A Novel PU Method for Mining Area Based on Edge Detection Using the SegNet Model

Due to the large deformation gradient caused by mining, it is easy to cause serious incoherence phenomenon in radar interferometry, and the traditional phase unwrapping (PU) method is limited in this case. To solve this problem, a novel PU method for mining area based on edge detection using the SegNet model is proposed for mining subsidence basins with large deformation. First, SegNet network was used to extract the edge information of the subsidence basin in the mining area. Then, the edges were refined and connected by the Zhang-Suen thinning method and regional growth method, respectively. Finally, PU was completed by the determined phase jump variables. Simulated interferograms with different signal-to-noise ratio (SNR) and two real interferograms with different interference qualities are selected for experiments. Compared with the three traditional PU methods and two deep learning PU methods, the proposed model has higher accuracy and better robustness. When the SNR is 1 and 4, the unwrapping error distribution area of the proposed method is the smallest, and the PU result is more close to the real situation in the interferogram of real mining area. The novel two-step PU method effectively solves the problem that the traditional PU method is seriously affected by noise and large deformation.