Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | 2017 IEEE International Geoscience and Remote Sensing Symposium |
| Untertitel | International Cooperation for Global Awareness, IGARSS 2017 - Proceedings |
| Herausgeber (Verlag) | Institute of Electrical and Electronics Engineers Inc. |
| Seiten | 3007-3010 |
| Seitenumfang | 4 |
| ISBN (elektronisch) | 9781509049516 |
| Publikationsstatus | Elektronisch veröffentlicht (E-Pub) - 4 Dez. 2017 |
| Veranstaltung | 37th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2017 - Fort Worth, USA / Vereinigte Staaten Dauer: 23 Juli 2017 → 28 Juli 2017 |
Abstract
This work presents a spatio-temporal Conditional Random Field (CRF) based model for crop recognition from multi-temporal remote sensing image sequences. The association potential at each image site is based on the class posterior probabilities computed by a Random Forest (RF) classifier given the features at the corresponding site. A contrast-sensitive Potts model is used as a label smoothing method in the spatial domain, whereas the interactions in the temporal domain are modeled based on expert knowledge about the possible transitions between adjacent epochs. The CRF based model was tested for crop mapping in two subtropical areas based on a sequences of 9 Landsat and 14 Sentinel-1 images from Ipuã, São Paulo and Campo Verde, Mato Grosso, respectively, two municipalities in Brazil. The experiments showed significant improvements of the accumulated F1 score per class against a mono-temporal CRF approach of up to 50% and 75% for a total of 8 and 11 classes using Optical and SAR images respectively.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Angewandte Informatik
- Erdkunde und Planetologie (insg.)
- Allgemeine Erdkunde und Planetologie
Zitieren
- Standard
- Harvard
- Apa
- Vancouver
- BibTex
- RIS
2017 IEEE International Geoscience and Remote Sensing Symposium: International Cooperation for Global Awareness, IGARSS 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017. S. 3007-3010 8127631.
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Spatial-temporal conditional random field based model for crop recognition in tropical regions
AU - Achanccaray, P.
AU - Feitosa, R. Q.
AU - Rottensteiner, F.
AU - Sanches, I. D.
AU - Heipke, C.
N1 - Publisher Copyright: © 2017 IEEE. Copyright: Copyright 2018 Elsevier B.V., All rights reserved.
PY - 2017/12/4
Y1 - 2017/12/4
N2 - This work presents a spatio-temporal Conditional Random Field (CRF) based model for crop recognition from multi-temporal remote sensing image sequences. The association potential at each image site is based on the class posterior probabilities computed by a Random Forest (RF) classifier given the features at the corresponding site. A contrast-sensitive Potts model is used as a label smoothing method in the spatial domain, whereas the interactions in the temporal domain are modeled based on expert knowledge about the possible transitions between adjacent epochs. The CRF based model was tested for crop mapping in two subtropical areas based on a sequences of 9 Landsat and 14 Sentinel-1 images from Ipuã, São Paulo and Campo Verde, Mato Grosso, respectively, two municipalities in Brazil. The experiments showed significant improvements of the accumulated F1 score per class against a mono-temporal CRF approach of up to 50% and 75% for a total of 8 and 11 classes using Optical and SAR images respectively.
AB - This work presents a spatio-temporal Conditional Random Field (CRF) based model for crop recognition from multi-temporal remote sensing image sequences. The association potential at each image site is based on the class posterior probabilities computed by a Random Forest (RF) classifier given the features at the corresponding site. A contrast-sensitive Potts model is used as a label smoothing method in the spatial domain, whereas the interactions in the temporal domain are modeled based on expert knowledge about the possible transitions between adjacent epochs. The CRF based model was tested for crop mapping in two subtropical areas based on a sequences of 9 Landsat and 14 Sentinel-1 images from Ipuã, São Paulo and Campo Verde, Mato Grosso, respectively, two municipalities in Brazil. The experiments showed significant improvements of the accumulated F1 score per class against a mono-temporal CRF approach of up to 50% and 75% for a total of 8 and 11 classes using Optical and SAR images respectively.
KW - crop recognition
KW - Landsat images
KW - probabilistic graphical models
KW - remote sensing
KW - Sentinel-1
UR - http://www.scopus.com/inward/record.url?scp=85041841951&partnerID=8YFLogxK
U2 - 10.1109/IGARSS.2017.8127631
DO - 10.1109/IGARSS.2017.8127631
M3 - Conference contribution
AN - SCOPUS:85041841951
SP - 3007
EP - 3010
BT - 2017 IEEE International Geoscience and Remote Sensing Symposium
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 37th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2017
Y2 - 23 July 2017 through 28 July 2017
ER -