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Titolo:
A comparison of canonical discriminant analysis and principal component analysis for spectral transformation
Autore:
Zhao, G; Maclean, AL;
Indirizzi:
Michigan Technol Univ, Sch Forestry, Houghton, MI 49931 USA Michigan Technol Univ Houghton MI USA 49931 estry, Houghton, MI 49931 USA
Titolo Testata:
PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING
fascicolo: 7, volume: 66, anno: 2000,
pagine: 841 - 847
Fonte:
ISI
Lingua:
ENG
Soggetto:
ACCURACY;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Engineering, Computing & Technology
Citazioni:
28
Recensione:
Indirizzi per estratti:
Indirizzo: Zhao, G S Carolina Dept Hlth & Environm Control, Div Biostat, 2600 Bull St, Columbia, SC 29201 USA S Carolina Dept Hlth & Environm Control 2600 Bull St Columbia SC USA 29201
Citazione:
G. Zhao e A.L. Maclean, "A comparison of canonical discriminant analysis and principal component analysis for spectral transformation", PHOTOGR E R, 66(7), 2000, pp. 841-847

Abstract

A study was conducted in Michigan's Upper Peninsula to test the strength and weakness of canonical discriminant analysis (CDA) as a spectral transformation technique to separate ground scene classes which have close spectralsignatures. Classification accuracies using CDA transformed images were compared to those using principal component analysis (PCA) transformed images. Results showed that Kappa accuracies using CDA images were significantly higher than those derived using PCA at alpha = 0.05. Comparison of CDA and PCA eigen structure matrices indicated that there is no distinct pattern interms of source variable contributions and load signs between the canonical discriminant functions and the principal components.

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Documento generato il 03/12/20 alle ore 16:06:09