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Self-Supervised Mamba for Hyperspectral Image Classification
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AI
DOI：10.1109/TGRS.2025.3622597.png)
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Abstract
En 中文
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 hyperspectral image (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 (FSL) 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 multidirectional selective scanning of HSI data cubes. Second, we develop a spatial-spectral masked Mamba (SAEM) framework that uses randomized masking and reconstruction tasks to enhance local representation learning. Third, we establish a unified self-supervised architecture incorporating contrastive learning (CL) to maximize mutual information between multiviews 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.
Keywords:
Contrastive learning (CL)
few-shot learning (FSL)
hyperspectral image classification (HSIC)
masked image modeling (MIM)
self-supervised learning (SSL)
spatial–spectral Mamba
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