Spatial-Spectral Morphological Mamba for Hyperspectral Image Classification

• URL: http://arxiv.org/abs/2408.01372v2
• Date: Fri, 23 Aug 2024 10:57:07 GMT
• Title: Spatial-Spectral Morphological Mamba for Hyperspectral Image Classification
• Authors: Muhammad Ahmad, Muhammad Hassaan Farooq Butt, Muhammad Usama, Adil Mehmood Khan, Manuel Mazzara, Salvatore Distefano, Hamad Ahmed Altuwaijri, Swalpa Kumar Roy, Jocelyn Chanussot, Danfeng Hong,
• Abstract summary: This paper introduces the Spatial-Spectral Morphological Mamba (MorpMamba) model in which, a token generation module first converts the hyperspectral image patch into spatial-spectral tokens. These tokens are processed by morphological operations, which compute structural and shape information using depthwise separable convolutional operations. Experiments on widely used HSI datasets demonstrate that the MorpMamba model outperforms (parametric efficiency) both CNN and Transformer models.
• Score: 27.04370747400184
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In recent years, the emergence of Transformers with self-attention mechanism has revolutionized the hyperspectral image (HSI) classification. However, these models face major challenges in computational efficiency, as their complexity increases quadratically with the sequence length. The Mamba architecture, leveraging a state space model (SSM), offers a more efficient alternative to Transformers. This paper introduces the Spatial-Spectral Morphological Mamba (MorpMamba) model in which, a token generation module first converts the HSI patch into spatial-spectral tokens. These tokens are then processed by morphological operations, which compute structural and shape information using depthwise separable convolutional operations. The extracted information is enhanced in a feature enhancement module that adjusts the spatial and spectral tokens based on the center region of the HSI sample, allowing for effective information fusion within each block. Subsequently, the tokens are refined through a multi-head self-attention which further improves the feature space. Finally, the combined information is fed into the state space block for classification and the creation of the ground truth map. Experiments on widely used HSI datasets demonstrate that the MorpMamba model outperforms (parametric efficiency) both CNN and Transformer models. The source code will be made publicly available at \url{https://github.com/MHassaanButt/MorpMamba}.

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