Event-driven Change-detection in Urban Environments Using Sar

SAR sensors are able to operate under nearly all weather conditions, at daylight or in the night. This is especially beneficial for event-driven applications, like time-critical change-detection for disaster management. Unfortunately, SAR systems suffer from occlusions and ambiguities, especially in urban areas. Additionally, due to layover and foreshortening effects, the geo-referencing of SAR images, which is a prerequisite for a change detection, is problematic in urban areas. The initial geo-referencing of the SAR data can be automatically improved using street vectors. By comparing image chips from the SAR image, to the transformed street data, correspondences can be found, which can be used for geo-referencing. Change-detection, combining newly acquired SAR images with other types of data, should use 3D-groundtruth, due to the side-looking property of SAR sensors. The result of the SAR simulation, based on 3D-data, is compared to the real SAR image. It is possible to even use simple models and assumptions for the simulation, like for example lambertian reflection.