用于高光谱图像分类的谱-空Mamba
Spectral-Spatial Mamba for Hyperspectral Image Classification
AI总结：
本文首次将Mamba状态空间模型引入高光谱图像分类，提出谱-空Mamba（SS-Mamba），通过谱-空token生成模块和堆叠的谱-空Mamba块实现空间与谱特征的高效建模与融合，在多个HSI数据集上取得与最先进方法相当的分类性能。
摘要：
近年来，深度学习模型在高光谱图像（HSI）分类中取得了优异的性能。在众多深度模型中，Transformer因其在建模HSI中空间-谱特征的长程依赖关系方面的卓越能力而逐渐引起关注。然而，Transformer由于自注意力机制而存在二次计算复杂度的问题，这比其他模型更重，因此在HSI处理中的应用受到限制。幸运的是，最近出现的基于状态空间模型的Mamba在实现Transformer建模能力的同时，展现出极高的计算效率。因此，本文对将Mamba应用于HSI分类进行了初步尝试，提出了谱-空Mamba（SS-Mamba）。具体而言，所提出的SS-Mamba主要由谱-空token生成模块和若干堆叠的谱-空Mamba块组成。首先，token生成模块将任意给定的HSI立方体转换为空间token和谱token序列。然后，这些token被送入堆叠的谱-空Mamba块（SS-MB）。每个SS-MB块由两个基本Mamba块和一个谱-空特征增强模块组成。空间token和谱token分别由两个基本Mamba块单独处理。此外，特征增强模块利用HSI样本的中心区域信息对空间token和谱token进行调制。通过这种方式，谱token和空间token相互协作，并在每个块内实现信息融合。在广泛使用的HSI数据集上进行的实验结果表明，与最先进的方法相比，所提出的模型取得了具有竞争力的结果。基于Mamba的方法为HSI分类打开了一扇新的窗口。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
