Linear-quadratic and polynomial Non-Negative Matrix Factorization; application to spectral unmixing

Author

Meganem, Ines ; Deville, Yannick ; Hosseini, Shahram ; Deliot, Philippe ; Briottet, Xavier ; Duarte, Leonardo T.

Author_Institution

IRAP, Univ. de Toulouse, Toulouse, France

fYear

2011

fDate

Aug. 29 2011-Sept. 2 2011

Firstpage

1859

Lastpage

1863

Abstract

In this article, we present a source separation method for linear-quadratic models. This class of mixing models is encountered in various real applications, such as hyperspectral unmixing for urban environments. Linear-quadratic mixing models are less studied in the literature than linear ones but there exist some methods for handling them, essentially Bayesian or based on Independent Component Analysis.

Keywords

Bayes methods; independent component analysis; matrix decomposition; polynomial matrices; source separation; spectral analysis; Bayesian analysis; NMF; artificial mixtures; artificial signals; hyperspectral unmixing; independent component analysis; linear-quadratic models; mixing models; polynomial nonnegative matrix factorization; reflectance spectra; source separation method; spectral unmixing; urban environments; Adaptation models; Estimation error; Hyperspectral imaging; Mathematical model; Polynomials;

fLanguage

English

Publisher

ieee

Conference_Titel

Signal Processing Conference, 2011 19th European

Conference_Location

Barcelona

ISSN

2076-1465

Type

conf

Filename

7074169

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2271184