Automated Damage Assessment from High Resolution Remote Sensing Imagery

by Jim Thomas Estimating the extent of damage caused by natural disasters is necessary for implementing effective recovery measures. Damage detection from high-resolution satellite or aerial imagery for post-disaster analysis has been a major research effort in the past decade. A careful analysis of images from before and after an event facilitates rapid detection and assessment of building damage. This work presents a first-of-its-kind system for automatic damage assessment. The proposed framework for damage estimation consists of three steps. First the preevent and post-event images are registered automatically. A SURF-based feature extraction and matching technique is used for automatic image registration. Next, the objects of interests such as buildings are extracted from pre-storm images. A novel robust algorithm for building detection is proposed and evaluated. Lastly, change detection is performed and damage is classified using supervised learning algorithms. Relevant features that reflect the spectral properties of damaged buildings are identified and used to classify the damage level into various states. To my parents with much love and gratitude

[1]  Manfred Ehlers,et al.  Photogrammetric Engineering and Remote Sensing , 2007 .

[2]  Jean Serra,et al.  Image Analysis and Mathematical Morphology , 1983 .

[3]  Christopher G. Harris,et al.  A Combined Corner and Edge Detector , 1988, Alvey Vision Conference.

[4]  Fumio Yamazaki,et al.  Applications of remote sensing and GIS for damage assessment , 2001 .

[5]  Christopher F. Barnes,et al.  Hurricane Disaster Assessments With Image-Driven Data Mining in High-Resolution Satellite Imagery , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[6]  Chunhong Pan,et al.  A Region-Based Approach to Building Detection in Densely Build-Up High Resolution Satellite Image , 2006, 2006 International Conference on Image Processing.

[7]  Hanan Samet,et al.  A general approach to connected-component labeling for arbitrary image representations , 1992, JACM.

[8]  Carol J. Friedland,et al.  Residential building damage from hurricane storm surge: proposed methodologies to describe, assess and model building damage , 2009 .

[9]  Hans-Hellmut Nagel,et al.  New likelihood test methods for change detection in image sequences , 1984, Comput. Vis. Graph. Image Process..

[10]  Victor J. D. Tsai,et al.  A comparative study on shadow compensation of color aerial images in invariant color models , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[11]  Hayder Radha,et al.  Computational Photography: Methods and Applications , 2010, J. Electronic Imaging.

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

[13]  Zhengyou Zhang,et al.  Flexible camera calibration by viewing a plane from unknown orientations , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[14]  Cem Ünsalan,et al.  Urban-Area and Building Detection Using SIFT Keypoints and Graph Theory , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[15]  Ramesh C. Jain,et al.  Illumination independent change detection for real world image sequences , 1989, Comput. Vis. Graph. Image Process..

[16]  Badrinath Roysam,et al.  Image change detection algorithms: a systematic survey , 2005, IEEE Transactions on Image Processing.

[17]  Masashi Matsuoka,et al.  AUTOMATED DAMAGE DETECTION AND VISUALIZATION OF THE 2003 BAM, IRAN EARTHQUAKE USING HIGH-RESOLUTION SATELLITE IMAGES , 2004 .

[18]  David G. Lowe,et al.  Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[19]  George Loizou,et al.  Computer vision and pattern recognition , 2007, Int. J. Comput. Math..

[20]  Ramakant Nevatia,et al.  Detection and Modeling of Buildings from Multiple Aerial Images , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[21]  Andrew W. Fitzgibbon,et al.  An Experimental Comparison of Range Image Segmentation Algorithms , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[22]  Christopher Hunt,et al.  Notes on the OpenSURF Library , 2009 .

[23]  S. Tanathong,et al.  Object oriented change detection of buildings after the Indian ocean tsunami disaster , 2008, 2008 5th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology.