ESTIMATION OF HYPERSPECTRAL COVARIANCE MATRICES

被引：2

作者：

Ben-David, Avishai ^{[1
]}

Davidson, Charles E. ^{[2
]}

机构：

[1] Res Dev & Engn Command, Edgewood Chem Biol Ctr, Aberdeen Proving Ground, MD 21010 USA

[2] Sci & Technol Corp, Edgewood, MD 21040 USA

来源：

2011 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS) | 2011年

关键词：

covariance matrices; eigenvalues and eigenfunctions; hyperspectral detection and signal processing algorithms; regularization;

D O I：

10.1109/IGARSS.2011.6050188

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

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.

引用

页码：4324 / 4327

页数：4

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