Laser & Optoelectronics Progress, Volume. 60, Issue 10, 1028004(2023)

Non-Subsampling Shearlet Transform Remote Sensing Image Fusion with Improved Dual-channel Adaptive Pulse Coupled Neural Network

Linian Ruan and Yan Dong*
Author Affiliations
  • Faculty of Land and Resources Engineering, Kunming University of Science and Technology, Kunming 650032, Yunnan, China
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    Figures & Tables(17)
    Frequency domain subdivision diagram and support interval of NSST. (a) Frequency domain subdivision map; (b) frequency domain support interval
    Architecture of DC-PCNN
    Fusion results of high-frequency coefficients in all directions. (a)-(b) High frequency subbands in 2 directions in first layer; (c)-(f) high frequency subbands in 4 directions in second layer
    Direction information calculation
    Flow chart
    Influence of decomposition layers on fusion effect
    First group of experimental data. (a) MS; (b) PAN
    Fusion results of first group. (a) SE; (b) NSCT; (c) ISCM; (d) WDCPAPCNN; (e) PAPCNN; (f) proposed method
    Second group of experimental data. (a) MS; (b) PAN
    Fusion results of second group. (a) SE; (b) NSCT; (c) ISCM; (d) WDCPAPCNN; (e) PAPCNN; (f) proposed method
    Third group of experimental data. (a) MS; (b) PAN
    Fusion results of third group. (a) SE; (b) NSCT; (c) ISCM; (d) WDCPAPCNN; (e) PAPCNN; (f) proposed method
    • Table 1. Quantitative evaluation of spatial information in all directions

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      Table 1. Quantitative evaluation of spatial information in all directions

      IndexMethod123456
      AGDCPCNN0.00870.00720.00430.00330.00360.0044
      IDCPCNN0.00670.00560.00170.00230.00160.0024
      SFDCPCNN0.02350.01900.01490.01150.01230.0138
      IDCPCNN0.02160.01730.00950.00980.00790.0102
      STDDCPCNN0.02110.01630.00940.00710.00740.0084
      IDCPCNN0.01810.01400.00640.00600.00510.0061
    • Table 2. Settings of NSST decomposition layers and corresponding directions

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      Table 2. Settings of NSST decomposition layers and corresponding directions

      Decomposition layersDirection number
      116
      216,16
      316,16,8
      416,16,8,8
      516,16,8,8,4
    • Table 3. Quantitative evaluation of first group of experiments

      View table

      Table 3. Quantitative evaluation of first group of experiments

      ImageMethodAG↑SF↑QAB/FVIFF↑EFMI↑
      1SE0.00680.01970.31270.56344.47440.9078
      NSCT0.01630.04070.70080.95236.82870.8894
      ISCM0.01380.03700.62610.89666.92630.9122
      WDCPA-PCNN0.01260.03130.50400.84576.92160.8986
      PAPCNN0.01690.04260.60500.94626.97460.9034
      Proposed method0.01750.04500.63530.99016.98230.9126
      2SE0.00780.01520.40830.58346.48310.8717
      NSCT0.01430.03810.68020.86306.36220.8555
      ISCM0.01260.03250.61130.84216.54960.8685
      WDCPA-PCNN0.01240.03060.51780.84356.66250.8534
      PAPCNN0.01560.03930.58000.89466.58520.8394
      Proposed method0.01660.04470.66480.89936.58020.8744
    • Table 4. Quantitative evaluation of second group of experiments

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      Table 4. Quantitative evaluation of second group of experiments

      ImageMethodAG↑SF↑QAB/FVIFF↑EFMI↑
      1SE0.00650.01530.38030.62605.58790.9325
      NSCT0.00820.02130.55220.46426.01600.9117
      ISCM0.00690.01800.39180.50786.17480.9234
      WDCPA-PCNN0.00700.01830.36300.50416.19240.9163
      PAPCNN0.00870.02220.52810.54956.19910.9122
      Proposed method0.00900.02400.60870.56896.17540.9250
      2SE0.00510.01130.42570.61676.01260.8975
      NSCT0.01370.03640.52151.07166.44050.8705
      ISCM0.01130.03010.43131.01546.57790.8877
      WDCPA-PCNN0.01250.03240.49541.02056.60320.8732
      PAPCNN0.01550.04100.51071.01506.64620.8523
      Proposed method0.01580.04260.49551.05006.63290.8886
    • Table 5. Quantitative evaluation of third group of experiments

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      Table 5. Quantitative evaluation of third group of experiments

      ImageMethodAG↑SF↑QAB/FVIFF↑EFMI↑
      1SE0.00330.00840.55250.70995.87430.9318
      NSCT0.00490.01570.64770.98205.82770.9404
      ISCM0.00450.01520.65620.97486.01930.9447
      WDCPA-PCNN0.00450.01240.52410.88576.09500.9248
      PAPCNN0.00510.01430.58140.98276.04430.9318
      Proposed method0.00530.01640.64060.98986.03160.9447
      2SE0.00470.00930.53020.75735.77090.9059
      NSCT0.00510.01230.67890.61245.36450.8900
      ISCM0.00490.01100.61910.69215.55130.9063
      WDCPA-PCNN0.00580.01410.59050.79715.66670.8825
      PAPCNN0.00570.01320.60540.73945.55130.8936
      Proposed method0.00610.01460.66930.74745.57510.9061
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    Linian Ruan, Yan Dong. Non-Subsampling Shearlet Transform Remote Sensing Image Fusion with Improved Dual-channel Adaptive Pulse Coupled Neural Network[J]. Laser & Optoelectronics Progress, 2023, 60(10): 1028004

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    Paper Information

    Category: Remote Sensing and Sensors

    Received: Nov. 3, 2021

    Accepted: Feb. 14, 2022

    Published Online: May. 17, 2023

    The Author Email: Dong Yan (dongyanchina@sina.com)

    DOI:10.3788/LOP212866

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