A Differential-Based Siamese Network Integrating the CSWin Transformer for Rural Land Cover Semantic Change Detection

Deep learning-based methods for land cover semantic change detection utilizing high-resolution, multi-temporal remote sensing imagery have emerged as a research hotspot. However, traditional CNN methods often struggle to preserve long-range spatial context information and face challenges in detecting land cover types with complex semantic change patterns in natural scenes. To address these issues, this study proposes a novel network architecture that integrates a Siamese network with differential structures and a Transformer. First, we introduce residual learning modules to improve the extraction of differential features and strengthen the representation of local features. Second, we integrate the Cross-Shaped Window (CSWin) Transformer into a differential-based Siamese network to enhance global feature extraction. To promote model training and evaluation, we propose a rural land cover change detection dataset—a high-precision dataset comprising 6 main rural land cover types. Ablation and comparative experiments were conducted on the publicly available SECOND datasets and the self-built RLCD dataset. Ablation studies on the RLCD dataset demonstrate that DSTNet achieves significant improvements over the baseline, with increases of 1.77%, 1.95%, 2.57%, and 0.92% in mIoU, Sek, Fscd, and OA. Comparative experiments on the SECOND datasets reveal that the mIoU, Sek, Fsd, and OA scores of DSTNet surpassed the second-best accuracy by 1.04%, 2.15%, 2.28%, and 0.72%.