Although Mamba models greatly improve Hyperspectral Image (HSI) classification, they have critical challenges in terms defining efficient and adaptive token sequences for improve performance. This paper therefore presents CSSMamba (Clustering-guided Spatial-Spectral Mamba) framework to better address the challenges, with the following contributions. First, to achieve efficient and adaptive token sequences for improved Mamba performance, we integrate the clustering mechanism into a spatial Mamba architecture, leading to a cluster-guided spatial Mamba module (CSpaMamba) that reduces the Mamba sequence length and improves Mamba feature learning capability. Second, to improve the learning of both spatial and spectral information, we integrate the CSpaMamba module with a spectral mamba module (SpeMamba), leading to a complete clustering-guided spatial-spectral Mamba framework. Third, to further improve feature learning capability, we introduce an Attention-Driven Token Selection mechanism to optimize Mamba token sequencing. Last, to seamlessly integrate clustering into the Mamba model in a coherent manner, we design a Learnable Clustering Module that learns the cluster memberships in an adaptive manner. Experiments on the Pavia University, Indian Pines, and Liao-Ning 01 datasets demonstrate that CSSMamba achieves higher accuracy and better boundary preservation compared to state-of-the-art CNN, Transformer, and Mamba-based methods.

翻译：尽管Mamba模型显著提升了高光谱图像分类性能，但其在构建高效且自适应的token序列以进一步提升性能方面仍面临关键挑战。为此，本文提出了CSSMamba（聚类引导的空谱Mamba）框架以更好地应对这些挑战，主要贡献如下：首先，为实现高效且自适应的token序列以提升Mamba性能，我们将聚类机制集成到空间Mamba架构中，构建了聚类引导的空间Mamba模块，该模块能有效缩短Mamba序列长度并增强其特征学习能力。其次，为同时提升空间与光谱信息的学习效果，我们将CSpaMamba模块与光谱Mamba模块相结合，形成了完整的聚类引导空谱Mamba框架。第三，为进一步增强特征学习能力，我们引入了注意力驱动的token选择机制以优化Mamba的token序列构建。最后，为使聚类机制与Mamba模型实现无缝协同，我们设计了可学习的聚类模块以自适应地学习聚类隶属关系。在帕维亚大学、印第安松树及辽宁01数据集上的实验表明，相较于当前最先进的CNN、Transformer及基于Mamba的方法，CSSMamba在分类精度和边界保持方面均表现出更优性能。