LDST-ChangeNet: Lightweight Remote Sensing Change Detection Model Based on Dual Spatio-Temporal Attention and Multi-Scale Decoding

Remote sensing image change detection is widely used in urban expansion analysis, land-use monitoring, and disaster assessment. Nevertheless, it still faces significant challenges due to pseudo-change interference in high-resolution imagery, the large-scale variation in small changed objects, and the need for lightweight models in real-world engineering applications. To address these issues, this paper proposes LDST-ChangeNet, a lightweight dual spatiotemporal attention network for change detection. The network adopts a Siamese EfficientNet-B1 as its dual-branch encoder and employs a differential bi-temporal feature fusion strategy (Diff) to explicitly model temporal discrepancies, enabling efficient feature extraction while significantly reducing model complexity. A Position Attention Module (PAM) is introduced at the encoder bottleneck to suppress pseudo changes caused by non-structural factors. Meanwhile, a lightweight Pyramid Pooling Module (PPM-lite) is incorporated at the entrance of the deepest decoder features to enhance multi-scale contextual representation. Furthermore, a Boundary Attention Module (BAM) is applied in the decoder output stage to improve boundary delineation and small-object change detection. Experimental results on the LEVIR-CD and WHU-CD datasets show that LDST-ChangeNet outperforms other state-of-the-art methods, achieving F1-scores of 90.67% and 91.08%, respectively. The model maintains a lightweight design, requiring only 11.72 M parameters and 10.03 GFLOPs on LEVIR-CD, and 11.77 M parameters and 9.12 GFLOPs on WHU-CD.