Advanced Methods For Building Information Extraction From Very High Resolution SAR Data To Support Emergency Response

Rapid damage assessment after natural disasters (e.g. earthquakes, floods) and violent conflicts (e.g. war-related destruction) is crucial for initiating effective emergency response actions. Remote sensing satellites equipped with multispectral and Synthetic Aperture Radar (SAR) imaging sensors can provide vital information due to their ability to map affected areas of interest with high geometric precision and in an uncensored manner. The new spaceborne Very High Resolution (VHR) SAR sensors onboard the TerraSAR-X and COSMO-SkyMed satellites can achieve spatial resolutions in the order of 1 m. In VHR SAR data, features from individual urban structures (like buildings) can be identified in their characteristic settings in urban settlement patterns. This thesis presents novel techniques to support emergency response after catastrophic events using latest generation earth observation imagery. In this context, the potential and limits of VHR SAR imagery for extracting information about individual buildings in an (semi-) automatic manner is investigated. The following main novel contributions are presented. First, we investigate the potential of the characteristic double bounce of a building in VHR SAR imagery to be exploited in automatic damage assessment techniques. In particular, we analyze empirically the relation between the double bounce effect and the aspect angle. Then, we propose a radar imaging simulator for urban structures, which is based on an adapted ray tracing procedure and a Lambertian-specular mixture model, emphasizing the geometrical effects of the scattering. Furthermore, we propose an approach to the height estimation of buildings from single detected SAR data. It is based on a "hypothesis generation - rendering - matching" procedure, where a series of hypotheses are generated and rendered by the previously introduced radar imaging simulator in order to compare the simulations with the actual VHR SAR data. Moreover, we present a method that detects buildings destroyed in an earthquake using pre-event VHR optical and post-event detected VHR SAR imagery. This technique evaluates the similarity between the predicted signature of the intact building in the post-event SAR scene and the actual scene to distinguish between damaged and undamaged buildings. Finally, we address the practical requirements of rapid emergency response scenarios by proposing an IT system infrastructure that enables collaborative and distributed geospatial data processing and on-demand map visualization. The effectiveness of all proposed techniques is confirmed by quantitative and qualitative experimental results obtained on airborne and spaceborne VHR SAR imagery.