IAE-CDNet: A Remote Sensing Change Detection Network for Buildings With Interactive Attention-Enhanced

Abstract

Currently, the development of deep learning has had a positive impact on remote sensing image change detection tasks, but many current methods still face challenges in effectively processing global and local features, especially in the task of building change detection in high-resolution images containing complex scenes. The extraction of target-related features is typically difficult, and changes in scene conditions further increase the difficulty of identifying real changes. To address these challenges, we propose the interactive attention-enhanced change detection network (IAE-CDNet). We design the local–global interaction attention module, which effectively establishes the interactive relationship between local and global features and realizes information interaction between branches, enhancing the ability to obtain architectural detail features. Additionally, our change perception attention enhancement module enhances the feature perception ability of the real change area through the joint action of the internal comprehensive feature extractor and the fusion attention mechanism. We conduct extensive experiments on three datasets. Results indicate that the evaluation indicators and performance of our IAE-CDNet are better than those of other state-of-the-art methods.