Similarity and dissimilarity relationships based graphs for multimodal change detection

Multimodal change detection (CD) is an increasingly interesting yet highly challenging subject in remote sensing. To facilitate the comparison of multimodal images, some image regression methods transform one image to the domain of the other image, allowing for images comparison in the same domain as in unimodal CD. In this paper, we begin by analyzing the limitations of previous image structure based regression models that only rely on similarity relationships. Then, we highlight the significance of incorporating dissimilarity relationships as a complementary approach to more comprehensively characterize and utilize the image structure. In light of this, we propose a novel method for multimodal CD called Similarity and Dissimilarity induced Image Regression (SDIR). Specifically, SDIR construct a similarity based k-nearest neighbors (KNN) graph and a dissimilarity based k-farthest neighbors (KFN) graph, where the former mainly characterizes the low-frequency information and the latter captures the high-frequency information in spectral domain. Therefore, the proposed SDIR that incorporates similarity (low-frequency) and dissimilarity (high-frequency) relationships enables to achieve better regression performance. After completing the image regression, we utilize a Markovian based fusion segmentation model to combine the change fusion and change extraction processes for improving the final CD accuracy. The proposed method's effectiveness is demonstrated through experiments on six real datasets and compared with eleven advanced and widely used methods, achieving 5.6% improvements in the average Kappa coefficient. The source code is accessible at https://github.com/yulisun/SDIR.