Laser & Optoelectronics Progress, Volume. 56, Issue 6, 061101(2019)

Quality Assessment of Remote Sensing Images Based on Deep Learning and Human Visual System

Di Liu and Yingchun Li*
Author Affiliations
  • Department of Electronic and Optical Engineering, Space Engineering University, Beijing 101416, China
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    Figures & Tables(14)
    Overall structure of assessment model
    Single-channel assessment framework based on parallel CNN
    VGG16 network structure
    Influences of blur and noise on visual characteristics of remote sensing images. (a) Original image; (b) image with noise; (c) image with noise once more; (d) image with blur; (e) image with blur once more
    Remote sensing images with different texture complexity. (a) S=9.2784; (b) S=23.1248; (c) S=19.0502; (d) S=14.9255; (e) S=20.1074; (f) S=13.2125; (g) S=23.2592; (h) S=18.7957
    Remote sensing images acquired by QuickBird-2 satellite. (a) Harbor; (b) vegetation; (c) road; (d) buildings
    Overall assessment results by proposed method
    Fitting scatter plot between proposed method and SSIM, PSNR, FSIM. (a) SSIM; (b) PSNR; (c) FSIM
    Fitting curves of DMOS for different assessment methods in LIVEMD dataset. (a) SSIM; (b) proposed method; (c) PSNR; (d) FSIM
    • Table 1. Blur level

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      Table 1. Blur level

      i12345
      Bblur_i54321
      Blurintensity01234
    • Table 2. Noise level

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      Table 2. Noise level

      j12345
      Nnoise_j54321
      Noiseintensity01234
    • Table 3. ITU-R quality and impairment scales

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      Table 3. ITU-R quality and impairment scales

      Five-grade scale54321
      QualityExcellentGoodFairPoorBad
      ImpairmentImperceptiblePerceptible,but not annoyingSlightlyannoyingAnnoyingVeryannoying
    • Table 4. Performance comparison of different methods

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      Table 4. Performance comparison of different methods

      MethodPLCCRMSESROCC
      SSIM0.89790.56310.8714
      PSNR0.84500.68420.7713
      FSIM0.89780.56020.8793
      Proposed0.90040.55660.8962
    • Table 5. Performance comparison of different methods in LIVEMD database

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      Table 5. Performance comparison of different methods in LIVEMD database

      MethodPLCCRMSESROCC
      SSIM0.767911.9487-0.6953
      PSNR0.775711.7910-0.7088
      FSIM0.817810.7358-0.8642
      Proposed0.89688.2523-0.8664
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    Di Liu, Yingchun Li. Quality Assessment of Remote Sensing Images Based on Deep Learning and Human Visual System[J]. Laser & Optoelectronics Progress, 2019, 56(6): 061101

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    Paper Information

    Category: Imaging Systems

    Received: Sep. 18, 2018

    Accepted: Sep. 30, 2018

    Published Online: Jul. 30, 2019

    The Author Email: Li Yingchun (13910953181@139.com)

    DOI:10.3788/LOP56.061101

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