IGroupSS-Mamba: Interval Group Spatial–Spectral Mamba for Hyperspectral Image Classification

Abstract

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 S6 in HSI classification, suffering from suboptimal performance and computational efficiency. In light of this, this article investigates a lightweight interval group spatial-spectral mamba framework (IGroupSS-Mamba) for HSI classification, which allows for multidirectional and multiscale 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 nonoverlapping groups at intervals, and then integrates a unidirectional S6 for each group with a specific scanning direction to achieve nonredundant sequence modeling. Compared with conventional applying multidirectional 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 significantly outperforms the state-of-the-art methods in classification accuracy and achieves lower model parameters and floating point operations (FLOPs). The code is available at https://github.com/IIP-Team/ IGroupSS-Mamba.