Quantitative Assessment Of Building Damage In Urban Area Using Very High Resolution Images

Very high resolution images are particularly well adapted to damage assessment methodology in urban area because on one hand it allows an analysis focused on the buildings solely through an object-oriented analysis, and on the other hand it permits a quantitative evaluation of this damage assessment using a visually established ground truth. We propose in this paper a method of damage assessment that uses these two benefits. First an original object oriented approach to register the images is presented. Then a simple and fast damage assessment method based on correlation is proposed and tested on the test-case of the earthquake of Bam in December 2003. Each building of a test-area is classified using Support Vector Machines. The performance of the method in each case is evaluated thanks to a manually constructed reference database that uses the European Macroseismic Scale. As a result, 75% of buildings are well classified among four different EMS damage grades.

[1]  G. Bitelli,et al.  IMAGE CHANGE DETECTION ON URBAN AREA : THE EARTHQUAKE CASE , 2004 .

[2]  Ansgar Brunn,et al.  3rd International Workshop: Automatic Extraction of Man-Made Objects from Aerial and Space Images , 2001, Künstliche Intell..

[3]  Emmanuel P. Baltsavias,et al.  Object extraction and revision by image analysis using existing geodata and knowledge: current status and steps towards operational systems☆ , 2004 .

[4]  Tara C. Hutchinson,et al.  Urban Damage Estimation Using Statistical Processing of Satellite Images , 2007 .

[5]  Yukio Kosugi,et al.  Automatic detection of damaged area of Iran earthquake by high-resolution satellite imagery , 2004, IGARSS 2004. 2004 IEEE International Geoscience and Remote Sensing Symposium.

[6]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  M. Matsuoka,et al.  DAMAGE DETECTION FROM HIGH-RESOLUTION SATELLITE IMAGES FOR THE 2003 BOUMERDES , ALGERIA EARTHQUAKE , 2002 .

[8]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[9]  B. Guindon,et al.  Computer-Based Aerial Image Understanding: A Review and Assessment of its Application to Planimetric Information Extraction from Very High Resolution Satellite Images , 1997 .

[10]  Michel Roux,et al.  Building detection from high-resolution color images , 1998, Remote Sensing.

[11]  U. Thonnessen,et al.  Change detection in satellite images , 2005, SPIE Defense + Commercial Sensing.

[12]  M. Neubert,et al.  A COMPARISON OF SEGMENTATION PROGRAMS FOR HIGH RESOLUTION REMOTE SENSING DATA , 2004 .

[13]  Curt H. Davis,et al.  Automated 2-D building footprint extraction from high-resolution satellite multispectral imagery , 2004, IGARSS 2004. 2004 IEEE International Geoscience and Remote Sensing Symposium.

[14]  Zhiqiang Chen,et al.  Urban damage estimation using statistical processing of satellite images: 2003 Bam, Iran earthquake , 2005, IS&T/SPIE Electronic Imaging.

[15]  Curt H. Davis,et al.  Automated Building Extraction from High-Resolution Satellite Imagery in Urban Areas Using Structural, Contextual, and Spectral Information , 2005, EURASIP J. Adv. Signal Process..

[16]  K. Kouchi.  Damage Detection Based on Object-based Segmentation and Classification from High-resolution Satellite Images for the 2003 Boumerdes , Algeria Earthquake , 2005 .

[17]  Helmut Mayer,et al.  Automatic Object Extraction from Aerial Imagery - A Survey Focusing on Buildings , 1999, Comput. Vis. Image Underst..

[18]  M. Matsuoka,et al.  Visual Damage Interpretation of Buildings in Bam City using QuickBird Images following the 2003 Bam, Iran, Earthquake , 2005 .

[19]  Taejung Kim,et al.  Toward an automated building detection system from satellite data , 1995, Remote Sensing.