A global patch similarity-based graph for unsupervised SAR image change detection

Abstract

ABSTRACT This letter presents a novel method for synthetic aperture radar (SAR) image change detection using the global patch similarity-based graph (GPSG). First, the SAR image is divided into a number of square patches, which are then vectorized and stacked to form a global patch matrix (GPM). The GPSG is constructed by connecting the global similar patches that are selected through traversing the GPM. With the support of GPSG, the second-order random walk matrix is designed to widely aggregate the attributes of patches within the two-hop global neighbourhood. The difference image with good separability can be generated by comparing bitemporal patches and their aggregated attributes. Finally, the Otsu thresholding is adopted to obtain the change map showing changed and unchanged portions. Experiments conducted on three real SAR datasets demonstrate the superiority of the proposed GPSG method in terms of both robustness and accuracy for change detection.