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Remote Sens. 2015, 7(10), 12680-12703; doi:10.3390/rs71012680

Automatic Detection and Classification of Pole-Like Objects in Urban Point Cloud Data Using an Anomaly Detection Algorithm

1
Department of Physics and Mathematics, University of Alcalá, Campus Universitario Ctra., Alcalá de Henares, 28871 Madrid, Spain
2
Department of Mining Exploitation, University of Oviedo, Escuela Politécnica de Mieres, Gonzalo Gutiérrez Quirós, 33600 Mieres, Spain
*
Author to whom correspondence should be addressed.
Academic Editors: Juha Hyyppä, Devrim Akca, Parth Sarathi Roy and Prasad S. Thenkabail
Received: 28 July 2015 / Revised: 4 September 2015 / Accepted: 17 September 2015 / Published: 28 September 2015
(This article belongs to the Special Issue Lidar/Laser Scanning in Urban Environments)
View Full-Text   |   Download PDF [1887 KB, uploaded 28 September 2015]   |  

Abstract

Detecting and modeling urban furniture are of particular interest for urban management and the development of autonomous driving systems. This paper presents a novel method for detecting and classifying vertical urban objects and trees from unstructured three-dimensional mobile laser scanner (MLS) or terrestrial laser scanner (TLS) point cloud data. The method includes an automatic initial segmentation to remove the parts of the original cloud that are not of interest for detecting vertical objects, by means of a geometric index based on features of the point cloud. Vertical object detection is carried out through the Reed and Xiaoli (RX) anomaly detection algorithm applied to a pillar structure in which the point cloud was previously organized. A clustering algorithm is then used to classify the detected vertical elements as man-made poles or trees. The effectiveness of the proposed method was tested in two point clouds from heterogeneous street scenarios and measured by two different sensors. The results for the two test sites achieved detection rates higher than 96%; the classification accuracy was around 95%, and the completion quality of both procedures was 90%. Non-detected poles come from occlusions in the point cloud and low-height traffic signs; most misclassifications occurred in man-made poles adjacent to trees. View Full-Text
Keywords: pole-like objects; feature extraction; pattern recognition; clustering; 3D point cloud; MLS; anomaly detection pole-like objects; feature extraction; pattern recognition; clustering; 3D point cloud; MLS; anomaly detection
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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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MDPI and ACS Style

Rodríguez-Cuenca, B.; García-Cortés, S.; Ordóñez, C.; Alonso, M.C. Automatic Detection and Classification of Pole-Like Objects in Urban Point Cloud Data Using an Anomaly Detection Algorithm. Remote Sens. 2015, 7, 12680-12703.

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