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The availability of very high resolution (VHR) satellite images has made the analysis of remotely sensed data an increasingly effective tool for the extraction of urban building damage caused by earthquakes. In this study, we proposed a novel method to extract collapsed buildings from bi-temporal VHR images using object-level homogeneity index (OHI) and object histogram. The OHI, which is calculated using an improved spatial homogeneity index of pixel, quantifies internal homogeneity of image object generated from image segmentation. The object histogram quantifies the spectral variability of image object. Ground objects that are intact and significantly different from collapsed buildings, such as vegetation and non-vegetated homogeneous areas, were first extracted from post-event VHR image using OHI and normalized difference vegetation index and were masked out. Collapsed buildings were then extracted from bi-temporal images of the remaining areas using object histogram and a curve matching method, multi-reference spectral angle mapper. The proposed method was evaluated and compared to two existing methods using bi-temporal QuickBird images over Bam, Iran, which was heavily hit by an earthquake in 2003. The experimental results showed that the proposed method outperformed the two comparative methods, with the increase of 11.38 and 5.65% in overall accuracy, and 14.27 and 7.83% in F-score, respectively. The proposed method provides a fast and reliable method for extraction of collapsed buildings.

Extraction of Earthquake-Induced Collapsed Buildings From Bi-Temporal VHR Images Using Object-Level Homogeneity Index and Histogram