Mining and Integrating Spatiotemporal Continuity Information for Remote Sensing Change Detection

Existing remote sensing (RS) change detection (CD) methods typically extract multi-scale features from bi-temporal images, followed by feature fusion and decoding. However, they often overlook spatiotemporal continuity and external correlations, leading to insufficient ability to mine spatiotemporal context. In this paper, we propose a Continuity Information Mining and Integrating Network (CIMINet), which captures and integrates spatiotemporal continuity to reduce pseudo-changes caused by scale differences and limited temporal cues. Spatially, bi-temporal images are processed by Adjacent-Scale Feature Fusion Module (ASFFM) and Spatial Change Extraction Module (SCEM) to extract continuous spatial features and spatial differences. Temporally, pseudo-video frames generated via interpolation help construct temporal continuity, which is further exploited by Temporal Change Extraction Module (TCEM). Finally, we jointly fuse spatial and temporal differences by Spatiotemporal Fusion Module (STFM), highlighting the change regions. Various experimental results indicate that CIMINet outperforms 12 SOTA CD methods on four RS datasets. The code can be found at https://github.com/Aosion518/CIMINet.