Hyperspectral image (HSI) classification constitutes the fundamental research in remote sensing fields. Convolutional Neural Networks (CNNs) and Transformers have demonstrated impressive capability in capturing spectral-spatial contextual dependencies. However, these architectures suffer from limited receptive fields and quadratic computational complexity, respectively. Fortunately, recent Mamba architectures built upon the State Space Model integrate the advantages of long-range sequence modeling and linear computational efficiency, exhibiting substantial potential in low-dimensional scenarios. Motivated by this, we propose a novel 3D-Spectral-Spatial Mamba (3DSS-Mamba) framework for HSI classification, allowing for global spectral-spatial relationship modeling with greater computational efficiency. Technically, a spectral-spatial token generation (SSTG) module is designed to convert the HSI cube into a set of 3D spectral-spatial tokens. To overcome the limitations of traditional Mamba, which is confined to modeling causal sequences and inadaptable to high-dimensional scenarios, a 3D-Spectral-Spatial Selective Scanning (3DSS) mechanism is introduced, which performs pixel-wise selective scanning on 3D hyperspectral tokens along the spectral and spatial dimensions. Five scanning routes are constructed to investigate the impact of dimension prioritization. The 3DSS scanning mechanism combined with conventional mapping operations forms the 3D-spectral-spatial mamba block (3DMB), enabling the extraction of global spectral-spatial semantic representations. Experimental results and analysis demonstrate that the proposed method outperforms the state-of-the-art methods on HSI classification benchmarks.

翻译：高光谱图像分类是遥感领域的基础研究课题。卷积神经网络和Transformer在捕获光谱-空间上下文依赖关系方面已展现出卓越能力，但这些架构分别存在感受野有限和二次计算复杂度的缺陷。幸运的是，基于状态空间模型构建的新型Mamba架构融合了长程序列建模优势与线性计算效率，在低维场景中展现出巨大潜力。受此启发，本文提出一种用于高光谱图像分类的新型三维光谱-空间Mamba框架，能以更高计算效率实现全局光谱-空间关系建模。技术上，设计光谱-空间令牌生成模块，将高光谱数据立方体转换为三维光谱-空间令牌集合。为突破传统Mamba局限于因果序列建模且难以适应高维场景的约束，引入三维光谱-空间选择性扫描机制，沿光谱和空间维度对三维高光谱令牌执行像素级选择性扫描。构建五种扫描路径以探究维度优先级的影响。该扫描机制与传统映射操作结合构成三维光谱-空间Mamba模块，可实现全局光谱-空间语义表征提取。实验结果表明，所提方法在高光谱图像分类基准测试中优于现有先进方法。