Comparative study on morphological principal component analysis of hyperspectral images

@article{Franchi2014ComparativeSO,
  title={Comparative study on morphological principal component analysis of hyperspectral images},
  author={Gianni Franchi and Jes{\'u}s Angulo},
  journal={2014 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)},
  year={2014},
  pages={1-4}
}
This paper deals with a problem of dimensionality reduction for hyperspectral images using principal component analysis. Hyperspectral image reduction is improved by adding structural/spatial information to the spectral information, by means of mathematical morphology tools. It can be then useful for instance in supervised classification. The key element of the approach is the computation of a covariance matrix which integrates simultaneously both spatial and spectral information. 

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