Title | Bayesian classification of hyperspectral imagery based on probabilistic sparse representation and Markov random field |
Publication Type | Journal Article |
Year of Publication | 2014 |
Authors | Xu, L., and J. Li |
Journal | IEEE Geoscience and Remote Sensing Letters |
Volume | 11 |
Start Page | 823 |
Issue | 4 |
Pagination | 823 - 827 |
Keywords | Bayes classifier, graph cut, hyperspectral image classification, Markov random field (MRF), probabilistic sparse representation (PSR) |
Abstract | 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. |
Bayesian classification of hyperspectral imagery based on probabilistic sparse representation and Markov random field
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