On independent component analysis applied to unmixing hyperspectral data


Autoria(s): Nascimento, José M. P.; Bioucas-Dias, José M.
Data(s)

27/05/2016

27/05/2016

2004

Resumo

One of the most challenging task underlying many hyperspectral imagery applications is the spectral unmixing, which decomposes a mixed pixel into a collection of reectance spectra, called endmember signatures, and their corresponding fractional abundances. Independent Component Analysis (ICA) have recently been proposed as a tool to unmix hyperspectral data. The basic goal of ICA is to nd a linear transformation to recover independent sources (abundance fractions) given only sensor observations that are unknown linear mixtures of the unobserved independent sources. In hyperspectral imagery the sum of abundance fractions associated to each pixel is constant due to physical constraints in the data acquisition process. Thus, sources cannot be independent. This paper address hyperspectral data source dependence and its impact on ICA performance. The study consider simulated and real data. In simulated scenarios hyperspectral observations are described by a generative model that takes into account the degradation mechanisms normally found in hyperspectral applications. We conclude that ICA does not unmix correctly all sources. This conclusion is based on the a study of the mutual information. Nevertheless, some sources might be well separated mainly if the number of sources is large and the signal-to-noise ratio (SNR) is high.

Identificador

NASCIMENTO, José M. P.; BIOUCAS-DIAS, José M. - On independent component analysis applied to unmixing hyperspectral data. Proceedings of SPIE - Image and Signal Processing for Remote Sensing IX. ISSN 0277-786X. Vol. 5238. pp. 306-315, 2004

0277-786X

http://hdl.handle.net/10400.21/6212

10.1117/12.510652

Idioma(s)

eng

Publicador

SPIE

Relação

POSI/34071/CPS/2000

Direitos

closedAccess

Palavras-Chave #Unmixing hyperspectral data #Independent component analysis #Mixture of Gaussians
Tipo

conferenceObject