Object-based change detection for individual buildings in SAR images captured with different incidence angles

Change detection of two SAR images captured with different incidence angles is a difficult task but may be important in urgent situations like earthquakes. This paper presents a simulation based algorithm to detect negative changes of buildings in two high resolution SAR images captured with different incidence angles. The analysis is supported by LiDAR data where individual wall models are extracted and are simulated to predict their shape in the SAR images. Afterwards, point signatures within the layover areas are extracted, converted to the same geometry, and are compared with a buffer change detection algorithm. The proposed method is tested for several buildings (in Munich city center) imaged in TerraSAR-X spotlight mode.