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@ARTICLE{art:global:local:MLRsub,
author={M. Khodadadzadeh and J. Li and A. Plaza and H. Ghassemian and J. M. Bioucas-Dias and X. Li},
journal={IEEE Transactions on Geoscience and Remote Sensing},
title={Spectral #x2013;Spatial Classification of Hyperspectral Data Using Local and Global Probabilities for Mixed Pixel Characterization},
year={2014},
volume={52},
number={10},
pages={6298-6314},
keywords={Markov processes;geophysical image processing;hyperspectral imaging;image classification;image representation;probability;random processes;regression analysis;support vector machines;MRF regularizer;Markov random field regularizer;global probability;hyperspectral data classification;local probability;mixed pixel characterization;pixel-based probabilistic support vector machine classifier;posterior probabilistic classification;remotely sensed hyperspectral image classification;spatial information;spectral information;spectral-spatial classification;subspace-based multinomial logistic regression method;Hyperspectral imaging;Probabilistic logic;Probability distribution;Support vector machines;Training;Vectors;Hyperspectral imaging;Markov random field (MRF);multiple classifiers;spectral¨Cspatial classification;spectral??spatial classification;subspace multinomial logistic regression (MLRsub);support vector machine (SVM)},
doi={10.1109/TGRS.2013.2296031},
ISSN={0196-2892},
month={Oct},}



@ARTICLE{art:MLRsub,
author={J. Li and J. M. Bioucas-Dias and A. Plaza},
journal={IEEE Transactions on Geoscience and Remote Sensing},
title={Spectral #x2013;Spatial Hyperspectral Image Segmentation Using Subspace Multinomial Logistic Regression and Markov Random Fields},
year={2012},
volume={50},
number={3},
pages={809-823},
keywords={Bayes methods;Markov processes;geophysical image processing;geophysical techniques;image classification;image segmentation;optimisation;random processes;regression analysis;remote sensing;Bayesian framework;MLR algorithm;alpha-expansion mincut-based integer optimization algorithm;hyperspectral image classification method;multilevel logistic Markov-Gibbs Markov random field;posterior probability distribution;posteriori segmentation method;real hyperspectral data set;remotely sensed hyperspectral image data;spatial domain;spatial information;spatial-contextual information analysis;spectral domain;spectral information;spectral-spatial hyperspectral image segmentation;subspace multinomial logistic regression algorithm;subspace projection method;Hyperspectral imaging;Image segmentation;Labeling;Logistics;Optimization;Training;Hyperspectral image segmentation;Markov random field (MRF);multinomial logistic regression (MLR);subspace projection method},
doi={10.1109/TGRS.2011.2162649},
ISSN={0196-2892},
month={March},}
