  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.
