Change detection for earthquake damage assessment in built-up areas using very high resolution optical and SAR imagery

Information on the impact of catastrophic events (e.g. earthquakes) can be derived from suitable satellite imagery by comparing data from a chosen reference before the event (pre-event) to imagery acquired shortly after the event (post-event). In this paper, we propose a novel method that detects buildings destroyed in an earthquake using pre-event very high resolution (VHR) multispectral and post-event detected VHR synthetic aperture radar (SAR) imagery. The core concept of the proposed method is the evaluation of the presence of the predicted undamaged building SAR signature in the post-event SAR scene. The decision if a building belongs to the damaged or undamaged building class is performed with a Bayesian classifier, trained either in a supervised or unsupervised manner. We show the results of the proposed method using VHR TerraSAR-X and COSMO-SkyMed, as well as VHR optical data for a subset of the town of Yingxiu, China, which was heavily damaged in the 2008 Sichuan earthquake.