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.

翻译：近年来，深度学习模型在高光谱图像分类任务中取得了优异的性能。在众多深度模型中，Transformer因其在建模高光谱图像空间-光谱特征长程依赖性方面的卓越表现而逐渐受到关注。然而，由于自注意力机制导致的二次计算复杂度问题，Transformer的计算负担较其他模型更为沉重，这限制了其在高光谱图像处理中的广泛应用。幸运的是，最近出现的基于状态空间模型的Mamba在保持Transformer建模能力的同时，展现出极高的计算效率。因此，本文首次尝试将Mamba应用于高光谱图像分类，提出了光谱-空间Mamba模型。具体而言，所提出的SS-Mamba主要由光谱-空间令牌生成模块和多个堆叠的光谱-空间Mamba块构成。首先，令牌生成模块将任意给定的高光谱图像立方体转换为空间和光谱令牌序列。随后，这些令牌被送入堆叠的光谱-空间Mamba块中。每个SS-MB块包含两个基础Mamba块和一个光谱-空间特征增强模块。空间和光谱令牌分别由两个基础Mamba块独立处理。此外，特征增强模块利用高光谱图像样本中心区域信息对空间和光谱令牌进行调制。通过这种方式，光谱与空间令牌在每个块内相互协作，实现信息融合。在广泛使用的高光谱图像数据集上进行的实验结果表明，与现有先进方法相比，所提模型取得了具有竞争力的性能。基于Mamba的方法为高光谱图像分类开启了新的窗口。