Abstract:Remote sensing image change detection plays a vital role in urban expansion and disaster monitoring. However, existing methods still exhibit limitations in feature extraction capability, resistance to pseudo-change interference, and multi-scale feature fusion. To this end, this study proposes MS-SwinCE, a change detection model that integrates multi-scale Swin Transformer, a contrastive feature enhancement module (CFEM), and a bi-directional feature pyramid network (BiFPN). The model enhances long-range dependency modeling via the local window and shifted mechanism, with CFEM accurately capturing change differences and suppressing noise, and BiFPN efficiently fusing multi-scale semantic information. Experimental results demonstrate that MS-SwinCE outperforms ChangeFormer on the LEVIR-CD dataset, with an improvement of 1.18% in IoU, 0.70% in F1 score, 0.32% in Precision, and 1.06% in Recall. On the WHU-CD dataset, MS-SwinCE achieves an increase of 1.84% in IoU, 1.06% in F1 score, 0.47% in Precision, and 1.66% in Recall compared to BIT. Additionally, while maintaining high accuracy, MS-SwinCE has a parameter count of 31.66M, notably lower than ChangeFormer (41.03M) with similar accuracy. Thus, an effective balance between accuracy and efficiency is achieved. Ablation studies further confirm the effectiveness and synergistic gain of each module.