提出稀疏可变形Mamba模型，提升高光谱图像分类精度与效率
Sparse Deformable Mamba for Hyperspectral Image Classification
- 设计稀疏可变形序列构建方法，自适应生成高效序列
- 在多个数据集上实现更高精度且计算量更低
- 适合需要高精度小目标分类的遥感应用
尽管Mamba模型显著提升了高光谱图像（HSI）分类性能，但如何高效构建Mamba token序列仍是一大挑战。本文提出稀疏可变形Mamba（SDMamba）方法以增强HSI分类效果。首先，设计高效的稀疏可变形序列（SDS）方法，自适应学习最优序列，实现稀疏且可变形的序列表示，提升细节保留能力并降低计算开销。其次，基于SDS，分别设计稀疏可变形空间Mamba模块（SDSpaM）和稀疏可变形光谱Mamba模块（SDSpeM），针对性建模空间与光谱信息。最后，引入基于注意力的特征融合机制，整合两模块输出。在多个基准数据集上与多种先进方法对比，结果表明所提方法在保持更少计算量的同时取得更高分类精度，并展现出更强的小类细节保留能力。
原文摘要 · Abstract (English)
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.
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