Recently, deep learning models have achieved excellent performance in
hyperspectral image (HSI) classification. Among the many deep models,
Transformer has gradually attracted interest for its excellence in modeling the
long-range dependencies of spatial-spectral features in HSI. However,
Transformer has the problem of quadratic computational complexity due to the
self-attention mechanism, which is heavier than other models and thus has
limited adoption in HSI processing. Fortunately, the recently emerging state
space model-based Mamba shows great computational efficiency while achieving
the modeling power of Transformers. Therefore, in this paper, we make a
preliminary attempt to apply the Mamba to HSI classification, leading to the
proposed spectral-spatial Mamba (SS-Mamba). Specifically, the proposed SS-Mamba
mainly consists of spectral-spatial token generation module and several stacked
spectral-spatial Mamba blocks. Firstly, the token generation module converts
any given HSI cube to spatial and spectral tokens as sequences. And then these
tokens are sent to stacked spectral-spatial mamba blocks (SS-MB). Each SS-MB
block consists of two basic mamba blocks and a spectral-spatial feature
enhancement module. The spatial and spectral tokens are processed separately by
the two basic mamba blocks, respectively. Besides, the feature enhancement
module modulates spatial and spectral tokens using HSI sample's center region
information. In this way, the spectral and spatial tokens cooperate with each
other and achieve information fusion within each block. The experimental
results conducted on widely used HSI datasets reveal that the proposed model
achieves competitive results compared with the state-of-the-art methods. The
Mamba-based method opens a new window for HSI classification.

该研究基于 Mamba 模型，提出了一种名为 SS-Mamba 的光谱空间分类方法，通过光谱 - 空间令牌生成模块和多个堆叠的光谱 - 空间 Mamba 块实现光谱和空间信息的融合，从而有效地进行高光谱图像分类。该方法在广泛使用的高光谱数据集上取得了与最先进方法相媲美的结果，为高光谱图像分类研究开辟了新的方向。