In remote sensing imagery, multi-class change detection (MCD) is crucial for fine-grained monitoring, yet it has long been plagued by feature ambiguity in change regions, which obscures subtle changes and hinders fine-grained discrimination under complex scene variations and scarce annotations. To address this, we propose the Tri-path DINO architecture, which adopts a multi-path decoupled complementary feature learning strategy to separately capture coarse-grained semantic features and fine-grained target-edge features. Specifically, we employ the DINOv3 pre-trained model as the backbone feature extraction network to learn coarse-grained features. An auxiliary path also adopts a siamese structure, progressively aggregating intermediate features from the siamese encoder to enhance the learning of fine-grained features. Finally, a multi-scale attention mechanism is introduced to augment the decoder network, where parallel convolutions adaptively capture and enhance contextual information under different receptive fields. The proposed method achieves state-of-the-art performance on Gaza-change, SECOND, and LandsatSCD, improving mIoU/$F_{\mathrm{scd}}$ by 1.01\%/2.93\%, 0.67\%/2.84\%, and 1.22\%/1.51\%, respectively, over the best competing method. Grad-CAM visualizations further confirm that the main and auxiliary paths naturally focus on coarse-grained semantic changes and fine-grained structural details, respectively. This synergistic complementarity provides a robust and interpretable solution for advanced change detection tasks, offering a basis for rapid and accurate damage assessment.

翻译：暂无翻译