DFANet: A Deep Feature Attention Network for Building Change Detection in Remote Sensing Imagery

Change detection (CD) in remote sensing (RS) is a fundamental task that seeks to identify changes in land cover by analyzing bitemporal images. In recent years, deep learning (DL)-based approaches have demonstrated remarkable success in a wide range of CD applications. However, most existing methods have limitations in detecting building edges and addressing pseudo-changes, and lack the ability to model feature context. In this paper, we introduce DFANet—a Deep Feature Attention Network specifically designed for building CD in RS imagery. First, we devise a spatial-channel attention module to strengthen the network’s capacity to extract change cues from bitemporal feature maps and reduce the occurrence of pseudo-changes. Second, we introduce a GatedConv module to improve the network’s capability for building edge detection. Finally, Transformer is introduced to capture long-range dependencies across bitemporal images, enabling the network to better understand feature change patterns and the relationships between different regions and land cover categories. We carried out comprehensive experiments on two publicly available building CD datasets—LEVIR-CD and WHU-CD. The results demonstrate that DFANet achieves exceptional performance in evaluation metrics such as precision, F1 score, and IoU, consistently outperforming existing state-of-the-art approaches.