Unsupervised SAR Image Change Detection via Structure Feature-Based Self-Representation Learning

Unsupervised low-rank matrix decomposition (LRMD) theory, leveraging inherent structural features of images, has demonstrated significant potential for synthetic aperture radar (SAR) image change detection (CD) under label scarcity. However, conventional methods rely on static sparsity priors, which inadequately model the spatial diversity and dynamic complexity of changes, leading to great decomposition errors. To address this limitation, we redefine CD within a multiview framework and propose a structurally diversified self-representation learning model. By jointly enforcing low-rank and sparse constraints on the coefficient matrix, our approach enhances the global consistency and local continuity of change information, effectively rectifying representation errors in complex change patterns. Furthermore, a robust background suppression mechanism is integrated to mitigate speckle noise and pseudo-changes, improving the discriminability between changed and unchanged regions. To refine ambiguous boundaries, an unsupervised classification refinement module is developed, calibrating transitional samples via nonlinear regression-based feature projection without labeled data. The entire framework operates without supervision, eliminating dependence on annotated samples. Extensive experiments on six bitemporal and three extended multitemporal datasets validate the effectiveness and superiority of the proposed method. The source code will be made available at https://github.com/95xiaoli/S3CD