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Open AccessArticle

Classification of Power Facility Point Clouds from Unmanned Aerial Vehicles Based on Adaboost and Topological Constraints

1
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
2
Department of Built Environment, University of New South Wales, Sydney 2052, Australia
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Beijing New3S Technology Pty. Ltd., Beijing 100085, China
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Land Satellite Remote Sensing Application Centre, MNR, Beijing 100048, China
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Faculty of Business Administration, The Chinese University of Hong Kong, Hong Kong 999077, China
*
Authors to whom correspondence should be addressed.
Sensors 2019, 19(21), 4717; https://doi.org/10.3390/s19214717
Received: 1 September 2019 / Revised: 22 October 2019 / Accepted: 28 October 2019 / Published: 30 October 2019
(This article belongs to the Section Remote Sensors, Control, and Telemetry)
Machine learning algorithms can be well suited to LiDAR point cloud classification, but when they are applied to the point cloud classification of power facilities, many problems such as a large number of computational features and low computational efficiency can be encountered. To solve these problems, this paper proposes the use of the Adaboost algorithm and different topological constraints. For different objects, the top five features with the best discrimination are selected and combined into a strong classifier by the Adaboost algorithm, where coarse classification is performed. For power transmission lines, the optimum scales are selected automatically, and the coarse classification results are refined. For power towers, it is difficult to distinguish the tower from vegetation points by only using spatial features due to the similarity of their proposed key features. Therefore, the topological relationship between the power line and power tower is introduced to distinguish the power tower from vegetation points. The experimental results show that the classification of power transmission lines and power towers by our method can achieve the accuracy of manual classification results and even be more efficient. View Full-Text
Keywords: point cloud classification; power line; power tower; the Adaboost algorithm; topological constraint point cloud classification; power line; power tower; the Adaboost algorithm; topological constraint
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Liu, Y.; Aleksandrov, M.; Zlatanova, S.; Zhang, J.; Mo, F.; Chen, X. Classification of Power Facility Point Clouds from Unmanned Aerial Vehicles Based on Adaboost and Topological Constraints. Sensors 2019, 19, 4717.

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