Abstract

Abstract. UAV LiDAR systems have unique advantage in acquiring 3D geo-information of the targets and the expenses are very reasonable; therefore, they are capable of security inspection of high-voltage power lines. There are already several methods for power line extraction from LiDAR point cloud data. However, the existing methods either introduce classification errors during point cloud filtering, or occasionally unable to detect multiple power lines in vertical arrangement. This paper proposes and implements an automatic power line extraction method based on 3D spatial features. Different from the existing power line extraction methods, the proposed method processes the LiDAR point cloud data vertically, therefore, the possible location of the power line in point cloud data can be predicted without filtering. Next, segmentation is conducted on candidates of power line using 3D region growing method. Then, linear point sets are extracted by linear discriminant method in this paper. Finally, power lines are extracted from the candidate linear point sets based on extension and direction features. The effectiveness and feasibility of the proposed method were verified by real data of UAV LiDAR point cloud data in Sichuan, China. The average correct extraction rate of power line points is 98.18%.

Highlights

  • Power line is the essential infrastructure for most of our social economic activities

  • Power line extraction methods based on supervised classification were presented

  • This paper proposes and implements an automatic power line extraction method based on 3D spatial features using UAV LiDAR point cloud data

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Summary

Introduction

Power line is the essential infrastructure for most of our social economic activities. The methods for power line detection based on airborne LiDAR point cloud data can be summarized into two categories: Linear feature detection methods and supervised classification methods. Kim (Kim, 2013) proposed a point-based supervised classification method for power line extraction in airborne LiDAR data. Guo (Guo, 2013) proposed a supervised classification method to identify power lines, pylons, buildings, ground and vegetation from point cloud data using a JointBoost classifier. This paper presents an automatic extracting power line method from UAV LiDAR data based on 3D spatial features of the power lines, which does not need point cloud filtering or training classification models. This paper proposes and implements an automatic power line extraction method based on 3D spatial features using UAV LiDAR point cloud data.

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