Byte Latent Mamba With State Space and Knowledge Distillation for Hyperspectral Image Classification

Hyperspectral image classification (HSIC) is a challenging task due to the high dimensionality of hyperspectral data, the complex interplay of spatial and spectral features, and the scarcity of annotated samples. Existing approaches, mainly based on tokenization-based feature extraction, introduce artificial segmentation, increasing computational cost, and may lead to information loss. To address these issues, a novel framework, byte latent Mamba with knowledge distillation (BLM-KD), which overcomes explicit tokenization by directly learning byte-level spectral–spatial representations from raw hyperspectral data, is proposed. The byte latent Mamba architecture learns compact and expressive byte-level features through an end-to-end convolutional encoder, preserving spectral continuity and spatial structure. A structured state-space model (SSM) is integrated to model long-range spatial–spectral dependencies efficiently via learned dynamic state transitions. Additionally, an adaptive knowledge distillation (KD) strategy is adopted, where a high-capacity teacher model selectively transfers salient features to a lightweight student model, driven by a temperature-controlled weighting schedule. This ensures robust generalization with reduced model complexity. A patch-based preprocessing scheme also excludes irrelevant zero-labeled samples, refining the training process. Extensive experiments conducted on multiple real-world hyperspectral benchmarks demonstrate that BLM-KD outperforms existing state-of-the-art methods in both classification accuracy and computational efficiency.