• Acta Photonica Sinica
  • Vol. 51, Issue 6, 0610005 (2022)
Ming LI, Fan LIU*, and Jingzhi LI
Author Affiliations
  • College of Data Science,Taiyuan University of Technology,Jinzhong,Shanxi 030600,China
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    DOI: 10.3788/gzxb20225106.0610005 Cite this Article
    Ming LI, Fan LIU, Jingzhi LI. Combining Convolutional Attention Module and Convolutional Auto-encoder for Detail Injection Remote Sensing Image Fusion[J]. Acta Photonica Sinica, 2022, 51(6): 0610005 Copy Citation Text show less
    Convolutional attention module flowchart
    Fig. 1. Convolutional attention module flowchart
    Channel attention module flowchart
    Fig. 2. Channel attention module flowchart
    Spatial attention flowchart
    Fig. 3. Spatial attention flowchart
    Method flowchart
    Fig. 4. Method flowchart
    Labeled images of the network model
    Fig. 5. Labeled images of the network model
    Input images of the network model
    Fig. 6. Input images of the network model
    Fusion images with different filters
    Fig. 7. Fusion images with different filters
    Image index values with different number of iterations
    Fig. 8. Image index values with different number of iterations
    Roma source images and fusion result by different methods
    Fig. 9. Roma source images and fusion result by different methods
    United Arab Emirates source images and fusion result by different methods
    Fig. 10. United Arab Emirates source images and fusion result by different methods
    ValuesLaplaceMeanMorphologicalGaussian
    CC0.051 20.104 20.178 10.333 8
    Table 1. CC for PLH and MH̃
    ValuesLaplaceMeanMorphologicalGaussianIdeal
    CC0.723 10.681 80.899 00.921 81
    SAM2.119 02.304 33.765 82.099 00
    ERGAS8.921 68.150 05.452 64.901 80
    UIQI0.802 10.792 20.871 50.893 01
    AG0.016 50.029 30.063 60.062 91
    RASE8.690 28.732 910.222 08.183 00
    Table 2. Fusion image metrics with different filters
    MethodsERGASRASESAMUIQIAGCC
    IHS7.885 715.851 71.611 00.754 30.029 50.754 5
    BDSD4.756 29.518 12.188 50.927 10.028 20.932 2
    MTF-GLP-HPM4.643 59.874 51.433 40.930 30.036 50.935 4
    SR-D7.368 227.234 21.788 90.795 70.019 90.846 6
    PNN4.654 89.781 91.516 70.901 10.031 00.928 1
    Di-PNN4.086 58.568 91.361 50.942 20.036 00.921 1
    GAN3.354 78.056 93.056 90.923 50.041 40.943 5
    CAE10.315 538.779 02.922 50.650 00.029 80.673 9
    Proposed3.242 88.137 21.316 70.929 10.043 60.934 1
    Ideal000111
    Table 3. Performance comparison of fusion results of Roma source images
    MethodsERGASRASESAMUIQIAGCC
    IHS5.562 412.301 50.450 10.842 10.014 40.841 2
    BDSD2.551 65.454 60.965 90.879 80.009 20.924 9
    MTF-GLP-HPM4.124 09.647 00.970 60.919 40.016 80.923 2
    SR-D8.411 715.447 91.251 50.859 40.011 90.886 0
    PNN4.096 89.601 91.134 10.920 00.016 70.924 3
    Di-PNN3.432 95.646 81.362 10.941 80.007 70.939 8
    GAN3.757 38.649 60.992 20.932 70.015 50.936 0
    CAE6.745 219.509 52.009 40.766 60.014 50.773 2
    Proposed1.078 94.757 80.264 10.953 90.007 70.944 0
    Ideal000111
    Table 4. Performance comparison of fusion results of United Arab Emirates source images
    Ming LI, Fan LIU, Jingzhi LI. Combining Convolutional Attention Module and Convolutional Auto-encoder for Detail Injection Remote Sensing Image Fusion[J]. Acta Photonica Sinica, 2022, 51(6): 0610005
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