Title :
Covariance estimation with limited training samples
Author :
Tadjudin, Saldju ; Landgrebe, David A.
Author_Institution :
Sch. of Electr. & Comput. Eng., Purdue Univ., West Lafayette, IN, USA
fDate :
7/1/1999 12:00:00 AM
Abstract :
This paper describes a covariance estimator formulated under an empirical Bayesian setting to mitigate the problem of limited training samples in the Gaussian maximum likelihood (ML) classification for remote sensing. The most suitable covariance mixture is selected by maximizing the average leave-one-out log likelihood. Experimental results using AVIRIS data are presented
Keywords :
Bayes methods; covariance analysis; geophysical signal processing; geophysical techniques; image classification; remote sensing; terrain mapping; AVIRIS; Bayes method; Gaussian maximum likelihood classification; average leave-one-out log likelihood; covariance estimation; covariance estimator; covariance mixture; empirical Bayesian setting; geophysical measurement technique; image classification; image processing; land surface; limited training sample; optical imaging; remote sensing; terrain mapping; Bayesian methods; Covariance matrix; Degradation; Density functional theory; Maximum likelihood estimation; Military computing; NASA; Parameter estimation; Remote sensing;
Journal_Title :
Geoscience and Remote Sensing, IEEE Transactions on
