The Mamba model has recently demonstrated strong potential in hyperspectral image (HSI) classification, owing to its ability to perform context modeling with linear computational complexity. However, existing Mamba-based methods usually neglect the spectral and spatial directional characteristics related to heterogeneous objects in hyperspectral scenes, leading to limited classification performance. To address these issues, we propose MambaMoE, a novel spectral-spatial mixture-of-experts framework, representing the first MoE-based approach in the HSI classification community. Specifically, we design a Mixture of Mamba Expert Block (MoMEB) that leverages sparse expert activation to enable adaptive spectral-spatial modeling. Furthermore, we introduce an uncertainty-guided corrective learning (UGCL) strategy to encourage the model's attention toward complex regions prone to prediction ambiguity. Extensive experiments on multiple public HSI benchmarks demonstrate that MambaMoE achieves state-of-the-art performance in both accuracy and efficiency compared to existing advanced approaches, especially for Mamba-based methods. Code will be released.
View on arXiv@article{xu2025_2504.20509,
title={ MambaMoE: Mixture-of-Spectral-Spatial-Experts State Space Model for Hyperspectral Image Classification },
author={ Yichu Xu and Di Wang and Hongzan Jiao and Lefei Zhang and Liangpei Zhang },
journal={arXiv preprint arXiv:2504.20509},
year={ 2025 }
}