Bayesian building extraction from high resolution polarimetric SAR data

Building extraction from high resolution Synthetic Aperture Radar (SAR) images can benefit from modelling the interaction of several elements in urban scene. This paper proposes a Bayesian approach to exploit the interplay. The appearances of buildings in SAR images are dependent on their orientation angles. We estimate the orientation angles of buildings by supervised learning. The knowledge of other object classes could contribute to the building detection. We extract surface evidence of major object classes. The integration of angle estimation, building detection and surface classes provides promising results.