Sar Simulation Based Change Detection with High-resolution Sar Images in Urban Environments

Combined processing using different sensor types, i.e. for applications like change detection, requires a good geo-referencing. Furthermore the individual sensor properties have to be taken into account. SAR systems are side-looking and run-time systems. They suffer from occlusions and ambiguities especially in urban areas. Additionally layover and shadow effects disturb the geo-referencing of SAR images in urban areas, which is a prerequisite for a successful change detection. An improved geo-referencing can be achieved by simulating 3D-city models or street datasets using a SAR simulator and comparing the simulated image to the real image. Correspondences between simulated and real image can be used for geo-referencing the image according to the coordinates of the 3D-city model or street dataset. The geo-referenced dataset can afterwards be used for change detection analysis. SAR images represent a side-view of the three dimensional world. An automated change detection using SAR images should take this fact into consideration and therefore should use 3D-models as reference for the change-detection. These models are simulated and the simulated image is compared to the geo-referenced image, revealing changes between the simulated model and the real image.