Spatial and Spatial-Spectral Morphological Mamba for Hyperspectral Image Classification

• URL: http://arxiv.org/abs/2408.01372v3
• Date: Sat, 30 Nov 2024 13:24:19 GMT
• Title: Spatial and Spatial-Spectral Morphological Mamba for Hyperspectral Image Classification
• Authors: Muhammad Ahmad, Muhammad Hassaan Farooq Butt, Adil Mehmood Khan, Manuel Mazzara, Salvatore Distefano, Muhammad Usama, Swalpa Kumar Roy, Jocelyn Chanussot, Danfeng Hong,
• Abstract summary: We propose the morphological spatial mamba (SMM) and morphological spatial-spectral Mamba (SSMM) model (MorpMamba)<n>MorpMamba combines the strengths of morphological operations and the state space model framework, offering a more computationally efficient alternative to transformers.<n> Experimental results on widely used HSI datasets demonstrate that MorpMamba achieves superior parametric efficiency compared to traditional CNN and transformer models.
• Score: 27.943537708598306
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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}.

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