Decompositions using maximum signal factors

Maximum autocorrelation factors (MAF) and whitened principal components analysis are gaining popularity as tools for exploratory analysis of hyperspectral images. This paper shows that the two approaches are mathematically identical when signal and noise (clutter) are defined similarly. It also show...

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Veröffentlicht in:	Journal of chemometrics 2014-08, Vol.28 (8), p.663-671
Hauptverfasser:	Gallagher, Neal B., Shaver, Jeremy M., Bishop, Randall, Roginski, Robert T., Wise, Barry M.
Format:	Artikel
Sprache:	eng
Schlagworte:	Approximation Autocorrelation Clutter Correlation analysis Decomposition generalized eigenvalue problem Hyperspectral imaging Mathematical analysis preprocessing Principal component analysis Principal components analysis Projection Signal processing signal to clutter Signal to noise ratio standard eigenvalue problem Subspaces whitening
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