Building detection from high-resolution PolSAR data at the rectangle level by combining region and edge information

We propose a new approach at the rectangle feature level to extract buildings from high-resolution polarimetric synthetic aperture radar (PolSAR) data, using both region-based and edge-based information. The first step employs low-level detectors to provide raw region and edge information of the scene. In the second step, the rectangle features are initially extracted from the edge detection results, and further optimized to best fit the rough region-based building detection results. In the last step, a novel Markov random field (MRF) framework for rectangles is proposed, in which the data energy term of rectangles is defined from the region information while the smoothness term is defined according to the contextual prior knowledge about the buildings. Under this framework, the building rectangles are identified from the optimized rectangle candidates by minimizing the total energy. The effectiveness of the proposed method is verified using the real fully PolSAR data.