PGNet: A GNN-Based Method with Mutual Information Minization for Fine-Grained Remote Sening Change Detection

Remote sensing image change detection (RSCD) is an active research area in remote sensing. Existing convolutional neural networks (CNN) and Transformer methods operate on images in Euclidean space, limiting flexible perception and interaction among different positions. Recent advancements in various fields have focused on transferring feature processing to non-Euclidean space, allowing for more flexible feature learning. Mapping different locations of high-resolution remote sensing images into non-Euclidean space enables more flexible perception of individual pixels. Motivated by this, we have developed a method solely based on GNN, termed Pure GNN-based Method with the Mutual Information Sensing (PGNet), specifically for detecting changes in remote sensing. In the PGNet framework, Graph Convolutional Networks (GCNs) are employed to derive graph-level characteristics. By aggregating information from neighboring nodes in the graph, we capture richer and more detailed features. Subsequently, by fusing the shallow and deep distributions of local and global features of bi-temporal remote sensing images, we achieve an effective combination of fine-grained and macro-level information. Finally, in order to make explicit semantic perception of bi-temporal images, we propose a Mutual Information Sensing module, which encourages bi-temporal feature pairs to enhance their understanding of each other's semantic information. To confirm the efficacy of PGNet, we carried out a series of tests with advanced techniques across multiple datasets. The outcomes show that our model surpasses existing CNN and Transformer-based methods in precision.