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Classification of Power Facility Point Clouds from Unmanned Aerial Vehicles Based on Adaboost and Topological Constraints
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 prob...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6864668/ https://www.ncbi.nlm.nih.gov/pubmed/31671626 http://dx.doi.org/10.3390/s19214717 |
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author | Liu, Yuxuan Aleksandrov, Mitko Zlatanova, Sisi Zhang, Junjun Mo, Fan Chen, Xiaojian |
author_facet | Liu, Yuxuan Aleksandrov, Mitko Zlatanova, Sisi Zhang, Junjun Mo, Fan Chen, Xiaojian |
author_sort | Liu, Yuxuan |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-6864668 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-68646682019-12-23 Classification of Power Facility Point Clouds from Unmanned Aerial Vehicles Based on Adaboost and Topological Constraints Liu, Yuxuan Aleksandrov, Mitko Zlatanova, Sisi Zhang, Junjun Mo, Fan Chen, Xiaojian Sensors (Basel) Article 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. MDPI 2019-10-30 /pmc/articles/PMC6864668/ /pubmed/31671626 http://dx.doi.org/10.3390/s19214717 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Liu, Yuxuan Aleksandrov, Mitko Zlatanova, Sisi Zhang, Junjun Mo, Fan Chen, Xiaojian Classification of Power Facility Point Clouds from Unmanned Aerial Vehicles Based on Adaboost and Topological Constraints |
title | Classification of Power Facility Point Clouds from Unmanned Aerial Vehicles Based on Adaboost and Topological Constraints |
title_full | Classification of Power Facility Point Clouds from Unmanned Aerial Vehicles Based on Adaboost and Topological Constraints |
title_fullStr | Classification of Power Facility Point Clouds from Unmanned Aerial Vehicles Based on Adaboost and Topological Constraints |
title_full_unstemmed | Classification of Power Facility Point Clouds from Unmanned Aerial Vehicles Based on Adaboost and Topological Constraints |
title_short | Classification of Power Facility Point Clouds from Unmanned Aerial Vehicles Based on Adaboost and Topological Constraints |
title_sort | classification of power facility point clouds from unmanned aerial vehicles based on adaboost and topological constraints |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6864668/ https://www.ncbi.nlm.nih.gov/pubmed/31671626 http://dx.doi.org/10.3390/s19214717 |
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