Fast extraction of power lines from mobile LiDAR point clouds based on SVM classification in non-urban area

Shokri, Danesh; Rastiveis, Heidar; Sheikholeslami, Seyed Mohammad; Shahhoseini, Reza; Li, Jonathan

doi:10.22059/eoge.2022.317348.1092

Fast extraction of power lines from mobile LiDAR point clouds based on SVM classification in non-urban area

Document Type : Original Article

Authors

Danesh Shokri ¹

Heidar Rastiveis ¹

Seyed Mohammad Sheikholeslami ²

Reza Shahhoseini ¹

Jonathan Li ³

¹ Department of Photogrammetry and Remote Sensing, School of Surveying and Geospatial Eng., University of Tehran, Tehran, Iran

² Department of Communications, School of Electrical and Computer Eng., Faculty of Eng., University of Tehran, Tehran, Iran

³ Dept. of Geography and Environmental Management, University of Waterloo, Waterloo, Ontario, Canada

10.22059/eoge.2022.317348.1092

Abstract

Mobile Laser Scanning (MLS) systems have been used for power line inspection in a fast and precise fashion. However, manually processing of huge LiDAR point clouds is tedious and time-consuming. Thus, an automated method is needed. This study proposes a machine learning-based method for automated detection of power lines from MLS point clouds. The proposed method consists of three main steps: pre-processing, line extraction using Support Vector Machine (SVM), and post-extraction. In the pre-processing step, noisy and low-height points are eliminated after sectioning the collected point clouds. This step considerably reduces the volume of point clouds by 90%. Then, the point features including linearity, planarity, verticality, and the largest component of Principal Component Analysis (PCA) are used as the best-fitted descriptors for power line detection. After training the SVM by a small section of points, SVM properly classified the point clouds with about 97% and 98% accuracies regarding precision and recall, respectively. In the final step, a post-extraction is required to eliminate false points in the power line class. This step improved the recall from 98% to 99.4% and decreased slightly the precision accuracy from 97% to 95.5%. The results demonstrated that the proposed method works rapidly, about 14 seconds per section with an average of 5 million points in each section.

Keywords

Point Clouds

Support Vector Machines (SVM)

Power Line Extraction

Cables

Mobile Laser Scanner (MLS)