TY - GEN
T1 - Contextual unmixing of geospatial data based on Bayesian modeling
AU - Nishii, Ryuei
AU - Qin, Pan
AU - Uchi, Daisuke
N1 - Publisher Copyright:
© 2014 IEEE.
Copyright:
Copyright 2015 Elsevier B.V., All rights reserved.
PY - 2014/11/4
Y1 - 2014/11/4
N2 - Image classification has a long history for estimating landcover categories by feature vectors, and various methods have been proposed from many viewpoints; statistics, machine learning and others. Multivariate normal distributions are frequently used to model feature distributions. Also, it is known that contextual classification methods based on Markov random fields (MRF) improve non-contextual classifiers successfully.
AB - Image classification has a long history for estimating landcover categories by feature vectors, and various methods have been proposed from many viewpoints; statistics, machine learning and others. Multivariate normal distributions are frequently used to model feature distributions. Also, it is known that contextual classification methods based on Markov random fields (MRF) improve non-contextual classifiers successfully.
UR - http://www.scopus.com/inward/record.url?scp=84911454120&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84911454120&partnerID=8YFLogxK
U2 - 10.1109/IGARSS.2014.6946760
DO - 10.1109/IGARSS.2014.6946760
M3 - Conference contribution
AN - SCOPUS:84911454120
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 1631
EP - 1634
BT - International Geoscience and Remote Sensing Symposium (IGARSS)
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - Joint 2014 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2014 and the 35th Canadian Symposium on Remote Sensing, CSRS 2014
Y2 - 13 July 2014 through 18 July 2014
ER -