Hyperspectral image (HSI) classification remains a challenging task due to high spectral redundancy, complex spectral–spatial correlations, and the limited availability of labeled samples. To address these issues, this paper proposes a novel framework termed M3-Mamba, which integrates language-guided, multi-level, and Mamba-based spectral modeling for hyperspectral image classification. The proposed M3-Mamba leverages high-level semantic priors derived from multimodal representations to guide discriminative spectral modeling, enabling effective interaction between semantic information and fine-grained spectral features. In addition, a frequency-aware Mamba-based state space module is introduced to efficiently capture long-range spectral dependencies while avoiding the quadratic computational complexity of conventional attention mechanisms. Meanwhile, a text-guided modulation strategy is designed to adaptively reweight spectral responses under semantic guidance, suppressing redundant or noisy bands and enhancing class-relevant spectral responses without compromising spectral fidelity. This semantic-to-spectral modulation allows M3-Mamba to better cope with spectral variability and inter-class confusion. Extensive experiments conducted on four widely used benchmark datasets, including Indian Pines, Pavia University, Salinas, and Houston datasets, demonstrate thatM3-Mamba achieves competitive overall accuracy, average accuracy, and Kappa coefficient under the adopted benchmark settings. Ablation studies further validate the effectiveness of each key component, confirming that the proposed framework demonstrates promising effectiveness for hyperspectral image classification.
Peng et al. (Sun,) studied this question.
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