Context Spatial Awareness Remote Sensing Image Change Detection Network Based on Graph and Convolution Interaction

Remote sensing images are characterized by high dimensionality, complex textures, and large scales. Traditional Convolutional Neural Network (CNN) methods may overlook spatial relationships and contextual information among pixels when dealing with remote sensing data. Therefore, Graph Convolutional Networks (GCN) have emerged as a promising solution. In this paper, we propose a Contextual Spatial Awareness Remote Sensing Image Change Detection Network Based on Graph and Convolution interaction (CSAGC). We aim to enhance the handling of contextual information by introducing multiple augmentation modules. In CSAGC, we propose a high-performance encoder called Congraph that integrates a CNN and a Graph Neural Network (GNN). By preserving the respective features of both branches, we effectively fuse local detailed features and global positional features, achieving superior feature extraction capabilities. Additionally, we design two modules to facilitate the integration of multiscale spatial information: Contextual Spatial Awareness Module (CSAM) and Spatial Integration Module (SIM). CSAM, a crucial module connecting the encoder and decoder, jointly explores contextual features using the current feature branch and high-low level feature branches, leveraging spatial positional information for better content acquisition. SIM, located in the decoder module, aims to integrate the multiscale information outputted by CSAM, complementing the contextual information and improving the overall network’s ability to capture spatial contextual information. We conducted extensive experiments on three datasets, namely LEVIR-CD, WHU-CD, and GZ-CD. The experimental results demonstrate that CSAGC exhibits excellent performance, achieving significant performance improvements compared to state-of-the-art (SOTA) methods.