HCGNet: A Hybrid Change Detection Network Based on CNN and GNN

Image features occur at different scales, e.g., short-term and long-term ones, and both of them are significant in the change detection (CD) of remote sensing images. To the best of our knowledge, however, it is still a challenge on how to effectively combine them together for a full-scale CD. The development of deep learning techniques brings the light on this issue. In this work, we propose a hybrid initiative called HCGNet, combining convolutional neural network (CNN) and vision graph neural network (ViG) for capturing the local and global features, respectively, in which we conduct two main adaptions for high accuracy: 1) a shift graph convolution module to establish the association between a node and its surrounding nodes for enhancing the local feature extraction capability and 2) a dual-branch decoder structure that efficiently utilizes the multiscale features acquired from the encoder to enhance the accuracy of the change map. The results show that: 1) our proposal outperforms the state-of-the-art works and 2) the individual functions of each component are obvious in the ablation experiments.