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Proceedings Paper

Classification of LiDAR data based on region segmentation and decision tree
Author(s): Kai-si Liu; Yan-bing Wang; Hui-li Gong
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Paper Abstract

Aiming at spatial characteristics and echo information of the LiDAR point cloud data, design a regional segmentation and decision tree combined lidar point data classification method. First, based on the continuity of the LiDAR point cloud to finish the experiment area's region segmentation. Then, statistics each area boundaries and internal the number of dihedral angle cosine, to draw a line chart. Using the intersection's cosine of line chart , and region segmentation's minimum height as threshold to determine the ground point and the non-ground points. Finally, statistics separately all LiDAR point data set's dihedral angle, echo times, echo intensity, mean elevation, four constraint information to build a decision tree to determine which type of feature vesting each divided region. Using classification confusion matrix to assess the classification's accuracy, overall accuracy is higher than 94%. Experimental results show that this method can effectively separate roads, trees, buildings and terrain.

Paper Details

Date Published: 26 November 2014
PDF: 8 pages
Proc. SPIE 9262, Lidar Remote Sensing for Environmental Monitoring XIV, 926213 (26 November 2014); doi: 10.1117/12.2069203
Show Author Affiliations
Kai-si Liu, Capital Nomal Univ. (China)
Yan-bing Wang, Capital Normal Univ. (China)
Hui-li Gong, Capital Normal Univ. (China)


Published in SPIE Proceedings Vol. 9262:
Lidar Remote Sensing for Environmental Monitoring XIV
Upendra N. Singh; Kazuhiro Asai, Editor(s)

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