Remote sensing change detection (CD) networks have been increasingly powerful with the application of convolutional neural networks (CNNs) and transformers. The CNN-based CD method with a CNN backbone has been widely used and plays an significant role. In complex spatial relationships within remote sensing images, CNNs may face limitations due to the restricted receptive field, making it challenging to handle intricate pixel relationships effectively. Therefore, to address this limitation of CNNs, we introduce vision graph neural network (ViG) to tackle the constrained receptive field issue. In addition, we propose a backbone network named Congraph, which integrates convolution and graph interaction. Congraph simultaneously leverages local information from CNNs and global information from GNNs, enabling more comprehensive feature extraction for more accurate change detection. Furthermore, we introduce a multiscale mixed attention (MMA) module to make the model focus on different scale feature information. MMA replaces small-scale features in the multilayer encoder with self-attention to capture global feature information within small-scale features. Finally, we feed bitemporal features into a transformer module to obtain feature difference information and generate the ultimate feature difference map. Through extensive experiments on the LEVIR-CD, WHU-CD, and GZ-CD datasets, our method demonstrates more significant performance advantages compared to the current state-of-the-art change detection methods.
