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unrban-building-damage-detection-by-PJLi.ppt
1. Urban Building Damage Detection From Very High Resolution Imagery By One-Class SVM and Shadow Information Peijun Li, Benqin Song and Haiqing Xu Peking University, P. R. China Email: pjli@pku.edu.cn
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3. Introduction Prompt and accurate detection of damage to urban infrastructure caused by disasters (e.g. earthquakes) Very high resolution satellite (VHR) images Automated detection and assessment methods: urgently required Fusion of different sensor data, use of single source data Existing methods (VHR optical data): mostly spectral data only, Objective : use of shadow change information to refine results
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5. Flowchart of method Bitemporal images Bitemporal image segmentation Initial building damage detection: OCSVM Shadow and its change detection Result refinement Final result Accuracy assessment
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7. Multilevel segmentation method (Multichannel watershed transformation + dynamics of contours) Li, P., Guo, J., Song, B. and Xiao, X., 2011, A multilevel hierarchical image segmentation method for urban impervious surface mapping using very high resolution imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 4(1), 103-116.
8. Initial building damage detection using OCSVM Building damage (‘building to non-building’): target class Multi-date composite classification: One-class Support Vector Machine (OCSVM) – one-class classifier
15. Result comparison Building damage detection results by different methods (all in %) Damaged Undamaged OA Kappa PA UA PA UA Spectral only 69.63 66.41 84.82 86.63 80.25 53.71 Proposed method 63.73 84.75 95.06 84.44 85.88 63.25
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