Supervised Classification of Power Lines from Airborne LiDAR Data in Urban Areas

Yanjun Wang,Kai Li,Lin Liu,Qi Chen,Chaokui Li,Dunyong Zheng

doi:10.3390/rs9080771

Abstract

Automatic extraction of power lines using airborne LiDAR (Light Detection and Ranging) data has been one of the most important topics for electric power management. However, this is very challenging over complex urban areas, where power lines are in close proximity to buildings and trees. In this paper, we presented a new, semi-automated and versatile framework that consists of four steps: (i) power line candidate point filtering, (ii) local neighborhood selection, (iii) spatial structural feature extraction, and (iv) SVM classification. We introduced the power line corridor direction for candidate point filtering and multi-scale slant cylindrical neighborhood for spatial structural features extraction. In a detailed evaluation involving seven scales and four types for local neighborhood selection, 26 structural features, and two datasets, we demonstrated that the use of multi-scale slant cylindrical neighborhood for individual 3D points significantly improved the power line classification. The experiments indicated that precision, recall and quality rate of power line classification is more than 98%, 98% and 97%, respectively. Additionally, we showed that our approach can reduce the whole processing time while achieving high accuracy.

Highlights

Inspection of power lines to detect and eliminate hidden risks is an important task for urban and rural power supply management and scientific planning [1,2]
The conventional methods for power line extraction include: (i) statistical analysis of point clouds based on height, density or number of pulses, etc. [5,6,7]; (ii) Hough transform and clustering based on 2D image processing [5,8,9,10,11]; (iii) supervised classification based on geometrical and distribution features of laser points [2,12,13,14]
We proposed an accurate and fast power line classification method that works over complex urban scenes

Summary

Introduction

Inspection of power lines to detect and eliminate hidden risks is an important task for urban and rural power supply management and scientific planning [1,2]. Airborne LiDAR data volume is large and power lines are usually close to vegetation and buildings over urban areas. These make it difficult to extract urban power line points accurately and quickly from LiDAR point cloud. The development of highly efficient, rapid and automated methods for extracting urban power lines from airborne LiDAR point cloud data is a critical issue. The conventional methods for power line extraction include: (i) statistical analysis of point clouds based on height, density or number of pulses, etc. The conventional methods for power line extraction include: (i) statistical analysis of point clouds based on height, density or number of pulses, etc. [5,6,7]; (ii) Hough transform and clustering based on 2D image processing [5,8,9,10,11]; (iii) supervised classification based on geometrical and distribution features of laser points [2,12,13,14]

Methods

Results

Discussion

Conclusion