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SCIENCE CHINA Information Sciences, Volume 61, Issue 1: 018102(2018) https://doi.org/10.1007/s11432-016-9085-y

Model-based variational fusion for reducing spectraldistortion

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  • ReceivedApr 25, 2016
  • AcceptedMar 16, 2017
  • PublishedJul 11, 2017

Abstract

There is no abstract available for this article.


Acknowledgment

This work was supported in part by National Natural Science Foundation of China (Grant Nos. 61473310, 41174164, 41175025) and China Meteorological Administration (Grant No. GYHY201306068).


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References

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  • Table 1   Quality assessment of different fusionmethods for each dataset
    rmDataset rmMethod rmsCC rmERGAS rmSAM rmQ4 rmQNR $D_\lambda$ $D_s$
    IKONOS rmGIHS 0.994 5.803 5.222 0.850 0.807 0.053 0.148
    rmGIHSA 0.989 4.145 4.441 0.858 0.764 0.105 0.146
    rmAdaptive IHS 0.987 4.834 4.586 0.852 0.864 0.042 0.098
    rmTVR 0.989 4.392 4.939 0.844 0.777 0.082 0.153
    rmMTF-CON 0.920 3.455 4.441 0.841 0.797 0.099 0.115
    rmProposed 0.970 3.451 3.574 0.899 0.890 0.028 0.085
    QuickBird rmGIHS 0.969 2.357 2.008 0.916 0.776 0.088 0.149
    rmGIHSA 0.975 1.507 1.605 0.919 0.768 0.139 0.109
    rmAdaptive IHS 0.942 1.503 1.420 0.922 0.842 0.084 0.080
    rmTVR 0.937 1.457 1.545 0.918 0.871 0.054 0.079
    rmMTF-CON 0.908 1.101 1.331 0.918 0.796 0.129 0.087
    rmProposed 0.912 1.048 1.096 0.937 0.915 0.038 0.049
    GeoEye-1 rmGIHS 0.991 4.384 3.384 0.944 0.779 0.115 0.121
    rmGIHSA 0.991 2.713 2.529 0.948 0.806 0.102 0.103
    rmAdaptive IHS 0.968 3.049 2.597 0.948 0.879 0.050 0.074
    rmTVR 0.986 2.472 2.568 0.948 0.855 0.069 0.083
    rmMTF-CON 0.929 2.059 2.340 0.947 0.801 0.116 0.094
    rmProposed 0.966 1.960 1.800 0.968 0.898 0.045 0.060

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