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基于三维Saab变换的高光谱图像压缩方法

徐艾明 黄宇星 沈秋

徐艾明, 黄宇星, 沈秋等 . 基于三维Saab变换的高光谱图像压缩方法[J]. 北京航空航天大学学报, 2022, 48(8): 1505-1514. doi: 10.13700/j.bh.1001-5965.2021.0521
引用本文: 徐艾明, 黄宇星, 沈秋等 . 基于三维Saab变换的高光谱图像压缩方法[J]. 北京航空航天大学学报, 2022, 48(8): 1505-1514. doi: 10.13700/j.bh.1001-5965.2021.0521
XU Aiming, HUANG Yuxing, SHEN Qiuet al. Hyperspectral image compression method based on 3D Saab transform[J]. Journal of Beijing University of Aeronautics and Astronautics, 2022, 48(8): 1505-1514. doi: 10.13700/j.bh.1001-5965.2021.0521(in Chinese)
Citation: XU Aiming, HUANG Yuxing, SHEN Qiuet al. Hyperspectral image compression method based on 3D Saab transform[J]. Journal of Beijing University of Aeronautics and Astronautics, 2022, 48(8): 1505-1514. doi: 10.13700/j.bh.1001-5965.2021.0521(in Chinese)

基于三维Saab变换的高光谱图像压缩方法

doi: 10.13700/j.bh.1001-5965.2021.0521
基金项目: 

国家自然科学基金 U1936202

国家自然科学基金 62071216

详细信息
    通讯作者:

    沈秋, E-mail: shenqiu@nju.edu.cn

  • 中图分类号: V221+.3; TB553

Hyperspectral image compression method based on 3D Saab transform

Funds: 

National Natural Science Foundation of China U1936202

National Natural Science Foundation of China 62071216

More Information
  • 摘要:

    高光谱图像中存储了丰富的光谱信息,具有极大的应用价值,但现有大部分高光谱图像压缩方法难以同时兼顾图像中的空间冗余与谱间冗余,导致压缩性能受到局限。针对该问题,提出了一种基于三维修正偏置的子空间(Saab)变换的高光谱图像压缩方法。采用三维Saab变换对高光谱图像的分块进行空间光谱信息融合的降维操作,同时去除谱间冗余和局部空间冗余;利用高效率视频编码(HEVC)中的帧内编码模块进一步去除空间冗余和统计冗余;实现低失真、高比率的高光谱图像压缩。在多个高光谱图像数据集上的实验结果表明,所提方法在同码率下重建图像的信噪比(SNR)比采用主成分分析(PCA)降维的方法至少提高0.62 dB,在高码率的情况下性能优于张量分解的压缩方法。同时,验证了不同降维方法对分类任务的性能影响,结果表明,所提方法更好地保留了图像中的重要特征,在低码率的情况下仍可以保持较高的分类精度。

     

  • 图 1  基于三维Saab变换的高光谱图像压缩方法流程

    Figure 1.  Flowchart of hyperspectral image compression method based on 3D Saab transform

    图 2  空间光谱信息融合

    Figure 2.  Fusion of spatial and spectral information

    图 3  Salinas数据集伪彩色图

    Figure 3.  Pseudo RGB image of Salinas dataset

    图 4  PaviaU数据集伪彩色图

    Figure 4.  Pseudo RGB image of PaviaU dataset

    图 5  Botswana数据集伪彩色图

    Figure 5.  Pseudo RGB image of Botswana dataset

    图 6  不同方法在Salinas数据集上的压缩性能

    Figure 6.  Compression performance of different methods on Salinas dataset

    图 7  本文方法和PCA在不同数据集上的压缩性能

    Figure 7.  Compression performance of the proposed method and PCA on different datasets

    图 8  在Salinas数据集重建图像上的分类精度

    Figure 8.  Classification accuracy of reconstructed image on Salinas dataset

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  • 被引次数: 0
出版历程
  • 收稿日期:  2021-09-06
  • 录用日期:  2021-10-01
  • 刊出日期:  2021-10-29

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