Acta Optica Sinica, Volume. 39, Issue 2, 0228001(2019)
Classification of Airborne LiDAR Point Cloud Data Based on Multiscale Adaptive Features
Figures & Tables(13)
Fig. 7. Classification results of different feature sets. (a) Classical geometric statistical feature set; (b) PFH feature set; (c) combinational feature set
Fig. 9. Classification results 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
Fig. 11. Classification results of D area based on feature set with different scales. (a) Large scale; (b) small scale; (c) multiscale
Table 1. Classification accuracy for different feature sets
View tableTable 1. Classification accuracy for different feature sets
Category Classification of classical geometric statistical feature set /% Classification of PFH feature set /% Classification of combinational feature set /% Ground 93.26 93.32 93.43 Vegetable 91.52 90.12 91.65 Building 90.64 92.43 92.72
Table 2. Classification accuracy at different scales
View tableTable 2. Classification accuracy at different scales
Scale /m Classification accuracy /% Ground Vegetable Building 0.1-1.0 95.83 95.36 94.86 0.1 85.33 86.29 79.42 0.3 92.64 94.75 82.32 0.5 86.72 89.63 85.94 0.8 82.53 87.49 89.87 1.0 78.68 82.63 92.12
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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
Paper Information
Category: Remote Sensing and Sensors
Received: May. 21, 2018
Accepted: Jun. 17, 2018
Published Online: May. 10, 2019
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