SAM3TGNet: A SAM3 Feature Encoding and Global Context Spatiotemporal Attention-Enhanced Change Detection Method for...

Change detection in high-resolution optical remote sensing images is essential for dynamic surface monitoring and land resource management. However, existing methods still face challenges in representing multiscale change information, accurately locating change boundaries, and handling the imbalance between changed and unchanged samples. This study proposes SAM3TGNet, which transforms the general visual features of Segment Anything Model 3 (SAM3) into change-oriented representations through lightweight channel adaptation, multiscale cross-temporal interaction, and spatiotemporal feature fusion, while the Global Context Spatial Attention (GCSA) module and dynamic weighted loss enhance boundary representation and alleviate class imbalance, respectively. First, a SAM3-based bi-temporal remote sensing image feature encoder with channel adapters is developed, where intermediate multiscale feature layers are exploited to enhance semantic representation and generalization for diverse change patterns. Second, the GCSA module is introduced after spatiotemporal feature fusion to model global dependencies and enhance local details, improving boundary localization accuracy. Third, a dynamic weighted change loss function is designed to adaptively adjust the contribution of changed pixels according to their proportion in each batch, reducing background bias caused by sample imbalance and improving change localization and completeness. Experiments on the Wuhan University Change Detection (WHU-CD) and Sun Yat-sen University Change Detection (SYSU-CD) datasets demonstrate that the proposed method achieves F1-scores of 94.08 ± 0.12% and 82.91 ± 0.20%, respectively, outperforming existing approaches in multiscale change detection and boundary delineation.