Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Joint Urban Remote Sensing Event 2013, JURSE 2013 |
| Herausgeber (Verlag) | IEEE Computer Society |
| Seiten | 139-142 |
| Seitenumfang | 4 |
| ISBN (Print) | 9781479902132 |
| Publikationsstatus | Veröffentlicht - 2013 |
| Veranstaltung | 2013 Joint Urban Remote Sensing Event, JURSE 2013 - Sao Paulo, Brasilien Dauer: 21 Apr. 2013 → 23 Apr. 2013 |
Publikationsreihe
| Name | Joint Urban Remote Sensing Event 2013, JURSE 2013 |
|---|
Abstract
In this work we address the task of contextual classification of an airborne LiDAR point cloud. For that purpose, we integrate a Random Forest classifier into a Conditional Random Field (CRF) framework. A CRF has been shown to deliver good results discerning multiple classes. It is a flexible approach for obtaining a reliable classification even in complex urban scenes. The incorporation of multi-scale features improves the results further. Based on the classification results, 2D building and tree objects are generated and evaluated by the benchmark of ISPRS WG III/4.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Computernetzwerke und -kommunikation
Zitieren
- Standard
- Harvard
- Apa
- Vancouver
- BibTex
- RIS
Joint Urban Remote Sensing Event 2013, JURSE 2013. IEEE Computer Society, 2013. S. 139-142 6550685 (Joint Urban Remote Sensing Event 2013, JURSE 2013).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Classification of urban LiDAR data using conditional random field and random forests
AU - Niemeyer, Joachim
AU - Rottensteiner, Franz
AU - Soergel, Uwe
PY - 2013
Y1 - 2013
N2 - In this work we address the task of contextual classification of an airborne LiDAR point cloud. For that purpose, we integrate a Random Forest classifier into a Conditional Random Field (CRF) framework. A CRF has been shown to deliver good results discerning multiple classes. It is a flexible approach for obtaining a reliable classification even in complex urban scenes. The incorporation of multi-scale features improves the results further. Based on the classification results, 2D building and tree objects are generated and evaluated by the benchmark of ISPRS WG III/4.
AB - In this work we address the task of contextual classification of an airborne LiDAR point cloud. For that purpose, we integrate a Random Forest classifier into a Conditional Random Field (CRF) framework. A CRF has been shown to deliver good results discerning multiple classes. It is a flexible approach for obtaining a reliable classification even in complex urban scenes. The incorporation of multi-scale features improves the results further. Based on the classification results, 2D building and tree objects are generated and evaluated by the benchmark of ISPRS WG III/4.
UR - http://www.scopus.com/inward/record.url?scp=84881358566&partnerID=8YFLogxK
U2 - 10.1109/JURSE.2013.6550685
DO - 10.1109/JURSE.2013.6550685
M3 - Conference contribution
AN - SCOPUS:84881358566
SN - 9781479902132
T3 - Joint Urban Remote Sensing Event 2013, JURSE 2013
SP - 139
EP - 142
BT - Joint Urban Remote Sensing Event 2013, JURSE 2013
PB - IEEE Computer Society
T2 - 2013 Joint Urban Remote Sensing Event, JURSE 2013
Y2 - 21 April 2013 through 23 April 2013
ER -