SAR Image Change Detection Via UR-ISTA

Abstract

In this paper, we propose a novel dictionary learning model based on the idea of deep unrolling to deal with the synthetic aperture radar (SAR) image change detection problem. Deep unrolling aims at unrolling the iterative algorithm into a trainable neural network. In our proposed method, the idea of unrolling is applied to the Iterative Shrinkage Threshold Algorithm (ISTA), which is one of classic algorithms for dictionary learning. Then, the proposed Unrolling Iterative Shrinkage Threshold Algorithm (UR-ISTA), is utilized to obtain the sparse codes of the difference results. Finally, the change map is computed by k-means clustering algorithm. The advantage of UR-ISTA method is relatively low time cost, which makes it possible to add dictionary updating step to calculate specific feature vectors. Experimental results show that the proposed approach has superior accuracy and precision compared to several well-known change detection techniques. The proposed UR-ISTA algorithm shows more robustness than another sparse representation algorithm.