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Acta Optica Sinica, Volume. 41, Issue 7, 0710001(2021)

Pansharpening Based on Multi-Branch CNN

Hongbin Wang, Song Xiao**, Jiahui Qu*, Wenqian Dong, and Tongzhen Zhang
Author Affiliations
  • State Key Laboratory of Integrated Services Networks, Xidian University, Xi′an, Shaanxi 710071, China
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    Network structure in proposed algorithm

    Fig. 1. Network structure in proposed algorithm

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    Fusion results of 40th image in Pavia Center dataset by different algorithms. (a) Reference image; (b) proposed algorithm; (c) SFIM; (d) MTF-GLP-HPM; (e) Bayesian sparse; (f) GSA; (g) PCA; (h) CNMF

    Fig. 2. Fusion results of 40th image in Pavia Center dataset by different algorithms. (a) Reference image; (b) proposed algorithm; (c) SFIM; (d) MTF-GLP-HPM; (e) Bayesian sparse; (f) GSA; (g) PCA; (h) CNMF

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    Fusion results of 24th image in Pavia Center dataset by different algorithms. (a) Reference image; (b) proposed algorithm; (c) SFIM; (d) MTF-GLP-HPM; (e) Bayesian sparse; (f) GSA; (g) PCA; (h) CNMF

    Fig. 3. Fusion results of 24th image in Pavia Center dataset by different algorithms. (a) Reference image; (b) proposed algorithm; (c) SFIM; (d) MTF-GLP-HPM; (e) Bayesian sparse; (f) GSA; (g) PCA; (h) CNMF

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    Fusion results of 24th image in CAVE dataset by different algorithms. (a) Reference image; (b) proposed algorithm; (c) SFIM; (d) MTF-GLP-HPM; (e) Bayesian sparse; (f) GSA; (g) PCA; (h) CNMF

    Fig. 4. Fusion results of 24th image in CAVE dataset by different algorithms. (a) Reference image; (b) proposed algorithm; (c) SFIM; (d) MTF-GLP-HPM; (e) Bayesian sparse; (f) GSA; (g) PCA; (h) CNMF

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    Fusion results of 5th image in CAVE dataset by different algorithms. (a) Reference image; (b) proposed algorithm; (c) SFIM; (d) MTF-GLP-HPM; (e) Bayesian sparse; (f) GSA; (g) PCA; (h) CNMF

    Fig. 5. Fusion results of 5th image in CAVE dataset by different algorithms. (a) Reference image; (b) proposed algorithm; (c) SFIM; (d) MTF-GLP-HPM; (e) Bayesian sparse; (f) GSA; (g) PCA; (h) CNMF

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    • Table 1. Evaluation results on Pavia Center dataset

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      Table 1. Evaluation results on Pavia Center dataset

      ParameterBayesiansparseCNMFGSAMTF-GLP-HPMPCASFIMProposedalgorithm
      CC0.93370.92510.94480.96750.91450.96310.9791
      SAM8.32967.12367.42546.22977.61626.25084.9153
      RMSE0.03630.03850.03170.02500.03700.02670.0185
      ERGAS5.72245.78654.91693.62905.74434.01592.8287

    • Table 2. Evaluation results on CAVE dataset

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      Table 2. Evaluation results on CAVE dataset

      ParameterBayesiansparseCNMFGSAMTF-GLP-HPMPCASFIMProposedalgorithm
      CC0.99420.99250.99360.98840.98930.98950.9968
      SAM5.56196.61335.88926.58776.50126.10814.4402
      RMSE0.01400.01680.01630.01990.02300.01930.0123
      ERGAS3.64713.98273.85745.38174.16755.08522.6174
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    Hongbin Wang, Song Xiao, Jiahui Qu, Wenqian Dong, Tongzhen Zhang. Pansharpening Based on Multi-Branch CNN[J]. Acta Optica Sinica, 2021, 41(7): 0710001

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    Paper Information

    Category: Image Processing

    Received: Oct. 14, 2020

    Accepted: Nov. 11, 2020

    Published Online: Apr. 11, 2021

    The Author Email: Xiao Song (xiaosong@mail.xidian.edu.cn), Qu Jiahui (jhqu@xidian.edu.cn)

    DOI:10.3788/AOS202141.0710001

    Topics

    laser devices and laser physics
    Lasers and Laser Optics
    Laser physics
    laser manufacturing
    Instrumentation, Measurement and Metrology