An Attention Feature Interaction Change Detection Method Based on Detail Enhancement for Dual-Temporal Hyperspectral Images

The application of hyperspectral image change detection (HSI-CD) in remote sensing is becoming increasingly widespread. However, due to the low spatial resolution of HSIs, conducting CD directly on the original HSIs does not effectively capture subtle changes. Therefore, this letter proposes an attention feature interaction CD method based on detail enhancement for dual-temporal HSIs (AIDECD). First, a detail enhancement module is designed to enhance the detail information of original HSIs. Second, considering the relationship between dual-temporal images, an attention interaction module is designed to achieve the interaction of temporal features between the dual-temporal images. Then, a multiscale feature extraction module is designed to capture features of different scales. The kappa coefficients obtained on three HSI datasets are 86.11%, 96.08%, and 97.54%, respectively. Compared with six other CD methods, this method has higher detection performance.