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Sensors 2016, 16(12), 2112; doi:10.3390/s16122112

Application of Template Matching for Improving Classification of Urban Railroad Point Clouds

1
Department of Geomatics Engineering, University of Calgary, 2500 University Drive NW, Calgary, AB T2N 1N4, Canada
2
Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, P.O. Box 217, Enschede 7514 AE, The Netherlands
*
Author to whom correspondence should be addressed.
Academic Editor: Changshan Wu
Received: 24 October 2016 / Revised: 3 December 2016 / Accepted: 8 December 2016 / Published: 12 December 2016

Abstract

This study develops an integrated data-driven and model-driven approach (template matching) that clusters the urban railroad point clouds into three classes of rail track, contact cable, and catenary cable. The employed dataset covers 630 m of the Dutch urban railroad corridors in which there are four rail tracks, two contact cables, and two catenary cables. The dataset includes only geometrical information (three dimensional (3D) coordinates of the points) with no intensity data and no RGB data. The obtained results indicate that all objects of interest are successfully classified at the object level with no false positives and no false negatives. The results also show that an average 97.3% precision and an average 97.7% accuracy at the point cloud level are achieved. The high precision and high accuracy of the rail track classification (both greater than 96%) at the point cloud level stems from the great impact of the employed template matching method on excluding the false positives. The cables also achieve quite high average precision (96.8%) and accuracy (98.4%) due to their high sampling and isolated position in the railroad corridor. View Full-Text
Keywords: LiDAR; point cloud; object recognition; segmentation; rail; cable LiDAR; point cloud; object recognition; segmentation; rail; cable
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Arastounia, M.; Oude Elberink, S. Application of Template Matching for Improving Classification of Urban Railroad Point Clouds. Sensors 2016, 16, 2112.

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