Details
| Originalsprache | Englisch |
|---|---|
| Aufsatznummer | 6739122 |
| Seiten (von - bis) | 1614-1618 |
| Seitenumfang | 5 |
| Fachzeitschrift | IEEE Geoscience and Remote Sensing Letters |
| Jahrgang | 11 |
| Ausgabenummer | 9 |
| Publikationsstatus | Veröffentlicht - Sept. 2014 |
Abstract
The classification of airborne lidar data is a relevant task in different disciplines. The information about the geometry and the full waveform can be used in order to classify the 3-D point cloud. In Wadden Sea areas, the classification of lidar data is of main interest for the scientific monitoring of coastal morphology and habitats, but it becomes a challenging task due to flat areas with hardly any discriminative objects. For the classification, we combine a conditional random field framework with a random forest approach. By classifying in this way, we benefit from the consideration of context on the one hand and from the opportunity to utilize a high number of classification features on the other hand. We investigate the relevance of different features for the lidar points in coastal areas as well as for the interaction of neighboring points.
ASJC Scopus Sachgebiete
- Erdkunde und Planetologie (insg.)
- Geotechnik und Ingenieurgeologie
- Ingenieurwesen (insg.)
- Elektrotechnik und Elektronik
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in: IEEE Geoscience and Remote Sensing Letters, Jahrgang 11, Nr. 9, 6739122, 09.2014, S. 1614-1618.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - Contextual classification of full waveform lidar data in the wadden sea
AU - Schmidt, Alena
AU - Niemeyer, Joachim
AU - Rottensteiner, Franz
AU - Soergel, Uwe
PY - 2014/9
Y1 - 2014/9
N2 - The classification of airborne lidar data is a relevant task in different disciplines. The information about the geometry and the full waveform can be used in order to classify the 3-D point cloud. In Wadden Sea areas, the classification of lidar data is of main interest for the scientific monitoring of coastal morphology and habitats, but it becomes a challenging task due to flat areas with hardly any discriminative objects. For the classification, we combine a conditional random field framework with a random forest approach. By classifying in this way, we benefit from the consideration of context on the one hand and from the opportunity to utilize a high number of classification features on the other hand. We investigate the relevance of different features for the lidar points in coastal areas as well as for the interaction of neighboring points.
AB - The classification of airborne lidar data is a relevant task in different disciplines. The information about the geometry and the full waveform can be used in order to classify the 3-D point cloud. In Wadden Sea areas, the classification of lidar data is of main interest for the scientific monitoring of coastal morphology and habitats, but it becomes a challenging task due to flat areas with hardly any discriminative objects. For the classification, we combine a conditional random field framework with a random forest approach. By classifying in this way, we benefit from the consideration of context on the one hand and from the opportunity to utilize a high number of classification features on the other hand. We investigate the relevance of different features for the lidar points in coastal areas as well as for the interaction of neighboring points.
KW - Classification
KW - coast
KW - conditional random fields (CRFs)
KW - lidar
KW - random forests (RFs)
UR - http://www.scopus.com/inward/record.url?scp=84899971791&partnerID=8YFLogxK
U2 - 10.1109/LGRS.2014.2302317
DO - 10.1109/LGRS.2014.2302317
M3 - Article
AN - SCOPUS:84899971791
VL - 11
SP - 1614
EP - 1618
JO - IEEE Geoscience and Remote Sensing Letters
JF - IEEE Geoscience and Remote Sensing Letters
SN - 1545-598X
IS - 9
M1 - 6739122
ER -