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Advisor(s)
Abstract(s)
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.
Description
Keywords
Dependent component analysis Unsupervised unmixing Hyperspectral data Linear mixture Model Simplex
Citation
NASCIMENTO, José M. P.; BIOUCAS-DIAS, José M. - Blind hyperspectral unmixing. Image and Signal Processing for Remote Sensing XIII - Proceedings of SPIE. ISSN 0277-786X. Vol. 6748. 67480J-1- 67480J8, 2007
Publisher
SPIE