MoEMamba: Mixture-of-Scale-And-Attention-Experts Mamba Network for Remote Sensing Change Detection

The Mamba network has recently emerged as a powerful architecture for remote sensing change detection (RSCD). Notably, the fusion of multi-scale features and heterogeneous attention mechanisms has proven effective in enhancing the performance of Mamba network. However, existing methods fail to quantitatively evaluate the actual contribution weights of different components during feature fusion, thereby limiting the interpretability of their improvements. To address this issue, this paper proposes a novel mixture-of-scale-and-attention-experts Mamba Network (MoEMamba) for the RSCD task, which has the following innovations and contributions: (1) a Siamese Mamba architecture is employed to extract features from bi-temporal images; (2) a mixture-of-scale-experts module is designed to enhance the generalization ability across multi-scale objects, which quantitatively evaluates the contribution of multiple scale-specific experts; and (3) a mixture-of-attention-experts module is proposed to improve the accuracy, which adaptively fuses complementary responses of heterogeneous attention mechanisms based on the learned weights. Extensive experiments on the WHU-CD and LEVIR-CD datasets demonstrate that MoEMamba outperforms existing state-of-the-art methods. Specifically, it achieves F1 scores of 94.20% and 92.62%, and IoU values of 89.04% and 86.25%, respectively. Importantly, this study provides valuable insights into the interpretability of multi-scale feature fusion and heterogeneous attention fusion for RSCD research.