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Remote Sens. 2014, 6(4), 3302-3320; doi:10.3390/rs6043302

Extraction of Urban Power Lines from Vehicle-Borne LiDAR Data

1,2,3,* , 1,2,3
,
1,2,3
and
1,2,3,*
1
Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing University, 163 Xianlin Avenue, Nanjing 210023, China
2
Collaborative Innovation Center for the South Sea Studies, Nanjing University, 163 Xianlin Avenue, Nanjing 210023, China
3
Department of Geographic Information Science, Nanjing University, 163 Xianlin Avenue, Nanjing 210023, China
*
Authors to whom correspondence should be addressed.
Received: 12 October 2013 / Revised: 26 February 2014 / Accepted: 24 March 2014 / Published: 11 April 2014
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Abstract

Airborne LiDAR has been traditionally used for power line cruising. Nevertheless, data acquisition with airborne LiDAR is constrained by the complex environments in urban areas as well as the multiple parallel line structures on the same power line tower, which means it is not directly applicable to the extraction of urban power lines. Vehicle-borne LiDAR system has its advantages upon airborne LiDAR and this paper tries to utilize vehicle-borne LiDAR data for the extraction of urban power lines. First, power line points are extracted using a voxel-based hierarchical method in which geometric features of each voxel are calculated. Then, a bottom-up method for filtering the power lines belonging to each power line is proposed. The initial clustering and clustering recovery procedures are conducted iteratively to identify each power line. The final experiment demonstrates the high precision of this technique. View Full-Text
Keywords: power line extraction; power line filtering; urban power line; vehicle-borne LiDAR data power line extraction; power line filtering; urban power line; vehicle-borne LiDAR data
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Cheng, L.; Tong, L.; Wang, Y.; Li, M. Extraction of Urban Power Lines from Vehicle-Borne LiDAR Data. Remote Sens. 2014, 6, 3302-3320.

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