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Research and Application of Automatic Classification Method for Patrol Targets of Transmission Lines Based on LiDAR Point Cloud

  • Chen Zhang
  • Yong Li
  • Xia Zhou
  • Ji YangEmail author
  • Chuanxun Yang
  • Hongkai Liu
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 980)

Abstract

The development of airborne laser radar technology and cloud data processing technology provides a new technical means for the acquisition and processing of geospatial 3D data. Based on the self-developed LiDAR UAV, this paper proposes a new point cloud automatic classification method (TL-PCACM) of transmission lines by using the improved fast 3D convex hull construction algorithm, k-means clustering analysis algorithm and Region-growing algorithm, which have high classification accuracy, good classification effect and fast processing speed. The accuracy of point cloud automatic classification is over 95%, the efficiency can reach 60 km/h. The proposed method is applied to the analysis of tree obstacles under transmission lines. The efficiency of the automatic identification of tree obstacles can reach 720 towers/hour. The accuracy can reach 50 cm. It provides high efficiency, high quality, fully automatic and intelligent data processing method for the classification of tree obstacles, effectively improving the processing accuracy and speed of geospatial 3D information data.

Keywords

Airborne laser radar UAV Transmission line Point cloud automatic classification Analysis of tree obstacles 

Notes

Acknowledgments

The research is supported by Implementation of Innovation Driven Development Capacity Building Special Funds of Guangdong Academy of Sciences (2017GDASCX-0101, 2017GDASCX-0601), Guangdong Innovative and Entrepreneurial Research Team Program (2016ZT06D336), Guangzhou Science and Technology Program (201604016047, 201806010106), Guangdong Science and Technology Program (2017B010117008), Guangzhou Hydraulic Technological Innovation Project (MZSK-2016-01, SW-2018-01), National Natural Science Foundation of China (41401430), Key Lab of Guangdong for Utilization of Remote Sensing and Geographical Information System (2017B030314138).

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Chen Zhang
    • 1
  • Yong Li
    • 1
  • Xia Zhou
    • 1
  • Ji Yang
    • 1
    Email author
  • Chuanxun Yang
    • 1
  • Hongkai Liu
    • 1
  1. 1.Guangzhou Institute of Geography, Key Lab of Guangdong for Utilization of Remote Sensing and Geographical Information System, Guangdong Open Laboratory of Geospatial Information Technology and ApplicationGuangzhouChina

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