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Robust Principal Components for Hyperspectral Data Analysis

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Image Analysis and Recognition (ICIAR 2009)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 5627))

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Abstract

Remote sensing data present peculiar features and characteristics that may make their statistical processing and analysis a difficult task. Among them, it can be mentioned the volume of data involved, the redundancy, the presence of unexpected values that arise mainly due to noisy pixels and background objects whose responses to the sensor are very different from those of their neighbours. Sometimes, the volume of data and number of variables involved is so large that any statistical analysis becomes unmanageable if data are not condensed in some way. A commonly used method to deal with this situation is Principal Component Analysis (PCA) based on classical statistics: sample mean and covariance matrices. The drawback in using sample covariance or correlation matrices as measures of variability is their high sensitivity to spurious values. In this work we analyse and evaluate the use of some Robust Principal Component techniques and make a comparison of Robust and Classical PCs performances when applied to satellite data provided by the hyperspectral sensor AVIRIS (Airborne Visible/Infrared Imaging Spectrometer). We conclude that some robust approaches are the most reliable and precise when applied as a data reduction technique before performing supervised image classification.

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Author information

Authors and Affiliations

  1. CONICET (Consejo Nacional de Investigaciones Científicas y Técnicas, Argentina), Argentina

    María M. Lucini

  2. FaCENA, Universidad Nacional del Nordeste, Av. Libertad 5460, 3400, Corrientes, Argentina

    María M. Lucini

  3. Instituto de Computação, CPMAT & LCCV, Universidade Federal de Alagoas, BR 104 Norte km 97, 57072-970, Maceió, AL, Brazil

    Alejandro C. Frery

Authors
  1. María M. Lucini
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  2. Alejandro C. Frery
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Editor information

Editors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Waterloo, N2L 3G1, Waterloo, Ontario, Canada

    Mohamed Kamel

  2. Faculty of Engineering, Institute of Biomedical Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465, Porto, Portugal

    Aurélio Campilho

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© 2009 Springer-Verlag Berlin Heidelberg

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Lucini, M.M., Frery, A.C. (2009). Robust Principal Components for Hyperspectral Data Analysis. In: Kamel, M., Campilho, A. (eds) Image Analysis and Recognition. ICIAR 2009. Lecture Notes in Computer Science, vol 5627. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02611-9_13

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  • DOI: https://doi.org/10.1007/978-3-642-02611-9_13

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-02610-2

  • Online ISBN: 978-3-642-02611-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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