An enhanced descriptor extraction algorithm for power line detection from point clouds
Danesh Shokri
Department of Photogrammetry and Remote Sensing, School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran
Search for more papers by this authorCorresponding Author
Heidar Rastiveis
Department of Photogrammetry and Remote Sensing, School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran
Lyles School of Civil Engineering, Purdue University, West Lafayette, Indiana, USA
Correspondence
Heidar Rastiveis, Lyles School of Civil Engineering, Purdue University, 550 Stadium Mall Dr., West Lafayette, IN 47907, USA.
Email: [email protected]
Search for more papers by this authorWayne A. Sarasua
Glenn Department of Civil Engineering, Clemson University, Clemson, South Carolina, USA
Search for more papers by this authorSaeid Homayouni
Centre Eau Terre Environnement, Institut National de la Recherche Scientifique (INRS), Quebec, Quebec, Canada
Search for more papers by this authorBenyamin Hosseiny
Department of Photogrammetry and Remote Sensing, School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran
Search for more papers by this authorAlireza Shams
Department of Environmental and Civil Engineering, Mercer University, Macon, Georgia, USA
Search for more papers by this authorDanesh Shokri
Department of Photogrammetry and Remote Sensing, School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran
Search for more papers by this authorCorresponding Author
Heidar Rastiveis
Department of Photogrammetry and Remote Sensing, School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran
Lyles School of Civil Engineering, Purdue University, West Lafayette, Indiana, USA
Correspondence
Heidar Rastiveis, Lyles School of Civil Engineering, Purdue University, 550 Stadium Mall Dr., West Lafayette, IN 47907, USA.
Email: [email protected]
Search for more papers by this authorWayne A. Sarasua
Glenn Department of Civil Engineering, Clemson University, Clemson, South Carolina, USA
Search for more papers by this authorSaeid Homayouni
Centre Eau Terre Environnement, Institut National de la Recherche Scientifique (INRS), Quebec, Quebec, Canada
Search for more papers by this authorBenyamin Hosseiny
Department of Photogrammetry and Remote Sensing, School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran
Search for more papers by this authorAlireza Shams
Department of Environmental and Civil Engineering, Mercer University, Macon, Georgia, USA
Search for more papers by this authorFunding information: This research received no external funding.
Abstract
Mobile terrestrial laser scanning (MTLS) systems provide a safe and efficient means to survey roadway corridors at high speed. MTLS point clouds are rich in planimetric data. However, manual extraction of useful information from these point clouds can be time consuming and laborious and automated object extraction from MTLS point clouds has become a hot topic in the remote sensing community. This study proposes an automated method for power line extraction from MTLS point clouds based on a multilayer perceptron (MLP) neural network. The proposed method consists of three main steps: (i) point cloud preprocessing, (ii) descriptor extraction and selection, and (iii) point classification. The preprocessing step involves filtering out more than 90% of the point cloud by eliminating the vast majority of unneeded points. Next, various descriptors are extracted from the remaining points including planarity, linearity, and verticality, and the descriptor standard deviation is used to select the best-suited descriptors for power line extraction. Finally, an MLP neural network is trained using the selected descriptors from several cable and noncable sample points. The proposed algorithm was evaluated in three MTLS point clouds in urban and nonurban environments totalling 5.5 kilometres in length. An average precision of 94% and a recall of 94% showed the algorithm’s reliability and feasibility.
CONFLICT OF INTEREST STATEMENT
The authors declare no conflict of interest.
Open Research
DATA AVAILABILITY STATEMENT
No data are available.
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