31 search results - page 1 / 7 » Low-Complexity Principal Component Analysis for Hyperspectra... |
IJHPCA
2008
13 years 5 months ago
2008
Principal component analysis (PCA) is an effective tool for spectral decorrelation of hyperspectral imagery, and PCA-based spectral transforms have been employed successfully in co...
CORR
2012
Springer
12 years 28 days ago
2012
Springer
We consider the problem of recovering a target matrix that is a superposition of low-rank and sparse components, from a small set of linear measurements. This problem arises in co...
DCC
2008
IEEE
14 years 4 months ago
2008
IEEE
A method is proposed for the compression of hyperspectral signature vectors on severely resourceconstrained encoding platforms. The proposed technique, compressive-projection prin...
ICIP
2006
IEEE
14 years 6 months ago
2006
IEEE
We investigate reducing the dimensionality of image sets by using principal component analysis on wavelet coefficients to maximize edge energy in the reduced dimension images. Lar...
AIPR
2003
IEEE
13 years 10 months ago
2003
IEEE
Although hyperspectral images provide abundant information about objects, their high dimensionality also substantially increases computational burden. Dimensionality reduction off...