Empirical Automatic Estimation of the Number of Endmembers in Hyperspectral Images

Author

Luo, Bin ; Chanussot, Jocelyn ; Douté, Sylvain ; Zhang, Liangpei

Author_Institution

State Key Lab. of Inf. Eng. in Surveying, Mapping & Remote Sensing, Wuhan Univ., Wuhan, China

Volume

10

Issue

1

fYear

2013

fDate

Jan. 2013

Firstpage

24

Lastpage

28

Abstract

In this letter, an eigenvalue-based empirical method is proposed in order to estimate the number of endmembers in hyperspectral data. This method is based on the distribution of the differences of the eigenvalues from the correlation and the covariance matrices, respectively. The eigenvalues corresponding to the noise are identical in the covariance and the correlation matrices, while the eigenvalues corresponding to the signal (the endmembers) are larger in the correlation matrix than in the covariance matrix. The proposed method is totally parameter free and very fast. It is validated by experiments carried on both synthetic and real data sets.

Keywords

correlation theory; covariance matrices; eigenvalues and eigenfunctions; geophysical image processing; geophysical techniques; correlation matrices; covariance matrices; eigenvalue-based empirical method; empirical automatic estimation; hyperspectral data; hyperspectral images; real data sets; synthetic data sets; Estimation; Hybrid fiber coaxial cables; Hyperspectral imaging; Signal to noise ratio; Imaging; spectral analysis;

fLanguage

English

Journal_Title

Geoscience and Remote Sensing Letters, IEEE

Publisher

ieee

ISSN

1545-598X

Type

jour

DOI

10.1109/LGRS.2012.2189934

Filename

6194271