Research on remote sensing landslide image recognition method based on dual-model ensemble swin transformer
To address the challenges of diverse landslide morphologies and strong background interference in post-earthquake mountainous regions, where a single model suffers from limited adaptability, this paper proposes a dual-model ensemble method for remote sensing landslide identification based on Swin Transformer. The method employs Swin Transformer as a shared backbone network to reduce computational redundancy, integrates the core enhancement modules from SCPD-Deeplabv3+ and LSMFormer to simultaneously capture the global structure of complex landslides and fine-scale boundary details of small landslides, and reuses the MSAD decoder for deep feature fusion to achieve pixel-level segmentation. Experimental results show that the ensemble model achieves a mean intersection over union (mIoU) of 91.88%, with precision, recall, and F1-score of 94.37%, 96.11%, and 94.78%, respectively. The proposed method outperforms each individual model, effectively reducing false positives and missed detections, while balancing the contour accuracy of large-scale landslides and the detail precision of small-scale landslides.