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

Authors :
Yanjun Wang
Dunyong Zheng
Chaokui Li
Kai Li
Qi Chen
Lin Liu
Source :
Remote Sensing. Aug2017, Vol. 9 Issue 8, p771. 16p.
Publication Year :
2017

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. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
20724292
Volume :
9
Issue :
8
Database :
Academic Search Index
Journal :
Remote Sensing
Publication Type :
Academic Journal
Accession number :
124868625
Full Text :
https://doi.org/10.3390/rs9080771