Band selection based on feature weighting for classification of hyperspectral data

@article{Huang2005BandSB,
  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},
  year={2005},
  volume={2},
  pages={156-159}
}
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
Highly Cited
This paper has 113 citations. REVIEW CITATIONS
83 Citations
14 References
Similar Papers

Citations

Publications citing this paper.
Showing 1-10 of 83 extracted citations

114 Citations

051015'08'11'14'17
Citations per Year
Semantic Scholar estimates that this publication has 114 citations based on the available data.

See our FAQ for additional information.

References

Publications referenced by this paper.
Showing 1-10 of 14 references

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
1 Excerpt

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

Similar Papers

Loading similar papers…