Signed Graph-Based Image Transformation for Heterogeneous Change Detection

Heterogeneous change detection (HeCD) is a highly valuable yet challenging task in remote sensing. To enable the comparison of heterogeneous images with different imaging mechanisms, some structural consistency-based image transformation methods have been proposed, which utilize graph models to represent image structures and constrain the transformed images and original images to have the same structural characteristics on the graph model. Consequently, these graph-based methods face two challenges: adequately characterizing the image structure and effectively utilizing the change information. To address these challenges, this article proposes a signed graph-based image transformation (SGIT) method for unsupervised HeCD. First, we analyze the limitations of previous unsigned graph-based methods in capturing the image structure, which leads to the failure to detect changes in some scenes. In light of this, we construct signed graph models that utilize positive/negative weights to represent the similarity/dissimilarity relationships within the image, respectively, and employ adaptive weighting, negative sampling, and neighborhood expansion strategies to bolster the structure representation capability of signed graphs. Second, we analyze how the change would induce a bimodal distribution of vertex feature distances in original and transformed images. Subsequently, a distribution-induced reweighted graph Laplacian regularization (RGLR) is proposed to exploit this prior change information. Finally, a more accuracy image transformation model is obtained by incorporating three types of constraints: signed graph-based structural consistency term, bimodal distribution-induced RGLR, and change sparsity-based penalty term. Extensive comparative experiments on five real datasets have demonstrated the effectiveness of the proposed SGIT.