Multiple Feature Learning for Hyperspectral Image Classification

@article{Li2015MultipleFL,
  title={Multiple Feature Learning for Hyperspectral Image Classification},
  author={Jun Li and Xin Huang and Paolo Gamba and Josx00E9 M. Bioucas-Dias and Liangpei Zhang and Jon Atli Benediktsson and Antonio J. Plaza},
  journal={IEEE Transactions on Geoscience and Remote Sensing},
  year={2015},
  volume={53},
  pages={1592-1606}
}
Hyperspectral image classification has been an active topic of research in recent years. In the past, many different types of features have been extracted (using both linear and nonlinear strategies) for classification problems. On the one hand, some approaches have exploited the original spectral information or other features linearly derived from such information in order to have classes which are linearly separable. On the other hand, other techniques have exploited features obtained through… CONTINUE READING
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Multinomial logistic regression algorithm

  • D. Böhning
  • Ann. Inst. Statist. Math., vol. 44, no. 1, pp…
  • 1992
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