Automatic detection and classification of pole-like objects in urban point cloud data using an anomaly detection algorithm
Autores
Rodríguez Cuenca, Borja; García Cortés, Silverio; Ordóñez, Celestino; Alonso Rodríguez, María ConcepciónIdentificadores
Enlace permanente (URI): http://hdl.handle.net/10017/32261DOI: 10.3390/rs71012680
ISSN: 2072-4292
Fecha de publicación
2015-07-28Cita bibliográfica
Remote sensing, 2015, v. 7, n. 10, p. 12680-12703
Palabras clave
Pole-like objects
Feature extraction
Pattern recognition
Clustering
3D point cloud
MLS
Anomaly detection
Tipo de documento
info:eu-repo/semantics/article
Versión
info:eu-repo/semantics/publishedVersion
Derechos
Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
Derechos de acceso
info:eu-repo/semantics/openAccess
Resumen
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.
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Colecciones
- MATEMATIC - Artículos [172]