Gaussian mixture classifier with regularized covariance estimator for hyperspectral data classification

Author

Kuo, Bor-Chen ; Yang, Jinn-Min ; Landgrebe, David A.

Author_Institution

Graduate Sch. of Educational Meas. & Stat., Nat. Taichung Teachers Coll., Taiwan

Volume

1

fYear

2003

fDate

21-25 July 2003

Firstpage

276

Abstract

New Gaussian mixture classifiers are designed by replacing the maximum likelihood covariance estimator with regularized covariance estimator in both parameters estimation and model selection steps. The results of simulated and real data experiments suggested that nearest mean clustering and Bayesian information criterion with regularized covariance estimator is a better choice to build a Gaussian mixture classifier.

Keywords

Gaussian processes; geophysical signal processing; geophysical techniques; maximum likelihood estimation; spectral analysis; BIC; Bayesian information criterion; Gaussian mixture classifier; hyperspectral data classification; maximum likelihood covariance estimator; model selection; nearest mean clustering; parameters estimation; real data experiments; regularized covariance estimator; simulated data; Bayesian methods; Computer science education; Electric variables measurement; Gaussian distribution; Hyperspectral imaging; Mathematics; Maximum likelihood estimation; National electric code; Parameter estimation; Statistics;

fLanguage

English

Publisher

ieee

Conference_Titel

Geoscience and Remote Sensing Symposium, 2003. IGARSS '03. Proceedings. 2003 IEEE International

Print_ISBN

0-7803-7929-2

Type

conf

DOI

10.1109/IGARSS.2003.1293749

Filename

1293749