Details
| Original language | English |
|---|---|
| Pages (from-to) | 354-371 |
| Number of pages | 18 |
| Journal | ISPRS Journal of Photogrammetry and Remote Sensing |
| Volume | 128 |
| Early online date | 26 Apr 2017 |
| Publication status | Published - Jun 2017 |
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
ASJC Scopus subject areas
- Physics and Astronomy(all)
- Atomic and Molecular Physics, and Optics
- Engineering(all)
- Engineering (miscellaneous)
- Computer Science(all)
- Computer Science Applications
- Earth and Planetary Sciences(all)
- Computers in Earth Sciences
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In: ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 128, 06.2017, p. 354-371.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Contextual segment-based classification of airborne laser scanner data
AU - Vosselman, George
AU - Coenen, Maximilian
AU - Rottensteiner, Franz
N1 - Publisher Copyright: © 2017 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS) Copyright: Copyright 2017 Elsevier B.V., All rights reserved.
PY - 2017/6
Y1 - 2017/6
N2 - 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.
AB - 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.
KW - Classification
KW - CRF
KW - Point cloud
KW - Segmentation
UR - http://www.scopus.com/inward/record.url?scp=85018625462&partnerID=8YFLogxK
U2 - 10.1016/j.isprsjprs.2017.03.010
DO - 10.1016/j.isprsjprs.2017.03.010
M3 - Article
AN - SCOPUS:85018625462
VL - 128
SP - 354
EP - 371
JO - ISPRS Journal of Photogrammetry and Remote Sensing
JF - ISPRS Journal of Photogrammetry and Remote Sensing
SN - 0924-2716
ER -