Details
| Original language | English |
|---|---|
| Pages (from-to) | 263-268 |
| Number of pages | 6 |
| Journal | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Volume | 1 |
| Publication status | Published - 13 Jul 2012 |
| Event | 22nd Congress of the International Society for Photogrammetry and Remote Sensing: Imaging a Sustainable Future, ISPRS 2012 - Melbourne, Australia Duration: 25 Aug 2012 → 1 Sept 2012 |
Abstract
In this paper, we investigate the potential of a Conditional Random Field (CRF) approach for the classification of an airborne LiDAR (Light Detection And Ranging) point cloud. This method enables the incorporation of contextual information and learning of specific relations of object classes within a training step. Thus, it is a powerful approach for obtaining reliable results even in complex urban scenes. Geometrical features as well as an intensity value are used to distinguish the five object classes building, low vegetation, tree, natural ground, and asphalt ground. The performance of our method is evaluated on the dataset of Vaihingen, Germany, in the context of the 'ISPRS Test Project on Urban Classification and 3D Building Reconstruction'. Therefore, the results of the 3D classification were submitted as a 2D binary label image for a subset of two classes, namely building and tree.
Keywords
- Classification, Conditional Random Fields, LiDAR, Point Cloud, Urban
ASJC Scopus subject areas
- Earth and Planetary Sciences(all)
- Earth and Planetary Sciences (miscellaneous)
- Environmental Science(all)
- Environmental Science (miscellaneous)
- Physics and Astronomy(all)
- Instrumentation
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In: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. 1, 13.07.2012, p. 263-268.
Research output: Contribution to journal › Conference article › Research › peer review
}
TY - JOUR
T1 - CONDITIONAL RANDOM FIELDS for LIDAR POINT CLOUD CLASSIFICATION in COMPLEX URBAN AREAS
AU - Niemeyer, J.
AU - Rottensteiner, F.
AU - Soergel, U.
PY - 2012/7/13
Y1 - 2012/7/13
N2 - In this paper, we investigate the potential of a Conditional Random Field (CRF) approach for the classification of an airborne LiDAR (Light Detection And Ranging) point cloud. This method enables the incorporation of contextual information and learning of specific relations of object classes within a training step. Thus, it is a powerful approach for obtaining reliable results even in complex urban scenes. Geometrical features as well as an intensity value are used to distinguish the five object classes building, low vegetation, tree, natural ground, and asphalt ground. The performance of our method is evaluated on the dataset of Vaihingen, Germany, in the context of the 'ISPRS Test Project on Urban Classification and 3D Building Reconstruction'. Therefore, the results of the 3D classification were submitted as a 2D binary label image for a subset of two classes, namely building and tree.
AB - In this paper, we investigate the potential of a Conditional Random Field (CRF) approach for the classification of an airborne LiDAR (Light Detection And Ranging) point cloud. This method enables the incorporation of contextual information and learning of specific relations of object classes within a training step. Thus, it is a powerful approach for obtaining reliable results even in complex urban scenes. Geometrical features as well as an intensity value are used to distinguish the five object classes building, low vegetation, tree, natural ground, and asphalt ground. The performance of our method is evaluated on the dataset of Vaihingen, Germany, in the context of the 'ISPRS Test Project on Urban Classification and 3D Building Reconstruction'. Therefore, the results of the 3D classification were submitted as a 2D binary label image for a subset of two classes, namely building and tree.
KW - Classification
KW - Conditional Random Fields
KW - LiDAR
KW - Point Cloud
KW - Urban
UR - http://www.scopus.com/inward/record.url?scp=85041430565&partnerID=8YFLogxK
U2 - 10.5194/isprsannals-I-3-263-2012
DO - 10.5194/isprsannals-I-3-263-2012
M3 - Conference article
AN - SCOPUS:85041430565
VL - 1
SP - 263
EP - 268
JO - ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
JF - ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
SN - 2194-9042
T2 - 22nd Congress of the International Society for Photogrammetry and Remote Sensing: Imaging a Sustainable Future, ISPRS 2012
Y2 - 25 August 2012 through 1 September 2012
ER -