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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。
Abstract:A novel pan-sharpening method was proposed for the fusion of multispectral image and panchromatic image. First, the original image was transformed from spatial domain to framelet domain by intensity, hue, saturation (IHS) transform and non-subsampled framelet transform. Second, the statistical fusion model of high frequency framelet coefficients was established with the random walk method based on graph theory. On basis of the neighborhood correlation and scale correlation of high frequency framelet coefficients, the novel compatibility function for random walk was constructed. Finally, the fusion weight estimation of high frequency framelet coefficients was translated into the solution of the random walk labeled problem. Experimental results show that the proposed method is beneficial to keep the spectral information and edge contour information of the image. It can reduce the spectral distortion while improve the spatial resolution simultaneously, and it outperforms the other state-of-the-art pan-sharpening methods.
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Key words:
- pan-sharpening /
- framelet transform /
- random walk /
- multispectral image /
- panchromatic image
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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
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 
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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
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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
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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
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