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arXiv 2408.01372cs.CVeess.IV
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面向高光谱图像分类的空间形态学Mamba与空谱形态学Mamba

Spatial and Spatial-Spectral Morphological Mamba for Hyperspectral Image Classification

University of Messina（墨西拿大学）；Shaoxing University（绍兴大学）；University of Hull（赫尔大学）；Innopolis University（因诺波利斯大学）；National University of Computer and Emerging Sciences（国家计算机与新兴科学大学）；Alipurduar Government Engineering and Management College（阿里布尔杜阿尔政府工程与管理学院）；Univ. Grenoble Alpes（格勒诺布尔大学）；Inria（法国国家信息与自动化研究所）；CNRS（法国国家科学研究中心）；Grenoble INP（格勒诺布尔理工学院）；Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院）；University of Chinese Academy of Sciences（中国科学院大学）

Muhammad Ahmad, Muhammad Hassaan Farooq Butt, Adil Mehmood Khan, Manuel Mazzara, Salvatore Distefano, Muhammad Usama, Swalpa Kumar Roy, Jocelyn Chanussot, Danfeng Hong

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AI总结：

针对Transformer类高光谱图像分类模型计算复杂度高的问题，提出结合形态学操作与状态空间模型的MorpMamba模型，通过多模块特征处理实现高精度下更优的参数效率。

摘要：

Transformer（尤其是自注意力机制）的最新进展显著提升了高光谱图像（HSI）分类的效果，但这类模型通常存在效率低下的问题，其计算复杂度随序列长度呈二次方增长。为解决这些挑战，我们提出了形态学空间Mamba（SMM）和形态学空谱Mamba（SSMM）模型（合称MorpMamba），该模型结合了形态学操作与状态空间模型框架的优势，提供了一种比Transformer计算效率更高的替代方案。在MorpMamba中，一个新颖的令牌生成模块首先将HSI图像块转换为空谱令牌；随后这些令牌经过腐蚀、膨胀等形态学操作处理，利用深度可分离卷积捕捉结构与形状信息。令牌增强模块通过基于HSI中心区域动态调整空间与光谱令牌来优化这些特征，确保每个模块内的特征有效融合；接着应用多头自注意力进一步丰富特征表示，使模型能够捕捉数据内部的复杂关系与依赖关系。最后，增强后的令牌被输入状态空间模块，该模块高效建模特征的时序演化以用于分类。在广泛使用的HSI数据集上的实验结果表明，MorpMamba在保持高精度的同时，相比传统CNN和Transformer模型实现了更优的参数效率。代码将公开发布于https://github.com/mahmad000/MorpMamba。

英文摘要：

Recent advancements in transformers, specifically self-attention mechanisms, have significantly improved hyperspectral image (HSI) classification. However, these models often suffer from inefficiencies, as their computational complexity scales quadratically with sequence length. To address these challenges, we propose the morphological spatial mamba (SMM) and morphological spatial-spectral Mamba (SSMM) model (MorpMamba), which combines the strengths of morphological operations and the state space model framework, offering a more computationally efficient alternative to transformers. In MorpMamba, a novel token generation module first converts HSI patches into spatial-spectral tokens. These tokens are then processed through morphological operations such as erosion and dilation, utilizing depthwise separable convolutions to capture structural and shape information. A token enhancement module refines these features by dynamically adjusting the spatial and spectral tokens based on central HSI regions, ensuring effective feature fusion within each block. Subsequently, multi-head self-attention is applied to further enrich the feature representations, allowing the model to capture complex relationships and dependencies within the data. Finally, the enhanced tokens are fed into a state space module, which efficiently models the temporal evolution of the features for classification. Experimental results on widely used HSI datasets demonstrate that MorpMamba achieves superior parametric efficiency compared to traditional CNN and transformer models while maintaining high accuracy. The code will be made publicly available at \url{https://github.com/mahmad000/MorpMamba}.

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。

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