Hyperspectral-LiDAR Collaborative Classification Combining State Space and Attention

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Graphic and Image Processing | 更新时间：2026-01-27

- Hyperspectral-LiDAR Collaborative Classification Combining State Space and Attention
- “The Mamba deep learning architecture integrates a multimodal state space hyperspectral remote sensing image classification network, achieving a breakthrough in the field of land object classification with an overall classification accuracy of over 99.33%.”
- Software Guide Vol. 25, Issue 1, Pages: 158-165(2026)
- 作者机构：
  
  1.湖南理工学院信息科学与工程学院
  2.三维重建与智能应用技术湖南省工程研究中心
  3.湖南理工学院人工智能学院，湖南岳阳 414000
- 作者简介：
- 基金信息：
- DOI：10.11907/rjdk.241930
  CLC： TP391.4
- Received：04 November 2024，
  
  Published：15 January 2026
- 稿件说明：
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罗天,戴元杰,万煌等.状态空间结合注意力的高光谱—LiDAR协同分类[J].软件导刊,2026,25(01):158-165.

LUO Tian,DAI Yuanjie,WAN Huang,et al.Hyperspectral-LiDAR Collaborative Classification Combining State Space and Attention[J].Software Guide,2026,25(01):158-165.
罗天,戴元杰,万煌等.状态空间结合注意力的高光谱—LiDAR协同分类[J].软件导刊,2026,25(01):158-165. DOI： 10.11907/rjdk.241930.

LUO Tian,DAI Yuanjie,WAN Huang,et al.Hyperspectral-LiDAR Collaborative Classification Combining State Space and Attention[J].Software Guide,2026,25(01):158-165. DOI： 10.11907/rjdk.241930.

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