Laser & Optoelectronics Progress, Volume. 58, Issue 14, 1428001(2021)

Adaptive Denoising Algorithm for Photon-Counting LiDAR Point Clouds

Chunhui Wang1,2、*, Aoyou Wang1,2, Wei Rong1,2, Yuliang Tao1,2, and Ruimin Fu1
Author Affiliations
  • 1Beijing Institute of Space Mechanics and Electricity, Beijing 100094, China
  • 2Key Laboratory for Space Laser Information Perception Technology, China Academy of Space Technology, Beijing 100094, China
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    Figures & Tables(13)
    Airport building photos and point cloud data. (a) Airport building; (b) point cloud data
    Change process of the search area shape
    Flow of the denoising algorithm
    Statistical histogram of the neighborhood density
    Neighborhood density of noise points after fitting
    Denoising result of the point cloud. (a) Noise and signal points after processing; (b) partial enlarged view
    Fitting result of the airport building contour
    Processing results of the MABEL point cloud data. (a) No.6; (b) No.8; (c) No.3; (d) No.9
    • Table 1. Fitting error of the airport building contour

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      Table 1. Fitting error of the airport building contour

      Serial numberNumber of signal pointsStandard deviation /m
      1900.21
      21040.23
      36320.13
      4700.15
      5810.27
      61070.24
      7990.18
      8860.16
      9750.22
      Sum13440.18
    • Table 2. Effect of θ on algorithm recognition rate and accuracy

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      Table 2. Effect of θ on algorithm recognition rate and accuracy

      θ/(°)h/ml/mμσFPFNTPR/%P/%
      30.224.26.52.311452292998.2696.25
      50.283.26.52.315218295799.3995.11
      100.402.36.52.31794298199.8794.34
    • Table 3. Effect of search area on algorithm recognition rate and accuracy

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      Table 3. Effect of search area on algorithm recognition rate and accuracy

      θ/(°)h/ml/mμσFPFNTPR/%P/%
      50.283.26.52.315218295799.3995.11
      50.354.09.42.916827294499.0994.60
      50.424.813.03.530741292198.6290.49
    • Table 4. MABEL point cloud data

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      Table 4. MABEL point cloud data

      Serial numberFlight timeScenarioNumber of points
      120120412T1644northwest Greenland25001
      220120412T1659central Greenland50000
      320120420T0954sea ice around Greenland40000
      420120420T1004edge of Greenland50001
      520120915T2300water15113
      620130919T1512vegetation, day30108
      720130920T2225vegetation, night33759
      820130927T1856land56201
      920140729T2106north pole40690
    • Table 5. Processing parameters and results of MABEL point cloud data

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      Table 5. Processing parameters and results of MABEL point cloud data

      Serial numberθ/(°)h/ml/mμσFPFNTPR/%P/%
      152.630.05.42.026733394899.1793.67
      252.630.06.62.31744180499.7891.20
      352.630.06.32.21382131699.8590.51
      452.630.06.62.31323130099.7790.78
      5513.1150.04.31.84351742699.9994.47
      653.945.04.71.960221564996.2498.95
      757.990.05.32.0142591914899.6999.26
      853.135.05.62.2352230374894.2291.41
      953.135.06.32.320130184298.4090.16
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    Chunhui Wang, Aoyou Wang, Wei Rong, Yuliang Tao, Ruimin Fu. Adaptive Denoising Algorithm for Photon-Counting LiDAR Point Clouds[J]. Laser & Optoelectronics Progress, 2021, 58(14): 1428001

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    Paper Information

    Category: Remote Sensing and Sensors

    Received: Sep. 27, 2020

    Accepted: Nov. 12, 2020

    Published Online: Jul. 14, 2021

    The Author Email: Chunhui Wang (xjtuchwang@foxmail.com)

    DOI:10.3788/LOP202158.1428001

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