Journal of Infrared and Millimeter Waves, Volume. 42, Issue 2, 250(2023)

Spaceborne photon counting lidar point cloud denoising method with the adaptive mountain slope

Guang-Hui HE1,2, Hong WANG1,2、*, Qiang FANG1,2, Yong-An ZHANG1,2, Dan-Lu ZHAO1,2, and Ya-Ping ZHANG1,2
Author Affiliations
  • 1School of Science, Kunming University of Science and Technology, Kunming 650500, China
  • 2Yunnan Provincial Key Laboratory of Modern Information Optics, Kunming University of Science and Technology, Kunming 650500, China
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    Figures & Tables(16)
    Point cloud coarse denoising results
    Point cloud fine removal noise process
    Schematic diagram of slope angle calculation of point cloud data and data segmentation processing diagram,(a) the angle of the elliptical domain under different slope angles, (b) slope angle calculation, (c) data segmentation, (d) consolidation of data segments
    The density cluster threshold is determined
    Specific clustering removal noise process
    Point cloud data fine removal noise
    ICESat-2 satellite transit area in the Area of Yellowstone National Park and Great Smoky Mountains Forest Park in the United States
    The spaceborne photon counting lidar matches the NEON data
    ATLAS data matches onboard data
    Denoising results of different denoising algorithms: (a) (d) (g) (j) the result of the algorithm of this paper processing Data1-4, (b) (e) (h) (k) the result of the LOF algorithm processing Data1-4, (c) (f) (i) (l) the result of the DBSCAN algorithm processing Data1-4
    Comparison of ground curves for different data
    Comparison of crown curves in different data
    The algorithm in this paper is compared with ATL08 data,(a)、(c)、(e)、(g) the results of processing and classifying Data1-4 for the algorithm herein, (b)、(d)、(f)、(h) the ATL08 data corresponding to Data1-4
    • Table 1. Comparison of different algorithms

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      Table 1. Comparison of different algorithms

      本文算法LOF算法DBSCAN算法
      PRFPRFPRF
      Data10.8780.9910.9310.66710.80.7990.9990.888
      Data20.8910.9990.9420.84810.9180.87710.934
      Data30.8510.9990.9190.84510.9160.860.9520.904
      Data40.7060.9660.8160.4290.950.5910.6380.9740.771
    • Table 2. Comparison of the efficiency of elliptic area clustering algorithms and slope adaptive elliptic area clustering algorithms

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      Table 2. Comparison of the efficiency of elliptic area clustering algorithms and slope adaptive elliptic area clustering algorithms

      坡度角自适应的椭圆域聚类算法椭圆域聚类算法
      PRFT(s)PRFT(s)
      Data10.8780.9910.9312.5310.81710.89916.766
      Data20.8910.9990.9424.7810.8730.9990.932115.234
      Data30.8510.9990.9193.4530.84610.91752.578
      Data40.7060.9660.8164.1880.6850.980.80666.297
    • Table 3. In this paper, the algorithmic processing results are compared with the ATL08 data

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      Table 3. In this paper, the algorithmic processing results are compared with the ATL08 data

      本文算法ATL08数据
      RMSE(m)R2Bias(m)RMSE(m)R2Bias(m)
      Data1地面0.358 80.999 7-0.019 90.732 30.998 70.207 5
      冠层3.744 90.968 6-1.688 717.7610.276 1-16.850 4
      Data2地面0.9180.999-0.035 21.3120.9980.079 7
      冠层4.349 10.977 7-2.593 67.259 60.936 4-5.033
      Data3地面1.732 30.993-0.365 12.833 10.976 20.912 4
      冠层4.397 40.95-0.868 65.110 40.94-0.161 4
      Data4地面2.177 50.999 4-0.568 712.121 90.980 27.887 9
      冠层5.967 80.994 7-2.415 29.627 40.9860.355 5
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    Guang-Hui HE, Hong WANG, Qiang FANG, Yong-An ZHANG, Dan-Lu ZHAO, Ya-Ping ZHANG. Spaceborne photon counting lidar point cloud denoising method with the adaptive mountain slope[J]. Journal of Infrared and Millimeter Waves, 2023, 42(2): 250

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    Paper Information

    Category: Research Articles

    Received: Sep. 6, 2022

    Accepted: --

    Published Online: Jul. 19, 2023

    The Author Email: Hong WANG (wanghongee@163.com)

    DOI:10.11972/j.issn.1001-9014.2023.02.016

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