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Sparse Deformable Mamba for Hyperspectral Image Classification
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AI
DOI：10.1109/LGRS.2025.3587256.png)
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Abstract
En 中文
Although Mamba models significantly improve hyperspectral image (HSI) classification, one critical challenge is the difficulty in building the sequence of Mamba tokens efficiently. This letter presents a sparse deformable Mamba (SDMamba) approach for enhanced HSI classification, with the following contributions. First, to enhance the Mamba sequence, an efficient sparse deformable sequencing (SDS) approach is designed to adaptively learn the “optimal” sequence, leading to a 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 three benchmark datasets with many state-of-the-art approaches, including convolutional neural networks (CNNs), GAN Transformer, and Mamba-based methods, demonstrating that the proposed approach can achieve higher accuracy with less computation, and better detail small-class preservation capability.
Keywords:
Deep learning (DL)
hyperspectral image (HSI) classification
sparse deformable Mamba (SDMamba)
sparse deformable spatial Mamba module (SDSpaM)
sparse deformable spectral Mamba module (SDSpeM)
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