Rapid Damage Detection in the Bam Area Using Multitemporal SAR and Exploiting Ancillary Data

In this paper, the problem of rapid earthquake damage detection in urban areas using multitemporal synthetic aperture radar data is addressed. It is shown that the combination of intensity and phase features enhances the damage pattern extracted from the data temporal stack using a spatially aware classifier. Moreover, the use of ancillary data, easily available for urban areas, further improves the accuracy by discarding uninteresting parts of the scene and forcing homogeneous classification within city blocks to avoid "class-blurring" effects consequential to the window-based computation of relevant measures. The procedure is validated based on results for the town of Bam, Iran, and compared with ground-based survey maps