Contextual segment-based classification of airborne laser scanner data

authored by: George Vosselman, Maximilian Coenen, Franz Rottensteiner
Abstract: Classification of point clouds is needed as a first step in the extraction of various types of geo-information from point clouds. We present a new approach to contextual classification of segmented airborne laser scanning data. Potential advantages of segment-based classification are easily offset by segmentation errors. We combine different point cloud segmentation methods to minimise both under- and over-segmentation. We propose a contextual segment-based classification using a Conditional Random Field. Segment adjacencies are represented by edges in the graphical model and characterised by a range of features of points along the segment borders. A mix of small and large segments allows the interaction between nearby and distant points. Results of the segment-based classification are compared to results of a point-based CRF classification. Whereas only a small advantage of the segment-based classification is observed for the ISPRS Vaihingen dataset with 4–7 points/m², the percentage of correctly classified points in a 30 points/m² dataset of Rotterdam amounts to 91.0% for the segment-based classification vs. 82.8% for the point-based classification.
Organisation(s): Institute of Photogrammetry and GeoInformation (IPI)
External Organisation(s): International Institute for Geo-Information Science and Earth Observation - ITC
Type: Article
Journal: ISPRS Journal of Photogrammetry and Remote Sensing
Volume: 128
Pages: 354-371
No. of pages: 18
ISSN: 0924-2716
Publication date: 06.2017
Publication status: Published
Peer reviewed: Yes
ASJC Scopus subject areas: Atomic and Molecular Physics, and Optics, Engineering (miscellaneous), Computer Science Applications, Computers in Earth Sciences
Electronic version(s): https://doi.org/10.1016/j.isprsjprs.2017.03.010 (Access: Closed)
https://research.utwente.nl/en/publications/a7a03fd9-2c42-485c-bb64-3726a55879de (Access: Open)

BibTeX

@article{d5d54178eeca43d69d4ed9e7e723dba3,
title = "Contextual segment-based classification of airborne laser scanner data",
abstract = "Classification of point clouds is needed as a first step in the extraction of various types of geo-information from point clouds. We present a new approach to contextual classification of segmented airborne laser scanning data. Potential advantages of segment-based classification are easily offset by segmentation errors. We combine different point cloud segmentation methods to minimise both under- and over-segmentation. We propose a contextual segment-based classification using a Conditional Random Field. Segment adjacencies are represented by edges in the graphical model and characterised by a range of features of points along the segment borders. A mix of small and large segments allows the interaction between nearby and distant points. Results of the segment-based classification are compared to results of a point-based CRF classification. Whereas only a small advantage of the segment-based classification is observed for the ISPRS Vaihingen dataset with 4–7 points/m2, the percentage of correctly classified points in a 30 points/m2 dataset of Rotterdam amounts to 91.0% for the segment-based classification vs. 82.8% for the point-based classification.",
keywords = "Classification, CRF, Point cloud, Segmentation",
author = "George Vosselman and Maximilian Coenen and Franz Rottensteiner",
note = "Publisher Copyright: {\textcopyright} 2017 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS) Copyright: Copyright 2017 Elsevier B.V., All rights reserved.",
year = "2017",
month = jun,
doi = "10.1016/j.isprsjprs.2017.03.010",
language = "English",
volume = "128",
pages = "354--371",
journal = "ISPRS Journal of Photogrammetry and Remote Sensing",
issn = "0924-2716",
publisher = "Elsevier",
}