IGroupSS-Mamba：用于高光谱图像分类的间隔分组空谱Mamba
IGroupSS-Mamba: Interval Group Spatial-Spectral Mamba for Hyperspectral Image Classification
AI总结：
针对高光谱图像分类中Mamba面临的高维冗余与效率问题，提出轻量级IGroupSS-Mamba框架，通过间隔分组S6机制与分层空谱模块实现高效多尺度全局空谱建模，性能超越现有最优方法。
摘要：
高光谱图像（HSI）分类在遥感领域受到了广泛关注。近期基于选择性状态空间模型（S6）构建的Mamba架构在长序列建模中展现出巨大潜力。然而，高光谱数据的高维度与信息冗余给Mamba在HSI分类中的应用带来了挑战，存在性能欠佳与计算效率不足的问题。鉴于此，本文提出了一种用于HSI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。技术层面，本文开发了间隔分组S6机制（IGSM）作为核心组件，该机制将高维特征按间隔划分为多个不重叠的组，随后为每组配备具有特定扫描方向的单向S6，以实现无冗余的序列建模。与传统对所有波段应用多方向扫描的方式相比，这种分组策略在降低计算成本的同时，能够利用不同扫描方向的互补优势。为充分捕获空谱上下文信息，本文引入了间隔分组空谱模块（IGSSB），该模块级联了两个基于IGSM的空间算子与光谱算子，分别沿空间维度和光谱维度刻画全局空谱关联。IGroupSS-Mamba采用分层结构，由多个IGSSB模块堆叠而成，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的多尺度空谱语义学习。大量实验表明，IGroupSS-Mamba的性能优于现有最优方法。
英文摘要：
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.
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