ”On the possibility of automatic multisensor image registration”. IEEE Transactions on Geoscience and Remote Sensing, 42(10), pp. 2104–2120.

[Inglada et Giros, 2004b] Inglada, J. et Giros, A. (2004b). ”On the real capabilities of remote sensing for disaster management-feedback from real cases”. In IEEE Proceedings of International Geoscience and Remote Sensing Symposium 2004, volume 2, pages 1110–1112.

[Inglada et Mercier, 2007] Inglada, J. et Mercier, G. (2007). ”A new statistical similarity measure for change detection in multitemporal SAR images and its extension to multiscale change analysis”. IEEE Transactions on Geoscience and Remote Sensing, 45, pp. 1432–1445.

[International-Charter, 2005] International-Charter (2005). ”International Charter Space and Major Disasters 4th Annual Report – January-December 2004”. Available online at : http://www.disasterscharter.org/downloadable/ (accessed 19 August 2008).

[International-Charter, 2007] International-Charter (2007). ”International Charter Space and Major Disasters”. Available online at : http ://www.disasterscharter.org/ (accessed 19 August 2008).

[Jain et al., 2000] Jain, A. ; Duin, R. ; et Jianchang, M. (2000). ”Statistical pattern recognition : a review”. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(1), pp. 4–37.

[Jin et Davis, 2005] Jin, X. et Davis, C. (2005). ”Automated building extraction from high-resolution satellite imagery in urban areas using structural, contextual, and spectral information”. EURASIP Journal on Applied Signal Processing, 14, pp. 2196–2206.

[Joachims, 1999] Joachims, T. (1999). ”Transductive Inference for Text Classification using Support Vector Machines”. In Proceedings of the Sixteenth International Conference on Machine Learning ICML’99, pages 200–209, San Francisco, CA, USA. Morgan Kaufmann Publishers Inc.

[Karjalainen et al., 2003] Karjalainen, M. ; Hyyppa, J. ; et Devillairs, Y. (2003). ”Urban change detection in the Helsinki metropolitan region using Radarsat-1 fine beam images”. In 2nd GRSS/ISPRS Joint Workshop on Remote Sensing and Data Fusion over Urban Areas, pages 273–277.

[Kauth et Thomas, 1976] Kauth, R. et Thomas, G. (1976). ”The Tasseled Cap - a graphic description of the spectral-temporal development of agricultural crops as seen by Landsat”. In Proceedings of the Symposium on Machine Processing of Remotely Sensed Data, Purdue University, West Lafayette, Indiana, pages 4B41–4B51.

[Kim et al., 2004] Kim, T. ; Lim, Y.-J. ; Jeong, S. ; et Kim, K.-O. (2004). ”Semi-automated Map Object Extraction From 1m Resolution Space Images”. In Proceedings of XXth ISPRS Congress, 12-23 July 2004 Istanbul, Turkey, pages 581–586.

[Kim et Muller, 1995] Kim, T. et Muller, J.-P. A. (1995). ”Toward an automated building detection system from satellite data”. In Proceedings of SPIE International Society for Optical Engineering, Image and Signal Processing for Remote Sensing II, volume 2579, pages 394–404.

[Kohiyama et Yamazaki, 2005] Kohiyama, M. et Yamazaki, F. (2005). ”Damage Detection for 2003 Bam, Iran, Earthquake Using Terra-ASTER Satellite Imagery”. Earthquake Spectra, 21, pp. S267–S274.

[Kosugi et al., 2004] Kosugi, Y. ; Sakamoto, M. ; Fukunishi, M. ; Lu, W. ; Doihara, T. ; et Kakumoto, S. (2004). ”Urban change detection related to earthquakes using an adaptative nonlinear mapping of high-resolution images”. IEEE Geoscience and Remote Sensing Letters, 1(3), pp. 152–156.

[Kouchi et al., 2006] Kouchi, K. ; Yamazaki, F. ; et Matsuoka, M. (2006). ”Tsunami Damage Detection Using Moderate-resolution Satellite Imagery”. In Proceedings of the 2nd Asia Conference on Earthquake Engineering, CD-ROM, 11p.

[Lee et al., 2004] Lee, Y. ; Lin, Y. ; et Wahba, G. (2004). ”Multicategory Support Vector Machines : Theory and Application to the Classification of Microarray Data and Satellite Radiance Data”. Journal of the American Statistical Association, 99(465), pp. 67–81.

[Lehmann, 1998] Lehmann, T. (1998). ”A Two-Stage Algorithm for Model-Based Registration of Medical Images”. In ICPR ’98 : Proceedings of the 14th International Conference on Pattern Recognition-Volume 1, page 344, Washington, DC, USA. IEEE Computer Society.

[Lewis et Gale, 1994] Lewis, D. et Gale, W. (1994). ”A sequential algorithm for training text classifiers”. In Proceedings of SIGIR-94, 17th ACM International Conference on Research and Development in Information Retrieval, pages 3–12. Springer Verlag.

[Li et al., 2002] Li, D. ; Sui, H. ; et Xiao, P. (2002). ”Automatic change detection of geo-spatial data from imagery”. In Integrated System for Spatial Data Production, Custodian and Decision Support, (Xi’an, China), International Archives of Photogrammetry and Remote Sensing, volume 34.

[Li et al., 1995] Li, H. ; Manjunath, B. ; et S.K.Mitra (1995). ”A Contour-Based Approach to Multisensor Image Registration”. IEEE Transactions on Image Processing, 4(3), pp. 320–334.

[Li et al., 1992] Li, S. ; Kittler, J. ; et Petrou, M. (1992). ”Matching and Recognition of Road Networks from Aerial Images”. In Proceedings of the Second European Conference on Computer Vision ECCV’92, St Margherita, Italy, pages 857–861.

[Liao et al., 2005] Liao, M. ; Zhang, L. ; et Lin, H. (2005). ”Change detection in multispectral imagery from multisensor”. In IEEE Proceedings of International Geoscience And Remote Sensing Symposium 2005, volume 6, pages 3998–4001.

[Libération, 2006] Libération (2006). ”Israël punit le Liban pour enlèvements”. Libération du 13 juillet 2006. Available online at : http ://www.liberation.fr/actualite/monde/193161.FR.php (accessed 19 August 2008).

[Likar et Pernuš, 1999] Likar, B. et Pernuš, F. (1999). ”Automatic extraction of corresponding points for the registration of medical images”. Medical Physics, 26, pp. 1678–1686.

[Liu et al., 2005] Liu, Z. ; Wang, J. ; et Liu, W. (2005). ”Building extraction from high resolution imagery based on multi-scale object oriented classification and probabilistic Hough transform”. In Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, volume 4, pages 2250–2253.

[Lu et al., 2004] Lu, D. ; Mausel, P. ; Brondizio, E. ; et Moran, E. (2004). ”Change detection techniques”. International Journal of Remote Sensing, 25(12), pp. 2365–2407.

[Lush, 2007] Lush (2007). ”Lisp Universal Shell”. Available online at : http ://lush.sourceforge.net/ (accessed 19 August 2008).

[Magsud et al., 2005] Magsud, M. ; Oo, K. ; et Rajapaksha, J. (2005). ”Tsunami Disaster Damage Detection and Assessment Using High Resolution Satellite Data, GIS and GPS - Case study in Sri Lanka”. In Proceedings of the Asian Conference on Remote Sensing 2005.

[Maître et Wu, 1987] Maître, H. et Wu, Y. (1987). ”Improving dynamic programming to solve image registration”. Pattern Recognition, 20(4), pp. 443–461.

[Mallat, 1999] Mallat, S. (1999). A wavelet tour of signal processing, second edition. Academic Press.

[Mansoury et al., 2007] Mansoury, B. ; Shinozuka, M. ; et Nourjou, R. (2007). ”SAR remote sensing for urban damage assessment for Tehran”. In 5(th) International Workshop on Remote Sensing Applications to Natural Hazards, Washington DC, USA.

[Matheron, 1970] Matheron, G. (1970). ”La Théorie des variables régionalisées et ses applications”. Cahiers du Centre de Morphologie Mathématique de Fontainebleau, École des Mines de Paris, 5.

[Matsuoka et al., 2004] Matsuoka, M. ; Vu, T. ; et Yamazaki, F. (2004). ”Automated damage detection and visualisation of the 2003 Bam, Iran earthquake using high-resolution satellite images”. In Proceeding of the Symposium on application of real-time information in disaster management, Japanese Society of Civil Engineers, Tokyo, Japan, pages 75–82.

[Mayer, 1999] Mayer, H. (1999). ”Automatic object extraction from aerial imagery – A survey focusing on buildings”. Computer Vision and Image Understanding, 74(2), pp. 138–149.

[Mayunga et al., 2005] Mayunga, S. ; Zhang, Y. ; et Coleman, D. (2005). ”Semi-Automatic Building Extraction utilizing Quickbird Imagery”. In Proceedings of the Joint Workshop of ISPRS and the German Association for Pattern Recognition, Object Extraction for 3D City Models, Road Databases and Traffic Monitoring, Vienna, Austria.

[Meinel et Neubert, 2004] Meinel, G. et Neubert, M. (2004). ”A comparison of segmentation programs for high-resolution remote sensing data”. In Proceedings of the ISPRS 2004 annual conference, Istanbul, Turkey, pages 1097–1102.

[Mitomi et al., 2002] Mitomi, H. ; Matsuoka, M. ; Yamazaki, F. ; Taniguchi, H. ; et Ogawa, Y. (2002). ”Determination of the areas with building damage due to the 1995 Kobe earthquake using Airborne MSS images”. In IEEE Proceedings of International Geoscience and Remote Sensing Symposium 2002, volume 5, pages 2871–2873.

[Mitomi et al., 2001] Mitomi, H. ; Saita, J. ; Matsuoka, M. ; et Yamazaki, F. (2001). ”Automated damage detection of buildings from aerial television images of the 2001 Gujarat earthquake”. In IEEE Proceedings of International Geoscience And Remote Sensing Symposium 2001, volume 1, pages 147–149.

[Miura et al., 2005] Miura, H. ; Wijeyewickrema, A. ; et Inoue, S. (2005). ”Evaluation of Tsunami Damage in the Eastern Part of Sri Lanka due to the 2004 Sumatra Earthquake Using High-Resolution Satellite Images”. In Proceedings of the 3rd International Workshop on Remote Sensing for Post-Disaster Response, Chiba, Japan.

[Miura et al., 2006] Miura, H. ; Yamazaki, F. ; et Matsuoka, M. (2006). ”Building damage mapping of the 2006 Central Java, Indonesia earthquake using high-resolution satellite images”. In 4(th) International Workshop on Remote Sensing for Disaster Response, Cambridge, UK.

[Moss et Hancock, 1997] Moss, S. et Hancock, E. (1997). ”Multiple line-template matching with the EM algorithm”. Pattern Recognition Letters, 18(11), pp. 1283–1292.

[Müller et Zaum, 2005] Müller, S. et Zaum, D. (2005). ”Robust Building Detection in Aerial Images”. In Proceedings of the Joint Workshop of ISPRS and the German Association for Pattern Recognition, Object Extraction for 3D City Models, Road Databases and Traffic Monitoring, Vienna, Austria, volume 3, pages 143–148.

[Nevatia et Babu, 1980] Nevatia, R. et Babu, K. (1980). ”Linear feature extraction and description”. Computer Graphics and Image Processing, 13, pp. 257–269.

[Nielsen, 2007] Nielsen, A. (2007). ”The Regularized Iteratively ReweightedMAD Method for Change Detection in Multi- and Hyperspectral Data”. IEEE Transactions on Image Processing, 16(2), pp. 463–478. Available online at : http ://www2.imm.dtu.dk/pubdb/p.php ?4695 (accessed 19 August 2008).

[Nielsen et al., 1998] Nielsen, A. A. ; Conradsen, K. ; et Simpson, J. J. (1998). ”Multivariate Alteration Detection (MAD) and MAF Postprocessing in Multispectral, Bitemporal Image Data : New Approaches to Change Detection Studies”. Remote Sensing of Environment, 64(1), pp. 1–19. Available online at : http ://www2.imm.dtu.dk/pubdb/p.php ?1220 (accessed 19 August 2008).

[Niemeyer et al., 2005] Niemeyer, I. ; Nussbaum, S. ; et Canty, M. (2005). ”Automation of Change Detection Procedures for Nuclear Safeguards-Related Monitoring Purposes”. In Proceedings of the 31st International Symposium of Remote Sensing of Environment, Global Monitoring for Sustainability and Security, Saint Petersburg.

[Parker et al., 1983] Parker, J. ; Kenyon, R. V. ; et Troxel, D. E. (1983). ”Comparison of Interpolating Methods for Image Resampling”. IEEE Transactions on Medical Imaging, MI-2(1), pp. 31–39.

[Pesaresi, 2000] Pesaresi, M. (2000). ”Texture Analysis for Urban Pattern Recognition Using Fine resolution Panchromatic Satellite Imagery”. Geographical and Environmental Modelling, 4(1), pp. 43–63.

[Pesaresi et Benediktsson, 2001] Pesaresi, M. et Benediktsson, J. (2001). ”A new approach for the morphological segmentation of high-resolution satellite imagery”. IEEE Transactions on Geoscience and Remote Sensing, 39(2), pp. 309–320.

[Pesaresi et al., 2007] Pesaresi, M. ; Gerhardinger, A. ; et Kayitakire, F. (2007). ”Monitoring settlement dynamics by anisotropic textural analysis of panchromatic VHR data”. In Proceedings of the Urban Remote Sensing Joint Event, 2007, pages 1–11.

[Pisano, 2006] Pisano, F. (2006). ”The Role of Satellite observation in the Lebanese Crisis”. Diva International, (27). available online at : http ://www.divainternational.ch/spip.php ?article123 (accessed 19 August 2008).

[Platt, 1999] Platt, J. (1999). ”Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods”. In Advances in Large Margin Classifiers, pages 61–74.

[Plaza et al., 2004] Plaza, A. ; Martínez, P. ; Pérez, R. ; et Plaza, J. (2004). ”A quantitative and comparative analysis of endmember extraction algorithms from hyperspectral data”. IEEE Transactions on Geoscience and Remote Sensing, 42(3), pp. 650–663.

[Porter et Canagarajah, 1997] Porter, R. et Canagarajah, N. (1997). ”Robust rotation-invariant texture classification : wavelet, Gabor filter and GMRF based schemes”. In IEE Proceedings on Vision, Image and Signal Processing, volume 144, pages 180–188.

[Powell, 1992] Powell, M. (1992). ”Tabulation of thin plate splines on a very fine two-dimensional grid”. Technical Report DAMTP 1992/NA2, University of Cambridge, Cambridge, U.K.

[Pratt, 1974] Pratt, W. (1974). ”Correlation techniques of image registration”. IEEE Transactions on Aerospace and Electronic Systems, 10, pp. 353–358.

[EM-DAT, 2007] EM-DAT (2007). ”EM-DAT : the International Disaster Database”. Available online at : http ://www.emdat.be/ (accessed 19 August 2008).

[Radke et al., 2005] Radke, R. ; Andra, S. ; Al-Kofahi, O. ; et Roysam, B. (2005). ”Image change detection algorithms : a systematic survey”. IEEE Transactions on Image Processing, 14(3), pp. 294–307.

[Ranade et Rosenfeld, 1980] Ranade, S. et Rosenfeld, A. (1980). ”Point Pattern Matching by Relaxation”. Pattern Recognition, 12(1), pp. 269–275.

[Ranchin et Wald, 2000] Ranchin, T. et Wald, L. (2000). ”Fusion of high spatial and spectral resolution images : The ARSIS concept and its implementation”. Photogrammetric Engineering and Remote Sensing, 66(1), pp. 49–61.

[Rathje et Crawford, 2003] Rathje, E. et Crawford, M. (2003). ”Earthquake damage identification using high-resolution satellite images from the 2003 Northern Algeria earthquake”. In Workshop on application of remote sensing for disaster response, Irvine, University of California.

[Rathje et al., 2005] Rathje, E. ; Kyu-Seok, W. ; Crawford, M. ; et Neuenschwander, A. (2005). ”Earthquake damage identification using multi-temporal high-resolution optical satellite imagery”. In IEEE Proceedings of International Geoscience and Remote Sensing Symposium 2005, volume 7, pages 5045–5048.

[Rau et al., 2005] Rau, J. ; Chen, L.-C. ; Tseng, C. ; Wu, D.-H. ; et Xie, M.-H. (2005). ”Disaster detection and damage estimation using satellite imagery and land-use information”. In IEEE Proceedings of International Geoscience and Remote Sensing Symposium 2005, volume 5, pages 3607–3610.

[Rosenblatt, 1962] Rosenblatt, F. (1962). The Principles of Neurodynamics. Spartan, New York.

[Rosenfeld et Brug, 1977] Rosenfeld, A. et Brug, G. V. D. (1977). ”Coarse-Fine Template Matching”. IEEE Transactions on Systems, Man and Cybernetics, 7, pp. 104–107.

[Rottensteiner et Briese, 2002] Rottensteiner, F. et Briese, C. (2002). ”A New Method for Building Extraction in Urban Areas from High-Resolution LIDAR Data”. In International Archives of Photogrammetry and Remote Sensing - Proceedings of the Symposium ISPRS, Graz, 2002, volume 34, pages A : 295–301.

[Roux, 1996] Roux, M. (1996). ”Automatic registration of SPOT images and digitized maps”. In Proceedings of the IEEE International Conference on Image Processing, volume 1, pages 625–628.

[Rumelhart et al., 1986] Rumelhart, D. ; Hinton, G. ; et Williams, R. (1986). ”Learning internal representations by error propagation”. Parallel distributed processing : explorations in the microstructure of cognition, 1, pp. 318–362.

[Saito et Spence, 2003] Saito, K. et Spence, R. (2003). ”Rapid damage mapping for post-earthquake building damage assessment”. In Workshop on the application of remote sensing technologies to disaster response, The Martin centre for Architectural and Urban Studies, University of Cambridge.

[Saito et Spence, 2004] Saito, K. et Spence, R. (2004). ”Application of Texture Analysis to High-Resolution Optical Satellite Images for Mapping Earthquake Building Damage Distribution - A Preliminary Assessment”. In Proceedings of the 2nd International Workshop on Remote Sensing for Post-Disaster Response, Newport Beach, California.

[Saito et al., 2005] Saito, K. ; Spence, R. ; et de C Foley, T. A. (2005). ”Visual Damage Assessment Using High-Resolution Satellite Images Following the 2003 Bam, Iran, Earthquake”. Earthquake Spectra, 21(S1), pp. S309–S318.

[Sakamoto et al., 2004] Sakamoto, M. ; Takasago, Y. ; Uto, K. ; Kakumoto, S. ; Kosugi, Y. ; et Doihara, T. (2004). ”Automatic detection of damaged area of Iran earthquake by high-resolution satellite imagery”. In IEEE Proceedings of International Geoscience and Remote Sensing Symposium 2004, volume 2, pages 1418–1421.

[Salton et Buckley, 1990] Salton, G. et Buckley, C. (1990). ”Improving retrieval performance by relevance feedback”. Journal of the American Society for Information Science, 41(4), pp. 288–297.

[Sarabandi et al., 2004] Sarabandi, P. ; Yamazaki, F. ; Matsuoka,M. ; et Kiremidjian, A. (2004). ”Shadow detection and radiometric restoration in satellite high resolution images”. In IEEE Proceedings of International Geoscience and Remote Sensing Symposium 2004, volume 6, pages 3744–3747.

[Sarti et al., 2003] Sarti, F. ; Inglada, J. ; et Bessis, J. (2003). ”The two emergencies of ’El Salvador’ in the frame of the international Charter ’Space and Major Disasters” ’. In IEEE Proceedings of Geoscience and Remote Sensing Symposium 2003, volume 3, pages 1543–1545.

[Scharstein et R.Szeliski, 2002] Scharstein, D. et R.Szeliski (2002). ”A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms”. International Journal of Computer Vision, 47(1-3), pp. 7–42.

[Schohn et Cohn, 2000] Schohn, G. et Cohn, D. (2000). ”Less is more : Active learning with support vector machines”. In Proceedings of the Seventemth International Conference on Machine Learning, pages 839–846.

[SCOR, 2004] SCOR (2004). ”Rapport annuel 2004”. Available online at : http://www.scor.com/www/index.php ?id=80 (accessed 19 August 2008).

[Sertel et al., 2007] Sertel, E. ; Kaya, S. ; et Curran, P. (2007). ”Use of Semivariograms to Identify Earthquake Damage in an Urban Area”. IEEE Transactions on Geoscience and Remote Sensing, 45(6), pp. 1590–1594.

[Sester et al., 1998] Sester, M. ; Hild, H. ; et Fritsch, D. (1998). ”Definition of ground-control features for image registration using GIS data”. In Proceedings of the Symposium on Object Recognition and Scene Classification from Multispectral and Multisensor Pixels CD-ROM, volume 32, pages 537–543.

[Shackelford et al., 2004] Shackelford, A. ; Davis, C. ; et Xiangyun, W. (2004). ”Automated 2-D building footprint extraction from high-resolution satellite multispectral imagery”. In IEEE Proceedings of International Geoscience and Remote Sensing Symposium 2004, volume 3, pages 1996–1999.

[Shin et al., 1997] Shin, D. ; Pollard, J. ; et J.P.Muller (1997). ”Accurate Geometric Correction of ATSR Images”. IEEE Transactions on Geoscience and Remote Sensing, 35(4), pp. 997–1006.

[Singh, 1989] Singh, A. (1989). ”Digital change detection techniques using remotely-sensed data”. International Journal of Remote Sensing, 10(6), pp. 989–1003.

[Stasolla et al., 2006] Stasolla, M. ; Gamba, P. ; Dell’Acqua, F. ; et Rathje, E. (2006). ”Matching spectral with spatial analysis to improve building damage recognition in VHR images”. In 4(th) International Workshop on Remote Sensing for Disaster Response, Cambridge, UK.

[Stockman et al., 1982] Stockman, G. C. ; Kopstein, S. ; et Bennet, S. (1982). ”Matching images to models for registration and object detection via clustering”. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-4(3), pp. 229–41.

[S.Tong et Koller, 2001] S.Tong et Koller, D. (2001). ”Support Vector Machine Active Learning with Applications to Text Classification”. Journal of Machine Learning Research, 2, pp. 45–66.

[Stramondo et al., 2006] Stramondo, S. ; Bignami, C. ; Chini, M. ; Pierdicca, N. ; et Tertulliani, A. (2006). ”Satellite radar and optical remote sensing for earthquake damage detection : results from different case studies”. International Journal of Remote Sensing, 27(20), pp. 4433–4447.

[Takeuchi et Yasuoka, 2005] Takeuchi, W. et Yasuoka, Y. (2005). ”Development of normalized vegetation, soil and water indices derived from satellite remote sensing data”. Journal of the Japan Society of Photogrammetry and Remote Sensing, 43, pp. 7–19.

[Taza et Suen, 1989] Taza, A. et Suen, C. (1989). ”Discrimination of Planar Shapes Using Shape Matrices”. IEEE Transactions on Systems, Man and Cybernetics, 19(5), pp. 1281–1289.

[Thevenaz et Unser, 2000] Thevenaz, P. et Unser, M. (2000). ”Optimization of mutual information for multiresolution image registration”. IEEE Transactions on Image Processing, 9(12), pp. 2083–2099.

[Thomas, 2006] Thomas, C. (2006). Fusion d’images de résolutions spatiales différentes. PhD thesis, Doctorat Informatique et Temps Réel, Robotique, Automatique, École Nationale Supérieure des Mines de Paris - CEP Centre Énergétique et Procédés, ENSMP.

[Thönnessen et al., 2005] Thönnessen, U. ; Hofele, G. ; et Middelmann, W. (2005). ”Change detection in satellite images”. In Proceedings of the SPIE Signal Processing, Sensor Fusion, and Target Recognition XIV, volume 5809, pages 197–207.

[Tian et al., 2003] Tian, J. ; Wang, J. ; et Shi, P. (2003). ”Urban building boundary extraction from Ikonos imagery”. In Proceedings of the 25e Symposium canadien sur la télédétection, Montréal.

[Toll et al., 1980] Toll, D. ; Royal, J. ; et Davis, J. (1980). ”Urban area up-date procedures using Landsat data”. In Proceedings of the Fall Technical Meeting of the American Society of Photogrammetry, Niagra Falls, Canada, pages RS–E1–17.

[Ton et Jain, 1989] Ton, J. et Jain, A. (1989). ”Registering Landsat images by point matching”. IEEE Transactions on Geoscience and Remote Sensing, 27(5), pp. 642–651.

[UNOSAT, 2007] UNOSAT (2007). ”United Nations Institute for Training and Research (UNITAR) Operational Satellite Applications Programme”. Available online at : http://unosat.web.cern.ch/ (accessed 19 August 2008).

[Vanderbrug et Rosenfeld, 1977] Vanderbrug, G. et Rosenfeld, A. (1977). ”Two-Stage Template Matching”. IEEE Transactions on Computers, C-26(4), pp. 384–393.

[Vapnik, 1995] Vapnik, N. (1995). The nature of statistical learning theory. Springer-Verlag New York, Inc., New York, NY, USA.

[Vapnik, 1998] Vapnik, N. (1998). Statistical Learning Theory. Springer-Verlag New York, Inc., New York, NY, USA.

[Vasileisky et al., 1998] Vasileisky, A. ; Zhukov, B. ; et Berger, M. (1998). ”Automated image coregistration based on linear feature recognition”. In Proceedings of the 2nd Conference on Fusion of Earth Data, Sophia Antipolis, France, pages 59–66.

[Verdeil, 2006] Verdeil, E. (2006). ”Reconstruire encore... Le bilan des destructions de la guerre de 2006 au Liban”. Available online at : http://halshs.archivesouvertes.fr/docs/00/09/64/66/PDF/Fabrique_Verdeil_cartes.pdf (accessed 19 August 2008).

[Vu et al., 2004] Vu, T. ; Matsuoka, M. ; et Yamazaki, F. (2004). ”Shadow Analysis in Assisting Damage Detection due to Earthquakes from QuickBird Imagery”. In Proceedings of the 20th Congress of International Society for Photogrammetry and Remote Sensing, volume B7, pages 607–610.

[Wang et al., 1983] Wang, C. ; Sun, H. ; Yada, S. ; et Rosenfeld, A. (1983). ”Some experiments in relaxation image matching using corner features”. Pattern Recognition, 16(2), pp. 167–182.

[Wang et Chen, 1997] Wang, W.-H. et Chen, Y.-C. (1997). ”Image registration by control points pairing using the invariant properties of line segments”. Pattern Recognition Letters, 18(3), pp. 269–281.

[Wang et al., 2005] Wang, Z. ; Ziou, D. ; Armenakis, C. ; Li, D. ; et Li, Q. (2005). ”A comparative analysis of image fusion methods”. IEEE Transactions on Geoscience and Remote Sensing, 43(6), pp. 1391–1402.

[Weston et Watkins, 1998] Weston, J. et Watkins, C. (1998). ”Multi-class Support Vector Machines”. Techinical report, Royal Holloway, University of London, Egham, UK.

[Weydahl, 1991] Weydahl, D. (1991). ”Change detection in SAR images”. In IEEE Proceedings of International Geoscience and Remote Sensing Symposium 1991, pages 1421–1424.

[Wiemker et al., 1996] Wiemker, R. ; Rohr, K. ; Binder, L. ; Sprengel, R. ; et Stiehl, H. (1996). ”Application of Elastic Registration to Imagery from Airborne Scanners”. In Proceedings of the XVIII Congress of the International Society for Photogrammetry and Remote Sensing ,ISPRS’96, volume XXXI part B4, pages 949–954, Vienna.

[Xinjian et al., 2004] Xinjian, S. ; Jiahang, L. ; et Jingyuan, Y. (2004). ”Extracting damaged building information from single remote sensing images of post-earthquake”. In IEEE Proceedings of International Geoscience and Remote Sensing Symposium 2004, volume 7, pages 4496–4497.

[Yamazaki et al., 2004] Yamazaki, F. ; Kouchi, K. ; Matsuoka, M. ; Kohiyama, M. ; et Muraoka, N. (2004). ”Damage Detection from High-Resolution Satellite Images for the 2003 Boumerdes, Algeria Earthquake”. In 13th World Conference on Earthquake Engineering, International Association for Earthquake Engineering, Vancouver, British Columbia, Canada, page 13.

[Yamazaki et al., 2005] Yamazaki, F. ; Yano, Y. ; et Matsuoka, M. (2005). ”Visual Damage Interpretation of buildings in the Bam, Iran, earthquake”. Earthquake Spectra, 21(S1), pp. S329–S336.

[Yeend, 1997] Yeend, J. (1997). ”Coastal natural hazards analysis of risk exposures”. In MTS/IEEE Conference Proceedings OCEANS ’97, volume 1, pages 208–212.

[Yusuf et al., 2001] Yusuf, Y. ; Matsuoka, M. ; et Yamazaki, F. (2001). ”Damage detection from Landsat-7 satellite images for the 2001 Gujarat, India earthquake”. In 22nd Asian conference on remote sensing, Singapore.

[Zhang et al., 2005] Zhang, J. ; Wang, X. ; Chen, T. ; et Zhang, Y. (2005). ”Change detection for the urban area based on multiple sensor information fusion”. In IEEE Proceedings of International Geoscience and Remote Sensing Symposium 2005, volume 1.

[Zheng et Chellappa, 1992] Zheng, Q. et Chellappa, R. (1992). ”A computational vision approach to image registration”. In Proceedings of the 11th IAPR International Conference on Pattern Recognition, volume 1, pages 193–197.

[Zitová et Flusser, 2003] Zitová, B. et Flusser, J. (2003). ”Image registration methods : a survey”. Image and vision computing, 24, pp. 997–1000.

[Zitová et al., 2002] Zitová, B. ; Flusser, J. ; et Sroubek, F. (2002). ”Application of image processing for the conservation of the medieval mosaic”. In Proceedings of the International Conference on Image Processing, pages 993–996.

Table of content

1 Introduction 1

2 Dégâts et télédétection 11

2.1 Catastrophes majeures - 11

2.1.1 Catastrophes naturelles - 11

2.1.2 Catastrophes d’origine humaine - 11

2.1.3 Dégâts - 12

2.1.4 Besoins pour gérer la crise - 14

2.2 Quantification de dégâts - 14

2.2.1 Objectifs - 14

2.2.2 Acteurs - 14

2.2.3 Échelles de quantification de dégâts - 16

2.3 Quantification de dégâts en télédétection - 18

2.3.1 Imagerie optique panchromatique et multispectrale - 19

2.3.2 Imagerie radar - 24

2.3.3 Imagerie hyperspectrale - 26

2.4 État de l’art des méthodes existantes - 27

2.4.1 Analyse multidate - 27

2.4.2 Analyse monotemporelle - 44

2.4.3 Conclusion sur l’état de l’art - 49

3 Protocole d’évaluation de la qualité des résultats 53

3.1 Validation - 53

3.2 Bases de données - 53

3.2.1 Base de données de référence - 53

3.2.2 Base de données des décalages - 57

3.2.3 Base de données d’évaluation et protocole - 57

3.3 Cas-tests - 58

3.3.1 Séisme de Boumerdès (Algérie) - 58

3.3.2 Séisme de Bam (Iran) - 60

3.3.3 Explosion à Ryongchon (Corée du Nord) - 63

3.3.4 Séisme de Muzaffarabad (Pakistan) - 65

3.3.5 Bombardements de Beyrouth (Liban) - 66

3.4 Étapes suivies pour le développement de la méthode - 68

4 Recalage géométrique 69

4.1 État de l’art des méthodes de recalage - 70

4.2 Méthode de recalage proposée - 72

4.2.1 Recalage des images - 72

4.2.2 Recalage des toits des bâtiments - 73

4.3 Résultats et évolutions - 77

4.3.1 Score d’appariement - 78

4.3.2 Détermination de la seconde zone de recherche et apport de celle-ci - 81

4.3.3 Analyse des résultats finaux de recalage - 83

4.3.4 Qualité du recalage en fonction de la modalité des images - 84

4.4 Identification des problèmes d’application - 90

4.5 Adéquation de la méthode de recalage proposée à la quantification de dégâts - 91

5 Quantification de dégâts 95

5.1 Indices de dégâts - 95

5.1.1 Corrélation - 95

5.1.2 Analyse de texture - 96

5.2 Classifieurs - 99

5.2.1 Réseau de neurones - Perceptron multicouches - 99

5.2.2 Support Vector Machine - 100

5.2.3 Apprentissage - 103

5.3 Résultats des méthodes proposées - 104

5.3.1 Taille de l’ensemble d’apprentissage - 105

5.3.2 Classification à partir des indices de corrélation - 106

5.3.3 Classification à partir des indices de texture - 112

5.3.4 Comparaison avec les résultats attendus - 116

5.3.5 Comparaison des résultats obtenus avec ceux trouvés dans la littérature . . . 116

5.3.6 Performance de classification en fonction de la modalité des images - 118

6 Conclusion 123

Liste des acronymes 128

Bibliographie 129

Annexe 145

Statistiques de consultation

Repository Staff Only: edit this item