DINO-CD:Marrying DINOv3 with Dual-Branch Complementary Reconstruction for Remote Sensing Change Detection

The goal of remote sensing change detection (RSCD) is to accurately distinguish land-surface changes. Recently, Vision foundation models (VFMs), such as the self-supervised DINOv3, have demonstrated strong semantic parsing capabilities across various visual tasks. However, directly applying DINOv3 to RSCD remains challenging. First, DINOv3 produces low-resolution, single-scale feature representations, which are unfavorable for pixel-wise change classification. Second, DINOv3 is not tailored for RSCD and thus struggles to model geospatial differences along the temporal dimension. To address these issues, Marrying DINOv3 with Dual-Branch Complementary Reconstruction for Remote Sensing Change Detection (DINO-CD) is proposed. Specifically, a dual-branch complementary reconstruction module is introduced to enable interaction between DINO and CNN branches for complementary feature enhancement, producing multi-scale representations that preserve global semantic consistency while strengthening local detail discrimination. In addition, a hierarchical difference aggregation module is designed to comprehensively enhance temporal discrepancies via explicit frequency learning and multi-scale directional aggregation, thereby highlighting effective change regions and reducing false alarms and missed detections. Extensive experiments on multiple datasets demonstrate that DINO-CD outperforms state-of-the-art methods, achieving gains of 6% in F1-score and 8% in IoU on the CLCD dataset.