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Dependent Component Analysis: A Hyperspectral Unmixing Algorithm

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Pattern Recognition and Image Analysis (IbPRIA 2007)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 4478))

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Abstract

Linear unmixing decomposes a hyperspectral image into a collection of reflectance spectra of the materials present in the scene, called endmember signatures, and the corresponding abundance fractions at each pixel in a spatial area of interest.

This paper introduces a new unmixing method, called Dependent Component Analysis (DECA), which overcomes the limitations of unmixing methods based on Independent Component Analysis (ICA) and on geometrical properties of hyperspectral data.

DECA models the abundance fractions 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. The performance of the method is illustrated using simulated and real data.

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Authors and Affiliations

  1. Instituto Superior de Engenharia de Lisboa and Instituto de Telecomunicações,  

    José M. P. Nascimento

  2. Instituto de Telecomunicações and Instituto Superior Técnico, T.U. Lisbon,  

    José M. Bioucas-Dias

Authors
  1. José M. P. Nascimento
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  2. José M. Bioucas-Dias
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Joan Martí José Miguel Benedí Ana Maria Mendonça Joan Serrat

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Nascimento, J.M.P., Bioucas-Dias, J.M. (2007). Dependent Component Analysis: A Hyperspectral Unmixing Algorithm. In: Martí, J., Benedí, J.M., Mendonça, A.M., Serrat, J. (eds) Pattern Recognition and Image Analysis. IbPRIA 2007. Lecture Notes in Computer Science, vol 4478. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72849-8_77

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  • DOI: https://doi.org/10.1007/978-3-540-72849-8_77

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-72848-1

  • Online ISBN: 978-3-540-72849-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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