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Open AccessArticle

Automatic Extraction of High-Voltage Power Transmission Objects from UAV Lidar Point Clouds

1
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
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Engineering Research Center of Space-Time Data Capturing and Smart Application, The Ministry of Education, Wuhan 430079, China
3
School of Engineering, Newcastle University, Newcastle Upon Tyne NE1 7RU, UK
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Nanjing Foreign Language School British Columbia Academy, Nanjing 210016, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(22), 2600; https://doi.org/10.3390/rs11222600
Received: 4 October 2019 / Revised: 1 November 2019 / Accepted: 3 November 2019 / Published: 6 November 2019
(This article belongs to the Special Issue Point Cloud Processing and Analysis in Remote Sensing)
Electric power transmission and maintenance is essential for the power industry. This paper proposes a method for the efficient extraction and classification of three-dimensional (3D) targets of electric power transmission facilities based on regularized grid characteristics computed from point cloud data acquired by unmanned aerial vehicles (UAVs). First, a special hashing matrix was constructed to store the point cloud after noise removal by a statistical method, which calculated the local distribution characteristics of the points within each sparse grid. Secondly, power lines were extracted by neighboring grids’ height similarity estimation and linear feature clustering. Thirdly, by analyzing features of the grid in the horizontal and vertical directions, the transmission towers in candidate tower areas were identified. The pylon center was then determined by a vertical slicing analysis. Finally, optimization was carried out, considering the topological relationship between the line segments and pylons to refine the extraction. Experimental results showed that the proposed method was able to efficiently obtain accurate coordinates of pylon and attachments in the massive point data and to produce a reliable segmentation with an overall precision of 97%. The optimized algorithm was capable of eliminating interference from isolated tall trees and communication signal poles. The 3D geo-information of high-voltage (HV) power lines, pylons, conductors thus extracted, and of further reconstructed 3D models can provide valuable foundations for UAV remote-sensing inspection and corridor safety maintenance. View Full-Text
Keywords: transmission tower; power line; feature extraction; pylon detection; reconstruction transmission tower; power line; feature extraction; pylon detection; reconstruction
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MDPI and ACS Style

Zhang, R.; Yang, B.; Xiao, W.; Liang, F.; Liu, Y.; Wang, Z. Automatic Extraction of High-Voltage Power Transmission Objects from UAV Lidar Point Clouds. Remote Sens. 2019, 11, 2600.

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