Band selection based on feature weighting for classification of hyperspectral data

  title={Band selection based on feature weighting for classification of hyperspectral data},
  author={Rui Huang and Mingyi He},
  journal={IEEE Geoscience and Remote Sensing Letters},
A new feature weighting method for band selection is presented, which is based on the pairwise separability criterion and matrix coefficients analysis. Through decorrelation of each class by principal component transformation, the criterion value of any band subset is the summations of the values of individual bands of it for the transformed feature space, and thus the computation amounts of calculating criteria of each band combinations are reduced. Following it, the corresponding matrix… CONTINUE READING
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Comparison of band selection results using different class separation measures in various day and night conditions

  • D. Sheffer, Y. Ultchin
  • Proc. SPIE. Conf. Algorithms and Technologies for…
  • 2003

The role of feature selection in artificial neural network applications

  • T. Kavzoglu, P. M. Mather
  • Int. J. Remote Sens., vol. 23, no. 15, pp. 2919…
  • 2002
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Unsupervised hyperspectral image analysis using independent component analysis

  • S. Chiang, C.-I. Chang, I. W. Ginsberg
  • Proc. IGARSS, vol. 4, Honolulu, HI, Jul. 2000, pp…
  • 2000

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