Acta Optica Sinica, Volume. 41, Issue 12, 1228002(2021)

SAR-Assisted Optical Remote Sensing Image Cloud Removal Method Based on Deep Learning

Mengyao Wang, Xiangchao Meng*, Feng Shao**, and Randi Fu
Author Affiliations
  • Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, Zhejiang 315211, China
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    Figures & Tables(7)
    Overall flowchart of proposed method
    Image samples
    Results of simulation experiments. (a) SAR images; (b) real optical images; (c) simulated optical images contaminated by clouds; (d) experimental results of cGAN model; (e) experimental results of pix2pix model; (f) experimental results of cGAN+SSIM model; (g) experimental results of proposed model
    Results of real experiments. (a) SAR images; (b) optical images without cloud cover in a similar time phase; (c) optical remote sensing images with cloud cover; (d) experimental results of cGAN model; (e) experimental results of pix2pix model; (f) experimental results of cGAN+SSIM model; (g) experimental results of proposed model
    • Table 1. Information about simulated experiments and real experiments

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      Table 1. Information about simulated experiments and real experiments

      DatasetSimulated experimentReal experiment
      SAROpticalSimulatedcorrupted opticalSARSimilar timephase opticalReal corruptedoptical
      Obtain time2020.04.292020.04.282020.04.282020.05.232020.05.132020.06.17
    • Table 2. Quantitative evaluation results of simulation experiments (mean and 95% confidence interval)

      View table

      Table 2. Quantitative evaluation results of simulation experiments (mean and 95% confidence interval)

      ModelProposed modelcGAN+SSIMpix2pixcGAN
      SSIMMean0.86550.8604¯0.85580.7241
      95% confidence interval(0.860,0.871)(0.855,0.866)¯(0.850,0.861)(0.718,0.730)
      CCMean0.84430.8399¯0.83490.3732
      95% confidence interval(0.836,0.852)(0.832,0.848)¯(0.827,0.843)(0.359,0.388)
      PSNRMean27.588627.5141¯27.321316.2218
      95% confidence interval(27.218,27.960)(27.126,27.902)¯(26.937,27.705)(16.158,16.285)
      ERGASMean20.224420.2382¯20.832676.6522
      95% confidence interval(19.640,20.809)(19.630,20.846)¯(20.208,21.457)(72.501,80.803)
      SAMMean1.91942.07042.0117¯6.5556
      95% confidence interval(1.874,1.965)(2.028,2.113)(1.979,2.056)¯(6.415,6.696)
    • Table 3. Quantitative evaluation results of real experiments (mean and 95% confidence interval)

      View table

      Table 3. Quantitative evaluation results of real experiments (mean and 95% confidence interval)

      ModelProposed modelcGAN+SSIMpix2pixcGAN
      SSIMMean0.74670.7371¯0.70730.6149
      95% confidence interval(0.737,0.756)(0.728,0.747)¯(0.697,0.718)(0.601,0.629)
      CCMean0.58200.5655¯0.54550.4398
      95% confidence interval(0.558,0.606)(0.542,0.589)¯(0.522,0.569)(0.415,0.465)
      PSNRMean23.803323.5446¯23.340920.5477
      95% confidence interval(23.448,24.158)(23.199,23.891)¯(22.998,23.684)(20.215,20.880)
      ERGASMean29.971931.0080¯31.420243.5042
      95% confidence interval(28.981,30.963)(30.062,31.955)¯(30.453,32.388)(42.023,44.986)
      SAMMean4.34234.70614.5815¯5.3864
      95% confidence interval(4.202,4.483)(4.570,4.842)(4.440,4.723)¯(5.213,5.560)
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    Mengyao Wang, Xiangchao Meng, Feng Shao, Randi Fu. SAR-Assisted Optical Remote Sensing Image Cloud Removal Method Based on Deep Learning[J]. Acta Optica Sinica, 2021, 41(12): 1228002

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    Paper Information

    Category: Remote Sensing and Sensors

    Received: Jan. 6, 2021

    Accepted: Feb. 1, 2021

    Published Online: Jun. 2, 2021

    The Author Email: Meng Xiangchao (mengxiangchao@nbu.edu.cn), Shao Feng (shaofeng@nbu.edu.cn)

    DOI:10.3788/AOS202141.1228002

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