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Remote Sens. 2017, 9(8), 771; https://doi.org/10.3390/rs9080771

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

1
National-Local Joint Engineering Laboratory of Geo-Spatial Information Technology, Hunan University of Science and Technology, No. 1 Taoyuan Road, Xiangtan 411201, China
2
Department of Geography, University of Hawaii at Mānoa, 2424 Maile Way, Honolulu, HI 96822, USA
3
Department of Geography, University of Cincinnati, Braunstein Hall, 400E, Cincinnati, OH 45221, USA
4
School of Geography and Planning, Sun Yat-Sen University, 135 Xingangxi Road, Guangzhou 510275, China
*
Authors to whom correspondence should be addressed.
Received: 10 May 2017 / Revised: 12 July 2017 / Accepted: 21 July 2017 / Published: 28 July 2017
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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. View Full-Text
Keywords: airborne LiDAR data; power line classification; urban power line; neighborhood selection; spatial structural feature airborne LiDAR data; power line classification; urban power line; neighborhood selection; spatial structural feature
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Wang, Y.; Chen, Q.; Liu, L.; Zheng, D.; Li, C.; Li, K. Supervised Classification of Power Lines from Airborne LiDAR Data in Urban Areas. Remote Sens. 2017, 9, 771.

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