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Collaborative classification of hyperspectral and LiDAR data based on CNN-transformer
Information Sciences | 更新时间：2024-05-06
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    • Collaborative classification of hyperspectral and LiDAR data based on CNN-transformer

    • “多模态分类任务在遥感图像处理领域取得了新突破。针对高光谱图像（HSI）与LiDAR数据融合处理的挑战，研究者提出了一种基于对比学习的CNN-Transformer协同分类网络——CLCT-Net。该网络通过创新的共有特征提取模块，实现了不同传感器数据间的语义对齐，解决了跨模态信息表达和特征对齐的难题。研究者设计了包含空间-通道分支和光谱上下文分支的双分支HSI编码器，以及结合频域自注意力机制的LiDAR编码器，从而充分提取并整合了两种数据的丰富特征。同时，他们巧妙地集成了对比学习方法，进一步提高了多模态数据协同分类的精度。在Houston 2013和Trento数据集上的实验验证，CLCT-Net模型的地物分类精度分别高达92.01%和98.90%，显著超越了其他同类模型。这一成果不仅为跨模态数据特征的深度挖掘和协同提取提供了有力工具，也为遥感图像处理领域的多模态分类任务研究开辟了新的方向。”
      ”
    • Optics and Precision Engineering   Vol. 32, Issue 7, Pages: 1087-1100(2024)

    • DOI：10.37188/OPE.20243207.1087    

      CLC： TP394.1;TH691.9

    • Published：10 April 2024，

      Received：23 October 2023，

      Revised：20 November 2023，

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  • WU Haibin,DAI Shiyu,WANG Aili,et al.Collaborative classification of hyperspectral and LiDAR data based on CNN-transformer[J].Optics and Precision Engineering,2024,32(07):1087-1100. DOI： 10.37188/OPE.20243207.1087.

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