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Signal Subspace Identification in Hyperspectral Linear Mixtures

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Pattern Recognition and Image Analysis (IbPRIA 2005)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 3523))

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Abstract

Hyperspectral applications in remote sensing are often focused on determining the so-called spectral signatures, i.e., the reflectances of materials present in the scene (endmembers) and the corresponding abundance fractions at each pixel in a spatial area of interest. The determination of the number of endmembers in a scene without any prior knowledge is crucial to the success of hyperspectral image analysis. This paper proposes a new mean squared error approach to determine the signal subspace in hyperspectral imagery. The method first estimates the signal and noise correlations matrices, then it selects the subset of eigenvalues that best represents the signal subspace in the least square sense.

This work was supported by the FCT, under the projects POSI/34071/CPS/2000 and PDCTE/CPS/49967/2003 and by DEETC of ISEL.

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Authors and Affiliations

  1. Instituto Superior de Engenharia de Lisboa and Instituto de Telecomunicações, R. Conselheiro Emídio Navarro N 1, edifício DEETC, 1950-062, Lisboa, Portugal

    José M. P. Nascimento

  2. Instituto Superior Técnico and Instituto de Telecomunicações, Av. Rovisco Pais, Torre Norte, Piso 10, 1049-001, Lisboa, Portugal

    José M. B. Dias

Authors
  1. José M. P. Nascimento
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  2. José M. B. Dias
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Editor information

Editors and Affiliations

  1. Instituto Superior Técnico & Instituto de Sistemas e Robótica,, 1049-001, Lisboa, Portugal

    Jorge S. Marques

  2. ETSI Informática y e Telecomunicación, University of Granada, 18071, Granada, Spain

    Nicolás Pérez de la Blanca

  3. Instituto Superior Técnico, CERENA-Centro de Recursos Naturais e Ambiente, Av. Rovisco Pais, 1049-001, Lisboa, Portugal

    Pedro Pina

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© 2005 Springer-Verlag Berlin Heidelberg

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Nascimento, J.M.P., Dias, J.M.B. (2005). Signal Subspace Identification in Hyperspectral Linear Mixtures. In: Marques, J.S., Pérez de la Blanca, N., Pina, P. (eds) Pattern Recognition and Image Analysis. IbPRIA 2005. Lecture Notes in Computer Science, vol 3523. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11492542_26

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  • DOI: https://doi.org/10.1007/11492542_26

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-26154-4

  • Online ISBN: 978-3-540-32238-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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