On the reliability of PCA for complex hyperspectral data

WHISPERS '09 - 1st Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing

Volumn , Issue , 2009, Pages

  Bajorski, Peter ^a  

^a ROCHESTER INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

Eigenvalue; Eigenvector; Hyperspectral image; Principal component analysis; Sampling scheme

Indexed keywords

EIGEN-VALUE; EIGENVECTORS; HYPER-SPECTRAL IMAGES; HYPERSPECTRAL IMAGE; SAMPLING SCHEMES;

ASYMPTOTIC ANALYSIS; ATMOSPHERICS; COVARIANCE MATRIX; DATA HANDLING; INDEPENDENT COMPONENT ANALYSIS; PRINCIPAL COMPONENT ANALYSIS; REMOTE SENSING; SAMPLING; SIGNAL PROCESSING; TARGET TRACKING;

EIGENVALUES AND EIGENFUNCTIONS;

EID: 72049131572     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/WHISPERS.2009.5289076     Document Type: Conference Paper

References (3)

1. Johnson, R.A. and D.W. Wichern, Applied Multivariate Statistical Analysis, 6th edition, Prentice Hall, 2007.
2. Feng, X., J. R. Schott, and T.W. Gallagher, "Modeling and testing of a modular imaging spectrometer instrument," Proc. SPIE, Vol. 2224, 215 (1994).
3. E.J.Ientilucci, "Comparison and Usage of Principal Component Analysis (PCA) and Noise Adjusted Principal Component (NAPC) Analysis or Maximum Noise Fraction (MNF)," Technical Report, Rochester Institute of Technology, 2003.