Hyperspectral image (HSI) classification has garnered substantial attention in remote sensing fields. Recent Mamba architectures built upon the Selective State Space Models (S6) have demonstrated enormous potential in long-range sequence modeling. However, the high dimensionality of hyperspectral data and information redundancy pose challenges to the application of Mamba in HSI classification, suffering from suboptimal performance and computational efficiency. In light of this, this paper investigates a lightweight Interval Group Spatial-Spectral Mamba framework (IGroupSS-Mamba) for HSI classification, which allows for multi-directional and multi-scale global spatial-spectral information extraction in a grouping and hierarchical manner. Technically, an Interval Group S6 Mechanism (IGSM) is developed as the core component, which partitions high-dimensional features into multiple non-overlapping groups at intervals, and then integrates a unidirectional S6 for each group with a specific scanning direction to achieve non-redundant sequence modeling. Compared to conventional applying multi-directional scanning to all bands, this grouping strategy leverages the complementary strengths of different scanning directions while decreasing computational costs. To adequately capture the spatial-spectral contextual information, an Interval Group Spatial-Spectral Block (IGSSB) is introduced, in which two IGSM-based spatial and spectral operators are cascaded to characterize the global spatial-spectral relationship along the spatial and spectral dimensions, respectively. IGroupSS-Mamba is constructed as a hierarchical structure stacked by multiple IGSSB blocks, integrating a pixel aggregation-based downsampling strategy for multiscale spatial-spectral semantic learning from shallow to deep stages. Extensive experiments demonstrate that IGroupSS-Mamba outperforms the state-of-the-art methods.

翻译：高光谱图像分类在遥感领域已引起广泛关注。近年来，基于选择性状态空间模型（S6）构建的Mamba架构在长序列建模方面展现出巨大潜力。然而，高光谱数据的高维特性与信息冗余对Mamba在HSI分类中的应用构成了挑战，导致其性能与计算效率未能达到最优。鉴于此，本文研究了一种用于高光谱图像分类的轻量级间隔分组空谱Mamba框架（IGroupSS-Mamba），该框架能够以分组和分层的方式实现多方向、多尺度的全局空谱信息提取。技术上，本文开发了间隔分组S6机制作为核心组件，该机制将高维特征按间隔划分为多个非重叠组，然后为每个组集成一个具有特定扫描方向的单向S6，以实现非冗余的序列建模。与传统的对所有波段应用多方向扫描相比，该分组策略在降低计算成本的同时，充分利用了不同扫描方向的互补优势。为了充分捕捉空谱上下文信息，本文引入了间隔分组空谱块，其中两个基于IGSM的空间和谱算子级联，分别沿空间维和谱维刻画全局空谱关系。IGroupSS-Mamba构建为由多个IGSSB块堆叠而成的分层结构，并集成了基于像素聚合的下采样策略，用于从浅层到深层阶段进行多尺度空谱语义学习。大量实验表明，IGroupSS-Mamba的性能优于现有最先进方法。