Self-Supervised Mamba for Hyperspectral Image Classification

Recent advances in mamba-based architectures have demonstrated promising potential for hyperspectral image classification (HSIC), offering linear-complexity long-range dependency modeling. However, two critical challenges persist in adapting this paradigm to HSI analysis: the substantial requirement for annotated training samples, and insufficient capacity to interpret the intricate spatial-spectral features inherent in HSI data, particularly under few-shot learning scenarios. To address these limitations, we present SSupMamba, a novel self-supervised mamba framework tailored for HSIC. First, we propose a composite scanning mamba block (CSMB) that enables comprehensive global feature extraction through multi-directional selective scanning of HSI data cubes. Second, we develop a spatial-spectral masked mamba (SAEM) framework that employs randomized masking and reconstruction tasks to enhance local representation learning. Third, we establish a unified self-supervised architecture incorporating contrastive learning to maximize mutual information between multi-views while preserving intrinsic spatial-spectral characteristics. Experimental results on four public datasets demonstrate that the proposed method exhibits excellent feature extraction capabilities under few-shot conditions and outperforms several state-of-the-art HSIC methods. The code is available at: https://github.com/Winkness/SSupMamba.