Accurate mangrove change detection is important for coastal ecosystem monitoring but remains challenging due to tidal disturbances, unstable land–water boundaries, and multi-scale distribution variability. Tidal fluctuations introduce spectral variations that obscure real changes. As a result, existing deep learning methods face difficulties in distinguishing tide-induced pseudo-changes while balancing semantic consistency and boundary accuracy. To address these issues, we propose DSDGMNet, which incorporates Dual-Stream Difference Modeling and Deep-Guided Multiscale Fusion. The dual-stream difference-driven strategy is designed to reduce tidal interference and improve sensitivity to true structural changes, and the deep-guided multiscale fusion module integrates global context with fine boundary details. Experiments on the GBCNR dataset show that DSDGMNet achieves an F1-score of 71.36% compared to 68.87% by SNUNet (Siamese Densely Connected UNet) and 66.39% by ChangeFormer. On the WHU-CD dataset, DSDGMNet yields an F1-score of 91.38%, in comparison with 89.85% for DDLNet and 88.82% for ChangeFormer. These results suggest the method’s effectiveness for mangrove change detection in complex intertidal environments.
Wang et al. (Sun,) studied this question.