Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | 2011 IEEE International Conference on Computer Vision Workshops, ICCV Workshops 2011 |
| Seiten | 235-242 |
| Seitenumfang | 8 |
| Publikationsstatus | Veröffentlicht - 2011 |
| Veranstaltung | 2011 IEEE International Conference on Computer Vision Workshops, ICCV Workshops 2011 - Barcelona, Spanien Dauer: 6 Nov. 2011 → 13 Nov. 2011 |
Publikationsreihe
| Name | Proceedings of the IEEE International Conference on Computer Vision |
|---|
Abstract
The increasing availability of multitemporal optical remote sensing data offers new potentials for land cover analysis. We present a novel approach for enhancing the classification accuracy of medium resolution data by combining them with high resolution data of an earlier acquisition time, thus saving data acquisition costs. Our approach uses Conditional Random Fields to model both spatial and temporal dependencies. Temporal context is considered by a novel extension of the CRF concept by an additional temporal interaction potential, which can model dependencies between identical regions in images of different acquisition times and scales. The model also considers different levels of abstraction in the class structures at different scales. The approach is tested with two set-ups of Ikonos, RapidEye, and Landsat imagery.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Software
- Informatik (insg.)
- Maschinelles Sehen und Mustererkennung
Zitieren
- Standard
- Harvard
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- Vancouver
- BibTex
- RIS
2011 IEEE International Conference on Computer Vision Workshops, ICCV Workshops 2011. 2011. S. 235-242 6130248 (Proceedings of the IEEE International Conference on Computer Vision).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Classification of multitemporal remote sensing data of different resolution using Conditional Random Fields
AU - Hoberg, Thorsten
AU - Rottensteiner, Franz
AU - Heipke, Christian
PY - 2011
Y1 - 2011
N2 - The increasing availability of multitemporal optical remote sensing data offers new potentials for land cover analysis. We present a novel approach for enhancing the classification accuracy of medium resolution data by combining them with high resolution data of an earlier acquisition time, thus saving data acquisition costs. Our approach uses Conditional Random Fields to model both spatial and temporal dependencies. Temporal context is considered by a novel extension of the CRF concept by an additional temporal interaction potential, which can model dependencies between identical regions in images of different acquisition times and scales. The model also considers different levels of abstraction in the class structures at different scales. The approach is tested with two set-ups of Ikonos, RapidEye, and Landsat imagery.
AB - The increasing availability of multitemporal optical remote sensing data offers new potentials for land cover analysis. We present a novel approach for enhancing the classification accuracy of medium resolution data by combining them with high resolution data of an earlier acquisition time, thus saving data acquisition costs. Our approach uses Conditional Random Fields to model both spatial and temporal dependencies. Temporal context is considered by a novel extension of the CRF concept by an additional temporal interaction potential, which can model dependencies between identical regions in images of different acquisition times and scales. The model also considers different levels of abstraction in the class structures at different scales. The approach is tested with two set-ups of Ikonos, RapidEye, and Landsat imagery.
UR - http://www.scopus.com/inward/record.url?scp=84856646912&partnerID=8YFLogxK
U2 - 10.1109/ICCVW.2011.6130248
DO - 10.1109/ICCVW.2011.6130248
M3 - Conference contribution
AN - SCOPUS:84856646912
SN - 9781467300629
T3 - Proceedings of the IEEE International Conference on Computer Vision
SP - 235
EP - 242
BT - 2011 IEEE International Conference on Computer Vision Workshops, ICCV Workshops 2011
T2 - 2011 IEEE International Conference on Computer Vision Workshops, ICCV Workshops 2011
Y2 - 6 November 2011 through 13 November 2011
ER -