Estimation of hyperspectral covariance matrices

Author

Ben-David, Avishai ; Davidson, Charles E.

Author_Institution

Res., Dev. & Eng. Command, Edgewood Chem. Biol. Center, U.S. Army, Aberdeen Proving Ground, MD, USA

fYear

2011

fDate

24-29 July 2011

Firstpage

4324

Lastpage

4327

Abstract

Estimation of covariance matrices is a fundamental step in hyperspectral remote sensing where most detection algorithms make use of the covariance matrix in whitening procedures. We present a simple method to improve the estimation of the eigenvalues of a sample covariance matrix. The method achieves two objectives simultaneously: improved estimation of eigenvalues and improved condition number (regularization). Our method is based on the Marcenko-Pastur law, theory of eigenvalue bounds, and energy conservation.

Keywords

covariance matrices; eigenvalues and eigenfunctions; geophysical image processing; geophysical signal processing; remote sensing; statistical analysis; Marcenko-Pastur law; detection algorithms; eigenvalue bounds; eigenvalue estimation; energy conservation; hyperspectral covariance matrix estimation; hyperspectral remote sensing; whitening procedures; Covariance matrix; Eigenvalues and eigenfunctions; Energy conservation; Estimation; Hyperspectral imaging; covariance matrices; eigenvalues and eigenfunctions; hyperspectral detection and signal processing algorithms; regularization;

fLanguage

English

Publisher

ieee

Conference_Titel

Geoscience and Remote Sensing Symposium (IGARSS), 2011 IEEE International

Conference_Location

Vancouver, BC

ISSN

2153-6996

Print_ISBN

978-1-4577-1003-2

Type

conf

DOI

10.1109/IGARSS.2011.6050188

Filename

6050188