Open AccessAUTOMATIC RAILWAY POWER LINE EXTRACTION USING MOBILE LASER SCANNING DATA
Author(s) -
Shanxin Zhang,
Cheng Wang,
Yang Zhou,
Yiping Chen,
Jonathan Li
Publication year - 2016
Publication title -
the international archives of the photogrammetry, remote sensing and spatial information sciences/international archives of the photogrammetry, remote sensing and spatial information sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.264
H-Index - 71
eISSN - 1682-1777
pISSN - 1682-1750
DOI - 10.5194/isprsarchives-xli-b5-615-2016
Subject(s) - piecewise , point cloud , computer science , line (geometry) , laser scanning , power (physics) , point (geometry) , trajectory , artificial intelligence , computer vision , real time computing , laser , mathematics , optics , mathematical analysis , physics , geometry , quantum mechanics , astronomy
Research on power line extraction technology using mobile laser point clouds has important practical significance on railway power lines patrol work. In this paper, we presents a new method for automatic extracting railway power line from MLS (Mobile Laser Scanning) data. Firstly, according to the spatial structure characteristics of power-line and trajectory, the significant data is segmented piecewise. Then, use the self-adaptive space region growing method to extract power lines parallel with rails. Finally use PCA (Principal Components Analysis) combine with information entropy theory method to judge a section of the power line whether is junction or not and which type of junction it belongs to. The least squares fitting algorithm is introduced to model the power line. An evaluation of the proposed method over a complicated railway point clouds acquired by a RIEGL VMX450 MLS system shows that the proposed method is promising.