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Advisor(s)
Abstract(s)
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.
Description
Keywords
Unmixing hyperspectral data Independent component analysis Mixture of Gaussians
Citation
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
Publisher
SPIE