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Advisor(s)
Abstract(s)
This paper introduces a new method to blindly unmix hyperspectral data, termed dependent component analysis (DECA). This method decomposes a hyperspectral images into a collection of reflectance (or radiance) spectra of the materials present in the scene (endmember signatures) and the corresponding abundance fractions at each pixel.
DECA assumes that each pixel is a linear mixture of the endmembers signatures weighted by the correspondent abundance fractions. These abudances are modeled as mixtures of Dirichlet densities, thus enforcing the constraints on abundance fractions imposed by the acquisition process, namely non-negativity and constant sum. The mixing matrix is inferred by a generalized expectation-maximization (GEM) type algorithm. This method overcomes the limitations of unmixing methods based on Independent Component Analysis (ICA) and on geometrical based approaches. The effectiveness of the proposed method is illustrated using simulated data based on U.S.G.S. laboratory spectra and real hyperspectral data collected by the AVIRIS sensor over Cuprite, Nevada.
Description
Keywords
Expectation-maximisation algorithm Geophysical techniques Geophysics computing Dirichlet densities Abundance fractions Dependent component analysis Generalized expectation-maximization algorithm Hyperspectral unmixing algorithm Algorithm design and analysis Hyperspectral imaging Hyperspectral sensors Ice Independent component analysis Infrared image sensors Laboratories Layout Reflectivity Telecommunications
Citation
NASCIMENTO, José M. P.; BIOUCAS-DIAS, José M. - Hyperspectral unmixing algorithm via dependent component analysis. IGARSS: 2007 IEEE International Geoscience and Remote Sensing Symposium, Vols 1-12: Sensing And Understanding Our Planet. ISBN 978-14244-1211-2. 4033-4036, 2007
Publisher
IEEE-Institute Electrical Electronics Engineers INC.