Exploring Context Alignment and Structure Perception for Building Change Detection

Automatically monitoring building changes can assist human experts in disaster rescue, urban planning, and resource protection. Consequently, much research focuses on building change detection. Recently, the methods based on deep learning have achieved impressive performance. However, most of them ignore the effect of bitemporal image misalignment, which is prone to lead to false detection. To this end, a building change detection model with context alignment and structure perception (CASP) is proposed. First, imitating the brain logic of humans to identify changes, a bitemporal interactive alignment module (BIAM) is designed, which suppresses the spatial dislocation noise via a bidirectional reference-guided feature aggregation strategy. Building on this, a difference-induced alignment module (DIAM) is introduced to mitigate the adverse impact of misalignment errors further and improve the accuracy of building change detection. Second, a structure-aware feature fusion module is developed and integrated into the feature encoder, to enhance the discrimination of building representations and highlight the specificity of the proposed method. Extensive experiments on three representative building change detection datasets are implemented to verify the superiority of the above improvements. The quantitative and qualitative results demonstrate that the proposed method achieves competitive performance. The code is available at https://github.com/ptdoge/CASP.