Classification of Hyperspectral Data Based on Semi-Supervised Tri-Training Learning Framework

Abstract:

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A semi-supervised learning framework based on the tri-training scheme is proposed for the classification of hyper spectral data. The framework involves two stages: multiple classifier learning by the improved tri-training and integrating the outputs of classifiers to the final hypothesis by decision fusion. To settle the ill-posed classification problem, in the stage of classifier learning, the label confidence of each learner is measured by the improved estimation of classifier error, and self-training is introduced to expand the labeled set using unlabeled samples with confident labels assigned by classifiers. Hyper spectral data classification experiments show the effectiveness of the proposed framework.

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Periodical:

Edited by:

David Wang

Pages:

374-382

DOI:

10.4028/www.scientific.net/KEM.500.374

Citation:

R. Huang and L. N. Zhou, "Classification of Hyperspectral Data Based on Semi-Supervised Tri-Training Learning Framework", Key Engineering Materials, Vol. 500, pp. 374-382, 2012

Online since:

January 2012

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$35.00

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