Abstract
In supervised and unsupervised image classification, it is known that contextual classification methods based on Markov random fields (MRFs) improve the performance of non-contextual classifiers. In this paper, we consider the unsupervised unmixing problem based on MRFs. The exact solutions maximizing local conditional densities are derived, and they show excellent performance for unximing of data sets. Furthermore a new stochastic model based on conditional random fields is proposed for unmixing of hyperspectral data. The approximation formula of its normalizing factor is also derived.
| Original language | English |
|---|---|
| Title of host publication | WHISPERS '09 - 1st Workshop on Hyperspectral Image and Signal Processing |
| Subtitle of host publication | Evolution in Remote Sensing |
| DOIs | |
| Publication status | Published - Dec 21 2009 |
| Event | WHISPERS '09 - 1st Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing - Grenoble, France Duration: Aug 26 2009 → Aug 28 2009 |
Publication series
| Name | WHISPERS '09 - 1st Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing |
|---|
Other
| Other | WHISPERS '09 - 1st Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing |
|---|---|
| Country | France |
| City | Grenoble |
| Period | 8/26/09 → 8/28/09 |
Fingerprint
All Science Journal Classification (ASJC) codes
- Computer Networks and Communications
- Computer Vision and Pattern Recognition
- Signal Processing
Cite this
Contextual unmixing of geospatial data based on Markov random fields and conditional random fields. / Nishii, Ryuei; Ozaki, Tomohiko.
WHISPERS '09 - 1st Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing. 2009. 5289004 (WHISPERS '09 - 1st Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Contextual unmixing of geospatial data based on Markov random fields and conditional random fields
AU - Nishii, Ryuei
AU - Ozaki, Tomohiko
PY - 2009/12/21
Y1 - 2009/12/21
N2 - In supervised and unsupervised image classification, it is known that contextual classification methods based on Markov random fields (MRFs) improve the performance of non-contextual classifiers. In this paper, we consider the unsupervised unmixing problem based on MRFs. The exact solutions maximizing local conditional densities are derived, and they show excellent performance for unximing of data sets. Furthermore a new stochastic model based on conditional random fields is proposed for unmixing of hyperspectral data. The approximation formula of its normalizing factor is also derived.
AB - In supervised and unsupervised image classification, it is known that contextual classification methods based on Markov random fields (MRFs) improve the performance of non-contextual classifiers. In this paper, we consider the unsupervised unmixing problem based on MRFs. The exact solutions maximizing local conditional densities are derived, and they show excellent performance for unximing of data sets. Furthermore a new stochastic model based on conditional random fields is proposed for unmixing of hyperspectral data. The approximation formula of its normalizing factor is also derived.
UR - http://www.scopus.com/inward/record.url?scp=72049083494&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=72049083494&partnerID=8YFLogxK
U2 - 10.1109/WHISPERS.2009.5289004
DO - 10.1109/WHISPERS.2009.5289004
M3 - Conference contribution
AN - SCOPUS:72049083494
SN - 9781424446872
T3 - WHISPERS '09 - 1st Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing
BT - WHISPERS '09 - 1st Workshop on Hyperspectral Image and Signal Processing
ER -