CSCNet: A Cross-Scale Coordination Siamese Network for Building Change Detection

Remote sensing image change detection (CD) has witnessed remarkable performance improvements with the guidance of deep learning models, particularly convolutional neural networks and transformers. Current CD methods heavily rely on multilayered backbone structures, such as ResNet and Unet, for feature extraction. However, these approaches exhibit limitations in coordinating the utilization of local and global features across different scales. In this article, we introduce a novel cross-scale coordinated siamese (CSC) network to effectively integrate multiscale information. We introduce a cross-scale coordination module (CSCM) within the CSC network to coordinate internal features of the local branch with cross-scale information from adjacent branches, while simultaneously attending to both the local and global regions. Furthermore, to comprehensively capture contextual information, we propose a transformer aggregation module as a decoder to harmonize the output features of CSCM. We extensively evaluate our proposed CSC network on three datasets, namely, LEVIR-CD, WHU-CD, and GZ-CD. The results demonstrate that our CSC network outperforms other leading methods significantly in terms of F1-score and intersection over union evaluation metrics.