• Laser & Optoelectronics Progress
  • Vol. 56, Issue 16, 162802 (2019)
Chengbin Xing, Xingsheng Deng*, and Kang Xu
Author Affiliations
  • Department of Surveying and Mapping Engineering, School of Traffic & Transportation Engineering, Changsha University of Science and Technology, Changsha, Hunan 410004, China
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    DOI: 10.3788/LOP56.162802 Cite this Article Set citation alerts
    Chengbin Xing, Xingsheng Deng, Kang Xu. Contour Determination Method for Threshold of Morphological Filtering Key Parameters[J]. Laser & Optoelectronics Progress, 2019, 56(16): 162802 Copy Citation Text show less
    Progressive filtering algorithm implementation process
    Fig. 1. Progressive filtering algorithm implementation process
    Contour generation flow chart
    Fig. 2. Contour generation flow chart
    Aerial photography of the survey area
    Fig. 3. Aerial photography of the survey area
    Contour to determine the shape of the stadium
    Fig. 4. Contour to determine the shape of the stadium
    Point cloud contour filling distribution in the building area
    Fig. 5. Point cloud contour filling distribution in the building area
    LiDAR point cloud distribution in the building area
    Fig. 6. LiDAR point cloud distribution in the building area
    Tilt angle to determine the same height difference
    Fig. 7. Tilt angle to determine the same height difference
    Isometric line-morphological filtering algorithm flow chart
    Fig. 8. Isometric line-morphological filtering algorithm flow chart
    Contour determination window threshold
    Fig. 9. Contour determination window threshold
    Distribution of point cloud
    Fig. 10. Distribution of point cloud
    Samp23 survey area before and after filtering. (a) Before filtering; (b) after filtering
    Fig. 11. Samp23 survey area before and after filtering. (a) Before filtering; (b) after filtering
    Filtering accuracy of different height difference thresholds (window threshold is 30 m)
    Fig. 12. Filtering accuracy of different height difference thresholds (window threshold is 30 m)
    Filtering accuracy of different window thresholds (height difference threshold is 20 m)
    Fig. 13. Filtering accuracy of different window thresholds (height difference threshold is 20 m)
    Filtering results of different algorithms in the FSite8_red2 survey area. (a) TIN algorithm; (b) morphology algorithm
    Fig. 14. Filtering results of different algorithms in the FSite8_red2 survey area. (a) TIN algorithm; (b) morphology algorithm
    Samp23 surveypoint cloudNumber ofsample pointsNumber ofground pointsNumber offeaturesTypeI error /%TypeII error /%
    Sample point250951322311872
    Calculated point25095114521364313.314.9
    Table 1. Number of point cloud points in Samp23 survey area and two types of error statistics
    Samp41 surveypoint cloudNumber ofsample pointsNumber ofground pointsNumber offeaturesType Ierror /%Type IIerror /%
    Sample point1123156025629
    Morphology filtering112316294493712.312.2
    TIN filtering112316732449920.120.0
    Table 2. Two algorithms in Samp41 data point ground point, feature point, type I error, and type II error
    Chengbin Xing, Xingsheng Deng, Kang Xu. Contour Determination Method for Threshold of Morphological Filtering Key Parameters[J]. Laser & Optoelectronics Progress, 2019, 56(16): 162802
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