End-to-End Hyperspectral Image Change Detection Based on Band Selection.

Change detection (CD) aims to identify differences in the same scene at different times. With the increasing amount of hyperspectral images (HSIs), more and more change detection techniques use HSIs as the raw data. HSIs often contain redundant bands, where only a few are crucial for CD while others may be detrimental. However, most existing HSI-CD methods extract features directly from full-dimensional HSIs, leading to a degradation of feature discrimination. To tackle this issue, in this paper, we propose an end-to-end hyperspectral image change detection network based on band selection (ECDBS), unlocking the potential synergy between band selection and CD. The network compromises a deep learning based band selection module and cascaded band-specific spatial attention (BSA) blocks. The band selection module selectively retains bands favourable to CD according to the importance of the bands measured based on band correlation. The BSA block tailors the feature extraction strategy for each band based on its feature distribution, allowing extracting sufficient features from each band. Experimental evaluations were conducted on three widely used HSI-CD datasets, demonstrating the effectiveness and superiority of our proposed method over other state-of-the-art techniques.