• Acta Optica Sinica
  • Vol. 39, Issue 2, 0228001 (2019)
Shujuan Yang1、2、*, Keshu Zhang2、*, and Yongshe Shao2
Author Affiliations
  • 1 University of Chinese Academy of Sciences, Beijing 100049, China
  • 2 Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China
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    DOI: 10.3788/AOS201939.0228001 Cite this Article Set citation alerts
    Shujuan Yang, Keshu Zhang, Yongshe Shao. Classification of Airborne LiDAR Point Cloud Data Based on Multiscale Adaptive Features[J]. Acta Optica Sinica, 2019, 39(2): 0228001 Copy Citation Text show less
    Local space coordinate system of adjacent points
    Fig. 1. Local space coordinate system of adjacent points
    Classification of bush area. (a) Large scale; (b) small scale
    Fig. 2. Classification of bush area. (a) Large scale; (b) small scale
    Classification of building. (a) Large scale; (b) small scale
    Fig. 3. Classification of building. (a) Large scale; (b) small scale
    Flowchart of multiscale adaptive-feature classification
    Fig. 4. Flowchart of multiscale adaptive-feature classification
    Experimental data
    Fig. 5. Experimental data
    Distribution of feature importance for different feature combinations
    Fig. 6. Distribution of feature importance for different feature combinations
    Classification results of different feature sets. (a) Classical geometric statistical feature set; (b) PFH feature set; (c) combinational feature set
    Fig. 7. Classification results of different feature sets. (a) Classical geometric statistical feature set; (b) PFH feature set; (c) combinational feature set
    Distribution of feature importance at different scales
    Fig. 8. Distribution of feature importance at different scales
    Classification results based on feature set with different scales. (a) Large scale; (b) small scale; (c) multiscale
    Fig. 9. Classification results based on feature set with different scales. (a) Large scale; (b) small scale; (c) multiscale
    Classification results of C area based on feature set with different scales. (a) Large scale; (b) small scale; (c) multiscale
    Fig. 10. Classification results of C area based on feature set with different scales. (a) Large scale; (b) small scale; (c) multiscale
    Classification results of D area based on feature set with different scales. (a) Large scale; (b) small scale; (c) multiscale
    Fig. 11. Classification results of D area based on feature set with different scales. (a) Large scale; (b) small scale; (c) multiscale
    CategoryClassification of classical geometric statistical feature set /%Classification of PFH feature set /%Classification of combinational feature set /%
    Ground93.2693.3293.43
    Vegetable91.5290.1291.65
    Building90.6492.4392.72
    Table 1. Classification accuracy for different feature sets
    Scale /mClassification accuracy /%
    GroundVegetableBuilding
    0.1-1.095.8395.3694.86
    0.185.3386.2979.42
    0.392.6494.7582.32
    0.586.7289.6385.94
    0.882.5387.4989.87
    1.078.6882.6392.12
    Table 2. Classification accuracy at different scales
    Shujuan Yang, Keshu Zhang, Yongshe Shao. Classification of Airborne LiDAR Point Cloud Data Based on Multiscale Adaptive Features[J]. Acta Optica Sinica, 2019, 39(2): 0228001
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