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Laser & Optoelectronics Progress, Volume. 59, Issue 10, 1001001(2022)

DEM Construction for Airborne LiDAR Data Based on Combined Filtering Algorithm

Xiangyong Tian1,2, Hong Hu1,2、*, and Bangxin Xu3
Author Affiliations
  • 1School of Resources and Environmental Engineering, Anhui University, Hefei 230601, Anhui , China
  • 2Anhui Province Engineering Laboratory for Mine Ecological Remediation, Hefei 230601, Anhui , China
  • 3Anhui Electric Power Design Institute Co., Ltd., China Energy Engineering Group, Hefei 230601, Anhui , China
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    AbstractGet PDF(in Chinese)
    Figures&Tables (14)
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    References (21)
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    Figures & Tables(14)
    Schematic of progressive morphological filtering

    Fig. 1. Schematic of progressive morphological filtering

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    Schematic of post-processing filtering based on space vector projection

    Fig. 2. Schematic of post-processing filtering based on space vector projection

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    Original point cloud data. (a) Samp21; (b) Samp31; (c) Samp51; (d) Samp52; (e) Samp53; (f) Samp54

    Fig. 3. Original point cloud data. (a) Samp21; (b) Samp31; (c) Samp51; (d) Samp52; (e) Samp53; (f) Samp54

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    Ground point cloud extracted by single progressive morphological filtering. (a) Samp21; (b) Samp31; (c) Samp51; (d) Samp52; (e) Samp53; (f) Samp54

    Fig. 4. Ground point cloud extracted by single progressive morphological filtering. (a) Samp21; (b) Samp31; (c) Samp51; (d) Samp52; (e) Samp53; (f) Samp54

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    Ground point cloud extracted by progressive morphological filtering and post-processing filtering. (a) Samp21; (b) Samp31; (c) Samp51; (d) Samp52; (e) Samp53; (f) Samp54

    Fig. 5. Ground point cloud extracted by progressive morphological filtering and post-processing filtering. (a) Samp21; (b) Samp31; (c) Samp51; (d) Samp52; (e) Samp53; (f) Samp54

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    1 m×1 m resolution DEM and linear regression analysis for Samp21. (a) DSM; (b) reference DEM; (c) DEM without post-processing filtering; (d) DEM with post-processing filtering; (e) linear regression analysis of Fig.6(c) to Fig.6(b); (f) linear regression analysis of Fig.6(d) to Fig.6(b)

    Fig. 6. 1 m×1 m resolution DEM and linear regression analysis for Samp21. (a) DSM; (b) reference DEM; (c) DEM without post-processing filtering; (d) DEM with post-processing filtering; (e) linear regression analysis of Fig.6(c) to Fig.6(b); (f) linear regression analysis of Fig.6(d) to Fig.6(b)

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    1 m×1 m resolution DEM and linear regression analysis for Samp31. (a) DSM; (b) reference DEM; (c) DEM without post-processing filtering; (d) DEM with post-processing filtering; (e) linear regression analysis of Fig.7(c) to Fig.7(b); (f) linear regression analysis of Fig.7(d) to Fig.7(b)

    Fig. 7. 1 m×1 m resolution DEM and linear regression analysis for Samp31. (a) DSM; (b) reference DEM; (c) DEM without post-processing filtering; (d) DEM with post-processing filtering; (e) linear regression analysis of Fig.7(c) to Fig.7(b); (f) linear regression analysis of Fig.7(d) to Fig.7(b)

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    1 m×1 m resolution DEM and linear regression analysis for Samp51. (a) DSM; (b) reference DEM; (c) DEM without post-processing filtering; (d) DEM with post-processing filtering; (e) linear regression analysis of Fig.8(c) to Fig.8(b); (f) linear regression analysis of Fig.8(d) to Fig.8(b)

    Fig. 8. 1 m×1 m resolution DEM and linear regression analysis for Samp51. (a) DSM; (b) reference DEM; (c) DEM without post-processing filtering; (d) DEM with post-processing filtering; (e) linear regression analysis of Fig.8(c) to Fig.8(b); (f) linear regression analysis of Fig.8(d) to Fig.8(b)

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    1 m×1 m resolution DEM and linear regression analysis for Samp54. (a)DSM; (b) reference DEM; (c) DEM without post-processing filtering; (d) DEM with post-processing filtering; (e) linear regression analysis of Fig.9(c) to Fig.9(b); (f) linear regression analysis of Fig.9(d) to Fig.9(b)

    Fig. 9. 1 m×1 m resolution DEM and linear regression analysis for Samp54. (a)DSM; (b) reference DEM; (c) DEM without post-processing filtering; (d) DEM with post-processing filtering; (e) linear regression analysis of Fig.9(c) to Fig.9(b); (f) linear regression analysis of Fig.9(d) to Fig.9(b)

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    1 m×1 m resolution DEM and linear regression analysis for Samp52. (a) DSM; (b) reference DEM; (c) DEM without post-processing filtering; (d) DEM with post-processing filtering; (e) linear regression analysis of Fig.10(c) to Fig.10(b); (f) linear regression analysis of Fig.10(d) to Fig.10(b)

    Fig. 10. 1 m×1 m resolution DEM and linear regression analysis for Samp52. (a) DSM; (b) reference DEM; (c) DEM without post-processing filtering; (d) DEM with post-processing filtering; (e) linear regression analysis of Fig.10(c) to Fig.10(b); (f) linear regression analysis of Fig.10(d) to Fig.10(b)

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    1 m×1 m resolution DEM and linear regression analysis for Samp53. (a) DSM; (b) reference DEM; (c) DEM without post-processing filtering; (d) DEM with post-processing filtering; (e) linear regression analysis of Fig.11(c) to Fig.11(b); (f) linear regression analysis of Fig.11(d) to Fig.11(b)

    Fig. 11. 1 m×1 m resolution DEM and linear regression analysis for Samp53. (a) DSM; (b) reference DEM; (c) DEM without post-processing filtering; (d) DEM with post-processing filtering; (e) linear regression analysis of Fig.11(c) to Fig.11(b); (f) linear regression analysis of Fig.11(d) to Fig.11(b)

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    • Table 1. Cross table

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      Table 1. Cross table

      Reference pointFiltered pointSum
      Ground pointsNon-ground points
      Ground pointsabe=a+b
      Non-ground pointscdf=c+d
      Sumg=a+ch=b+dn=a+b+c+d

    • Table 2. Parameter of point cloud filtering

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      Table 2. Parameter of point cloud filtering

      Samplewmax /msh0 /mhmax /md /ml /m
      Samp21200.30.536-12
      Samp31200.30.536-12
      Samp51201136-12
      Samp52201156-20
      Samp53101156-20
      Samp54100.50.536-12

    • Table 3. Errors, Kappa coefficient, R2, and RMSE of 6 groups of test data

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      Table 3. Errors, Kappa coefficient, R2, and RMSE of 6 groups of test data

      SampleNo post-processing filteringAdd post-processing filtering
      Ⅰ‍-typed error /%Ⅱ-typed error /%Total error /%KappaR2RMSEⅠ-typed error /%Ⅱ-typed error /%Total error /%KappaR2RMSE
      Samp210.586.051.790.94740.98520.08120.674.941.620.95260.99240.0567
      Samp310.184.982.390.95170.83930.42190.233.021.520.96940.98630.1087
      Samp510.2629.586.660.78250.99960.35210.712.533.280.90020.99990.1571
      Samp527.3639.210.710.48430.99751.38379.3324.5110.920.53250.99681.5816
      Samp533.6352.565.610.37720.99611.07946.3730.317.340.40100.99571.1347
      Samp540.6512.747.140.85770.99070.55261.685.563.760.92460.99810.2269
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    Xiangyong Tian, Hong Hu, Bangxin Xu. DEM Construction for Airborne LiDAR Data Based on Combined Filtering Algorithm[J]. Laser & Optoelectronics Progress, 2022, 59(10): 1001001

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    Paper Information

    Category: Atmospheric Optics and Oceanic Optics

    Received: Apr. 27, 2021

    Accepted: May. 21, 2021

    Published Online: May. 16, 2022

    The Author Email: Hong Hu (huhong@ahu.edu.cn)

    DOI:10.3788/LOP202259.1001001

    Topics

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