Title :
Nonlinear Bayesian unmixing of geospatial data based on GIBBS sampling
Author :
Ryuei Nishii ; Pan Qin ; Uchi, Daisuke
Author_Institution :
Inst. of Math. for Ind., Kyushu Univ., Fukuoka, Japan
Abstract :
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.
Keywords :
geophysical image processing; geophysical techniques; image classification; land cover; Gibbs sampling; Markov random fields; feature vectors; geospatial data; image classification; land cover categories; machine learning; model feature distributions; multivariate normal distributions; noncontextual classifiers; nonlinear Bayesian unmixing; Equations;
Conference_Titel :
Geoscience and Remote Sensing Symposium (IGARSS), 2013 IEEE International
Conference_Location :
Melbourne, VIC
Print_ISBN :
978-1-4799-1114-1
DOI :
10.1109/IGARSS.2013.6723483
