SAR image change detection based on sparse representation and a capsule network

Abstract

ABSTRACT Synthetic aperture radar (SAR) image change detection has good application prospects in the domain of remote sensing image processing. However, the accuracy of change detection is negatively affected by the inherent speckle noise in SAR images. To solve this problem, a method based on sparse representation (SR) and a capsule network is proposed. Firstly, sparse features of the difference image (DI) are extracted by the SR method. Secondly, a lightweight capsule network (L-CapsNet) is constructed, which is used to mine the spatial relationship between features. The network classifies the changed and unchanged pixels. Finally, the change map (CM) is generated. The proposed method can obtain more robust features while reducing the influence of speckle noise. Experiments are performed on four SAR data sets to compare the proposed method with related methods. The results confirm the superior performance of the proposed method.