Details
| Originalsprache | Englisch |
|---|---|
| Seiten | 5402-5405 |
| Seitenumfang | 4 |
| Publikationsstatus | Veröffentlicht - 2012 |
| Veranstaltung | 2012 32nd IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2012 - Munich, Deutschland Dauer: 22 Juli 2012 → 27 Juli 2012 |
Konferenz
| Konferenz | 2012 32nd IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2012 |
|---|---|
| Land/Gebiet | Deutschland |
| Ort | Munich |
| Zeitraum | 22 Juli 2012 → 27 Juli 2012 |
Abstract
This paper gives an overview about advanced techniques for classification and object detection that are being adopted for urban object detection from LiDAR data. The paper covers local supervised classifiers such as AdaBoost, SVM and Random Forests, statistical models of context such as Markov Random Fields and Conditional Random Fields, and sampling techniques. The relevance of features is also discussed. Applications include DTM generation and the extraction of buildings, trees, and low vegetation.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Angewandte Informatik
- Erdkunde und Planetologie (insg.)
- Allgemeine Erdkunde und Planetologie
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2012. 5402-5405 Beitrag in 2012 32nd IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2012, Munich, Deutschland.
Publikation: Konferenzbeitrag › Paper › Forschung › Peer-Review
}
TY - CONF
T1 - Advanced methods for automated object extraction from LiDAR in urban areas
AU - Rottensteiner, Franz
PY - 2012
Y1 - 2012
N2 - This paper gives an overview about advanced techniques for classification and object detection that are being adopted for urban object detection from LiDAR data. The paper covers local supervised classifiers such as AdaBoost, SVM and Random Forests, statistical models of context such as Markov Random Fields and Conditional Random Fields, and sampling techniques. The relevance of features is also discussed. Applications include DTM generation and the extraction of buildings, trees, and low vegetation.
AB - This paper gives an overview about advanced techniques for classification and object detection that are being adopted for urban object detection from LiDAR data. The paper covers local supervised classifiers such as AdaBoost, SVM and Random Forests, statistical models of context such as Markov Random Fields and Conditional Random Fields, and sampling techniques. The relevance of features is also discussed. Applications include DTM generation and the extraction of buildings, trees, and low vegetation.
KW - LiDAR
KW - Object detection
KW - Urban areas
UR - http://www.scopus.com/inward/record.url?scp=84873157177&partnerID=8YFLogxK
U2 - 10.1109/IGARSS.2012.6352385
DO - 10.1109/IGARSS.2012.6352385
M3 - Paper
AN - SCOPUS:84873157177
SP - 5402
EP - 5405
T2 - 2012 32nd IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2012
Y2 - 22 July 2012 through 27 July 2012
ER -