Sparse Deformable Mamba for Hyperspectral Image Classification
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arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866909581156810752 |
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| author | Xu, Lincoln Linlin Zhu, Yimin Dewis, Zack Xu, Zhengsen Alkayid, Motasem Heffring, Mabel Taleghanidoozdoozan, Saeid |
| author_facet | Xu, Lincoln Linlin Zhu, Yimin Dewis, Zack Xu, Zhengsen Alkayid, Motasem Heffring, Mabel Taleghanidoozdoozan, Saeid |
| contents | Although Mamba models significantly improve hyperspectral image (HSI) classification, one critical challenge is the difficulty in building the sequence of Mamba tokens efficiently. This paper presents a Sparse Deformable Mamba (SDMamba) approach for enhanced HSI classification, with the following contributions. First, to enhance Mamba sequence, an efficient Sparse Deformable Sequencing (SDS) approach is designed to adaptively learn the ''optimal" sequence, leading to sparse and deformable Mamba sequence with increased detail preservation and decreased computations. Second, to boost spatial-spectral feature learning, based on SDS, a Sparse Deformable Spatial Mamba Module (SDSpaM) and a Sparse Deformable Spectral Mamba Module (SDSpeM) are designed for tailored modeling of the spatial information spectral information. Last, to improve the fusion of SDSpaM and SDSpeM, an attention based feature fusion approach is designed to integrate the outputs of the SDSpaM and SDSpeM. The proposed method is tested on several benchmark datasets with many state-of-the-art approaches, demonstrating that the proposed approach can achieve higher accuracy with less computation, and better detail small-class preservation capability. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_09446 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Sparse Deformable Mamba for Hyperspectral Image Classification Xu, Lincoln Linlin Zhu, Yimin Dewis, Zack Xu, Zhengsen Alkayid, Motasem Heffring, Mabel Taleghanidoozdoozan, Saeid Computer Vision and Pattern Recognition Although Mamba models significantly improve hyperspectral image (HSI) classification, one critical challenge is the difficulty in building the sequence of Mamba tokens efficiently. This paper presents a Sparse Deformable Mamba (SDMamba) approach for enhanced HSI classification, with the following contributions. First, to enhance Mamba sequence, an efficient Sparse Deformable Sequencing (SDS) approach is designed to adaptively learn the ''optimal" sequence, leading to sparse and deformable Mamba sequence with increased detail preservation and decreased computations. Second, to boost spatial-spectral feature learning, based on SDS, a Sparse Deformable Spatial Mamba Module (SDSpaM) and a Sparse Deformable Spectral Mamba Module (SDSpeM) are designed for tailored modeling of the spatial information spectral information. Last, to improve the fusion of SDSpaM and SDSpeM, an attention based feature fusion approach is designed to integrate the outputs of the SDSpaM and SDSpeM. The proposed method is tested on several benchmark datasets with many state-of-the-art approaches, demonstrating that the proposed approach can achieve higher accuracy with less computation, and better detail small-class preservation capability. |
| title | Sparse Deformable Mamba for Hyperspectral Image Classification |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2504.09446 |