Stochastic geometrical model and Monte Carlo optimization methods for building reconstruction from InSAR data

In the reconstruction process, according to the radar configuration and the building geometry, a 3D building hypothesis is mapped to the SAR image plane and decomposed to feature regions such as layover, corner line, and shadow. Then, the statistical properties of intensity, interferometric phase and coherence of each region are explored respectively, and are included as region terms. Roofs are not directly considered as they are mixed with wall into layover area in most cases. When estimating the similarity between the building hypothesis and the real data, the prior, the region term, together with the edge term related to the contours of layover and corner line, are taken into consideration. In the optimization step, in order to achieve convergent reconstruction outputs and get rid of local extrema, special transition kernels are designed. The proposed framework is evaluated on the TanDEM-X dataset and performs well for buildings reconstruction.