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Article

Systematic Comparison of Power Line Classification Methods from ALS and MLS Point Cloud Data

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 and Environment, 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 Geographic Science, Center of Geo-Informatics for Public Security, Guangzhou University, 230 Guangzhou University City Outer Ring Road, Guangzhou 510006, China
5
School of Computer Science and Engineering, Hunan University of Science and Technology, No. 1 Taoyuan Road, Xiangtan 411201, China
6
School of Computing Science and Engineering, Vellore Institute of Technology (VIT), Vellore-632014, India
*
Authors to whom correspondence should be addressed.
Received: 17 July 2018 / Revised: 27 July 2018 / Accepted: 1 August 2018 / Published: 3 August 2018
Power lines classification is important for electric power management and geographical objects extraction using LiDAR (light detection and ranging) point cloud data. Many supervised classification approaches have been introduced for the extraction of features such as ground, trees, and buildings, and several studies have been conducted to evaluate the framework and performance of such supervised classification methods in power lines applications. However, these studies did not systematically investigate all of the relevant factors affecting the classification results, including the segmentation scale, feature selection, classifier variety, and scene complexity. In this study, we examined these factors systematically using airborne laser scanning and mobile laser scanning point cloud data. Our results indicated that random forest and neural network were highly suitable for power lines classification in forest, suburban, and urban areas in terms of the precision, recall, and quality rates of the classification results. In contrast to some previous studies, random forest yielded the best results, while Naïve Bayes was the worst classifier in most cases. Random forest was the more robust classifier with or without feature selection for various LiDAR point cloud data. Furthermore, the classification accuracies were directly related to the selection of the local neighborhood, classifier, and feature set. Finally, it was suggested that random forest should be considered in most cases for power line classification. View Full-Text
Keywords: laser scanning data; power line classification; random forest; feature selection; classifier laser scanning data; power line classification; random forest; feature selection; classifier
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MDPI and ACS Style

Wang, Y.; Chen, Q.; Liu, L.; Li, X.; Sangaiah, A.K.; Li, K. Systematic Comparison of Power Line Classification Methods from ALS and MLS Point Cloud Data. Remote Sens. 2018, 10, 1222. https://0-doi-org.brum.beds.ac.uk/10.3390/rs10081222

AMA Style

Wang Y, Chen Q, Liu L, Li X, Sangaiah AK, Li K. Systematic Comparison of Power Line Classification Methods from ALS and MLS Point Cloud Data. Remote Sensing. 2018; 10(8):1222. https://0-doi-org.brum.beds.ac.uk/10.3390/rs10081222

Chicago/Turabian Style

Wang, Yanjun, Qi Chen, Lin Liu, Xiong Li, Arun K. Sangaiah, and Kai Li. 2018. "Systematic Comparison of Power Line Classification Methods from ALS and MLS Point Cloud Data" Remote Sensing 10, no. 8: 1222. https://0-doi-org.brum.beds.ac.uk/10.3390/rs10081222

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