MIGA-Net: A Graph Interaction and Gated Context Network for High-Resolution Remote Sensing Change Detection

Remote sensing change detection (CD) aims to localize land-surface changes from bi-temporal imagery and plays an important role in applications such as urban monitoring, disaster assessment, and environmental analysis. In high-resolution scenarios, CD performance is often degraded by cross-temporal appearance inconsistency, large variations in target scale, and boundary ambiguity introduced during multi-level decoding. To address these challenges, we propose MIGA-Net, an end-to-end framework that jointly models spatio-temporal interaction, adaptive multi-scale context aggregation, and hierarchical boundary refinement. Specifically, the Spatio-Temporal Graph Interaction Module (ST-GIM) combines interactive attention and graph reasoning to suppress pseudo-changes caused by illumination or seasonal shifts; the Adaptive Gated Context Pyramid Module (AGCP) performs content-driven scale selection and regulates context injection through a gated residual mechanism to reduce noise amplification; and the Hierarchical Boundary-Aware Refinement Module (HBAR) integrates semantic channel filtering and explicit boundary attention for progressive contour recovery. Experiments on LEVIR-CD, WHU-CD, and SYSU-CD demonstrate that MIGA-Net achieves F1 scores of 91.84%, 92.52%, and 82.92%, and IoU scores of 84.91%, 86.08%, and 70.83%, respectively. The proposed method yields consistent improvements in both quantitative metrics and structural boundary quality, indicating its effectiveness for robust pseudo-change suppression and structurally faithful prediction in high-resolution remote sensing CD.