Abstract
Mamba has recently become a popular visual representation architecture for building change detection (BCD) in the remote sensing field. Although it effectively models global spatial dependencies, it still struggles to capture fine-grained local structures, thereby limiting its representation capability. To overcome this limitation, we propose ChangeMoE, a mixture-of-experts-based Mamba network for BCD from high-resolution remote sensing imagery. The main contributions and innovations are as follows: (1) A scan-specific mixture-of-experts (ScanMoE) module is proposed, which leverages a scan gating network to enhance global feature representations using eight selective scan mechanisms defined as scan-specific experts. We further quantify their contributions to improve the interpretability of global representation learning. (2) A global-and-local mixture-of-experts (GLMoE) module is proposed. We design the local and global expert networks to capture local details and global dependencies. A gating network is used to reveal their relative importance and adaptively fuse their complementary features. Experimental results on the LEVIR-CD and WHU-CD benchmarks validate the superiority of ChangeMoE over existing state-of-the-art networks. ChangeMoE achieves F1-scores of 92.55% and 94.11%, with IoU values of 86.13% and 88.88%, respectively. Compared with ChangeMamba as the baseline, the proposed ChangeMoE achieves improvements of 1.96% in F1-score and 3.33% in IoU on the LEVIR-CD dataset. These results demonstrate the effectiveness of the proposed ChangeMoE and provide an interpretable expert-fusion paradigm for understanding feature representation learning in BCD.