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Proceedings Paper

Blind hyperspectral unmixing
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Paper Abstract

Hyperspectral unmixing methods aim at the decomposition of a hyperspectral image into a collection endmember signatures, i.e., the radiance or reflectance of the materials present in the scene, and the correspondent abundance fractions at each pixel in the image. This paper introduces a new unmixing method termed dependent component analysis (DECA). This method is blind and fully automatic and it overcomes the limitations of unmixing methods based on Independent Component Analysis (ICA) and on geometrical based approaches. DECA is based on the linear mixture model, i.e., each pixel is a linear mixture of the endmembers signatures weighted by the correspondent abundance fractions. These abundances are modeled as mixtures of Dirichlet densities, thus enforcing the non-negativity and constant sum constraints, imposed by the acquisition process. The endmembers signatures are inferred by a generalized expectation-maximization (GEM) type algorithm. The paper illustrates the effectiveness of DECA on synthetic and real hyperspectral images.

Paper Details

Date Published: 26 October 2007
PDF: 8 pages
Proc. SPIE 6748, Image and Signal Processing for Remote Sensing XIII, 67480J (26 October 2007); doi: 10.1117/12.738158
Show Author Affiliations
José M. P. Nascimento, Instituto Superior Técnico (Portugal)
José M. Bioucas-Dias, Instituto Superior Técnico (Portugal)


Published in SPIE Proceedings Vol. 6748:
Image and Signal Processing for Remote Sensing XIII
Lorenzo Bruzzone, Editor(s)

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