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
Y1 - 2009
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 - https://www.scopus.com/pages/publications/72049083494
UR - https://www.scopus.com/pages/publications/72049083494#tab=citedBy
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
T2 - WHISPERS '09 - 1st Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing
Y2 - 26 August 2009 through 28 August 2009
ER -