A dual-difference change detection network for detecting building changes on high-resolution remote sensing images

Abstract

Existing deep learning-based change detection networks encounter challenges related to the temporal dependency inherent in dual-temporal images. In this study, a weight-shared dual-difference change detection network(DDCDNet) model is proposed based on feature extraction networks. The model employs feature discrimination modules fused with spatial and channel attention mechanisms at different hierarchical levels of the backbone network. In the encoding phase, the tiny version of the Swin Transformer is utilized as the backbone network, with a weight-sharing strategy applied to extract feature information from bi-temporal remote sensing images. The proposed model in this paper is experimented on the LEVIR-CD + and DSIFN datasets, achieving F1-scores (F1) of 87.71% and 85.79%,recalls of 83.87% and 81.17%, and IoU (Intersection over Union) scores of 78.11% and 75.12%, respectively. These results indicate that the proposed model significantly outperforms other comparative models, demonstrating a better capability of identifying temporal changes in buildings, excellent generalization capability.