TY - GEN
T1 - Comparative study on morphological principal component analysis of hyperspectral images
AU - Franchi, Gianni
AU - Angulo, Jesús
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/6/28
Y1 - 2014/6/28
N2 - 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.
AB - 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.
KW - Dimensionality reduction
KW - Hyperspectral images
KW - Mathematical Morphology
U2 - 10.1109/WHISPERS.2014.8077568
DO - 10.1109/WHISPERS.2014.8077568
M3 - Conference contribution
AN - SCOPUS:85008915825
T3 - Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing
BT - 2014 6th Workshop on Hyperspectral Image and Signal Processing
PB - IEEE Computer Society
T2 - 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2014
Y2 - 24 June 2014 through 27 June 2014
ER -