Paper Title:
Classification of Hyperspectral Data Based on Semi-Supervised Tri-Training Learning Framework
  Abstract

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.

  Info
Periodical
Chapter
Chapter 2: Microwaves Optics and Image
Edited by
David Wang
Pages
374-382
DOI
10.4028/www.scientific.net/KEM.500.374
Citation
R. Huang, 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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$32.00
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