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基于框架域的随机游走全色锐化方法

王敬凯 杨小远

王敬凯, 杨小远. 基于框架域的随机游走全色锐化方法[J]. 北京航空航天大学学报, 2017, 43(4): 709-719. doi: 10.13700/j.bh.1001-5965.2016.0311
引用本文: 王敬凯, 杨小远. 基于框架域的随机游走全色锐化方法[J]. 北京航空航天大学学报, 2017, 43(4): 709-719. doi: 10.13700/j.bh.1001-5965.2016.0311
WANG Jingkai, YANG Xiaoyuan. Framelet-based random walk pan-sharpening method[J]. Journal of Beijing University of Aeronautics and Astronautics, 2017, 43(4): 709-719. doi: 10.13700/j.bh.1001-5965.2016.0311(in Chinese)
Citation: WANG Jingkai, YANG Xiaoyuan. Framelet-based random walk pan-sharpening method[J]. Journal of Beijing University of Aeronautics and Astronautics, 2017, 43(4): 709-719. doi: 10.13700/j.bh.1001-5965.2016.0311(in Chinese)

基于框架域的随机游走全色锐化方法

doi: 10.13700/j.bh.1001-5965.2016.0311
基金项目: 

国家自然科学基金 61671002

北京市自然科学基金 4152029

详细信息
    作者简介:

    王敬凯, 男, 博士研究生。主要研究方向:多源遥感图像融合

    杨小远, 女, 博士, 教授, 博士生导师。主要研究方向:应用调和分析与图像处理

    通讯作者:

    杨小远, E-mail:xiaoyuanyang@vip.163.com

  • 中图分类号: O29

Framelet-based random walk pan-sharpening method

Funds: 

National Natural Science Foundation of China 61671002

Beijing Municipal Natural Science Foundation 4152029

More Information
  • 摘要:

    针对多光谱图像与全色图像的融合问题,提出了一种新的全色锐化方法。该方法首先通过亮度、色调、饱和度(IHS)变换与非下采样框架变换将原图像从空间域变换到框架域,然后利用基于图论的随机游走,建立高频框架系数的统计融合模型。此模型根据高频框架系数的邻域相关性与尺度相关性构造新的随机游走协调函数,将高频框架系数融合权重的估计转化为随机游走标记问题的求解。实验结果表明,该方法有利于保持图像的光谱信息和边缘轮廓信息,可以在降低融合图像光谱误差的同时提高空间分辨率,并且优于一些主流全色锐化方法。

     

  • 图 1  基于图论的随机游走

    Figure 1.  Random walk based on graph theory

    图 2  高频框架系数的邻域相关性与尺度相关性

    Figure 2.  Neighborhood correlation and scale correlation of high frequency framelet coefficients

    图 3  全色图像与多光谱图像

    Figure 3.  Panchromatic images and multispectral images

    图 4  评价指标随k1k2的变化

    Figure 4.  Variation of evaluation indices with k1 and k2

    图 5  融合图像

    Figure 5.  Fusion images

    表  1  不同分解尺度的融合结果

    Table  1.   Fusion results for different decomposition level

    图像 J RASE (0) ERGAS (0) SCC (1)
    PY-PAN+PY-MS4 2 3.378 8 0.845 8 0.972 0
    3 4.336 5 1.084 2 0.978 9
    4 5.012 0 1.251 5 0.979 6
    WZ-PAN+WZ-MS 2 6.176 5 3.092 5 0.970 6
    3 9.135 6 4.566 7 0.976 9
    4 10.217 4 5.106 1 0.977 4
    下载: 导出CSV

    表  2  取不同k2的融合结果

    Table  2.   Fusion results for different k2

    图像 k2 RASE (0) ERGAS (0) SCC (1)
    PY-PAN+PY-MS 0.1 3.796 0 0.950 1 0.993 1
    0.5 3.544 8 0.887 4 0.985 3
    0.9 3.403 2 0.852 0 0.974 9
    1.0 3.378 8 0.845 8 0.972 0
    1.1 3.358 4 0.840 7 0.969 2
    1.5 3.289 3 0.823 6 0.957 2
    1.9 3.243 3 0.812 2 0.944 8
    2.0 3.232 6 0.809 5 0.941 7
    WZ-PAN+WZ-MS 0.1 9.374 3 4.686 2 0.991 8
    0.5 7.468 1 3.735 7 0.985 1
    0.9 6.377 2 3.192 3 0.973 8
    1.0 6.176 5 3.092 5 0.970 6
    1.1 5.996 6 3.003 0 0.967 3
    1.5 5.439 3 2.725 9 0.953 0
    1.9 5.062 7 2.538 8 0.938 1
    2.0 4.986 7 2.501 1 0.934 3
    下载: 导出CSV

    表  3  不同融合方法的融合结果

    Table  3.   Fusion results of different fusion methods

    图像 融合方法 RASE (0) ERGAS (0) SCC (1)
    PY-PAN+PY-MS GS 14.453 6 3.626 0 0.998 8
    IHS 13.193 0 3.272 8 0.999 6
    MTF+GLP 3.950 3 0.990 7 0.966 1
    AWT+CDWL 4.964 8 1.237 3 0.903 9
    AWT+SDM 6.793 1 1.695 0 0.993 9
    NSCT 7.863 2 1.995 9 0.999 0
    NFLT+CI 3.959 7 0.990 7 0.960 0
    SRDIP 3.742 8 0.935 3 0.948 0
    RW 9.485 9 2.355 0 0.986 5
    NFT+RW (k2=0.1) 3.796 0 0.950 1 0.993 1
    NFT+RW (k2=0.5) 3.544 8 0.887 4 0.985 3
    NFT+RW (k2=0.9) 3.403 2 0.852 0 0.974 9
    WZ-PAN+WZ-MS GS 32.363 8 16.231 3 0.990 1
    IHS 33.678 2 16.822 8 0.999 5
    MTF+GLP 6.440 0 3.232 3 0.868 9
    AWT+CDWL 13.256 7 6.614 3 0.931 9
    AWT+SDM 18.870 9 9.419 8 0.949 8
    NSCT 16.173 3 8.102 5 0.996 6
    NFLT+CI 6.437 4 3.223 8 0.956 3
    SRDIP 7.792 3 3.891 6 0.943 9
    RW 21.363 4 10.672 8 0.982 4
    NFT+RW (k2=0.1) 9.374 3 4.686 2 0.991 8
    NFT+RW (k2=0.5) 7.468 1 3.735 7 0.985 1
    NFT+RW (k2=0.9) 6.377 2 3.192 3 0.973 8
    下载: 导出CSV

    表  4  不同融合方法的运行时间

    Table  4.   Elapsed time of different fusion methodss

    融合方法 PY图像融合 WZ图像融合
    GS 0.195 6 0.232 6
    IHS 0.114 3 0.117 6
    MTF+GLP 73.718 5 72.531 1
    AWT+CDWL 0.835 5 0.877 4
    AWT+SDM 0.718 5 0.779 5
    NSCT 4.854 4 4.873 6
    NFLT+CI 0.557 0 0.394 8
    SRDIP 1 243.125 6 1 306.896 6
    RW 1.652 0 1.666 5
    NFT+RW (k2=0.1) 7.513 5 7.232 2
    NFT+RW (k2=0.5) 7.510 9 7.254 2
    NFT+RW (k2=0.9) 7.530 1 7.375 4
    下载: 导出CSV
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出版历程
  • 收稿日期:  2016-04-18
  • 录用日期:  2016-06-02
  • 网络出版日期:  2017-04-20

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