@article {77,
	title = {Bayesian classification of hyperspectral imagery based on probabilistic sparse representation and Markov random field},
	journal = {IEEE Geoscience and Remote Sensing Letters},
	volume = {11},
	year = {2014},
	pages = {823 - 827},
	chapter = {823},
	abstract = {<p>This letter presents a Bayesian method for hyperspectral image classification based on the sparse representation (SR) of spectral information and the Markov random field modeling of spatial information. We introduce a probabilistic SR approach to estimate the class conditional distribution, which proved to be a powerful feature extraction technique to be combined with the label prior distribution in a Bayesian framework. The resulting maximum a priori problem is estimated by a graph-cut-based α-expansion technique. The capabilities of the proposed method are proven in several benchmark hyperspectral images of both agricultural and urban areas.</p>
},
	keywords = {Bayes classifier, graph cut, hyperspectral image classification, Markov random field (MRF), probabilistic sparse representation (PSR)},
	author = {L Xu and J Li}
}
