MambaMoE: Mixture-of-Spectral-Spatial-Experts State Space Model for Hyperspectral Image Classification
Journal:
arXiv
Published Date:
Apr 29, 2025
Abstract
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.
