(Semi-) Supervised Probabilistic Principal Component Analysis for Hyperspectral Remote Sensing Image Classification

Abstract : In this paper, we have applied supervised probabilistic principal component analysis (SPPCA) and semi-supervised probabilistic principal component analysis (S2PPCA) for feature extraction in hyperspectral remote sensing imagery. The two models are all based on probabilistic principal component analysis (PPCA) using EM learning algorithm. SPPCA only relies on the labeled samples into the projection phase, while S2PPCA is able to incorporate both the labeled and unlabeled information. Experimental results on three real hyperspectral images demonstrate the SPPCA and S2PPCA outperform some conventional feature extraction methods for classifying hyperspectral remote sensing image with low computational complexity.
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Article dans une revue
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, IEEE, 2014, 7 (6), pp.2224-2236. 〈10.1109/JSTARS.2013.2279693〉
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http://hal.univ-grenoble-alpes.fr/hal-01128050
Contributeur : Vincent Couturier-Doux <>
Soumis le : lundi 9 mars 2015 - 11:02:10
Dernière modification le : lundi 9 avril 2018 - 12:22:34

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Junshi Xia, Jocelyn Chanussot, Peijun Du, Xivan He. (Semi-) Supervised Probabilistic Principal Component Analysis for Hyperspectral Remote Sensing Image Classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, IEEE, 2014, 7 (6), pp.2224-2236. 〈10.1109/JSTARS.2013.2279693〉. 〈hal-01128050〉

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