CF-GCN: Graph Convolutional Network for Change Detection in Remote Sensing Images

The remote sensing image change detection methods based on deep learning have made great progress.However, many CNN-based methods persistently face challenges in connecting long-range semantic concepts because of their limited receptive fields. Recently, some methods that combine transformers effectively extract global information by modeling the context in the temporal and spatial domains has been proposed to solve the problem, but they still suffer from both the incorrect identification of "non-semantic changes" and the incomplete and irregular boundary extraction due to the deterioration of local feature details. In response to these inquiries, we propose a novel network, CF-GCN, based on graph convolutional structures for change detection. Specifically, in the encoder and decoder of the network, different projection strategies are employed to construct coordinate space graph convolution and feature interaction graph convolution. The Boundary Perception Module extracts spatial boundary features of shallow layers and enhances boundary perception ability during graph-based information propagation, effectively suppressing the tendency of image boundary information to gradually smooth out. At the same time, the knowledge review module is utilized to form knowledge complementarity between key layers of the network, effectively mitigating the propagation of erroneous knowledge in the deep network. On the LEVIR-CD dataset, the IoU score of CF-GCN is 83.41%, which is 0.35% and 0.39% higher than ChangeStar and DMINet, respectively. On the WHU-CD dataset, the F1 and IoU are as high as 91.83% and 84.90%, which are significantly better than other state-of-the-art networks. The experimental results show that, in addition to CNN and Transformer, the graph-convolution structure approach is expected to be another major research direction for performing fully supervised change detection. Our code and pre-trained models will be available at https://github.com/liucongcharles/CF-GCN.