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International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition

EMMCVPR 2001: Energy Minimization Methods in Computer Vision and Pattern Recognition pp 344–356Cite as

Two Variational Models for Multispectral Image Classification

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2134))

Abstract

We propose two variational models for supervised classification of multispectral data. Both models take into account contour and region information by minimizing a functional compound of a data term (2D surface integral) taking into account the observation data and knowledge on the classes, and a regularization term (1D length integral) minimizing the length of the interfaces between regions. This is a free discontinuity problem and we have proposed two different ways to reach such a minimum, one using a Γ -convergence approach and the other using a level set approach to model contours and regions.

Both methods have been previously developed in the case of monospectral observations. Multispectral techniques allow to take into account information of severalsp ectralbands of satellite or aerialsensors. The goalof this paper is to present the extension of both variationalcl assification methods to multispectral data. We show an application on real data from SPOT (XS mode) satellite for which we have a ground truth. Our results are also compared to results obtained by using a hierarchical stochastic model.

This work has been conducted in relation with the GdR-PRC ISIS research group (http://www.isis.enst.fr)

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Author information

Authors and Affiliations

  1. Ariana, joint research group I3S (CNRS/UNSA) and INRIA, Inria, 2004 route des Lucioles, BP 93, 06902, Sophia Antipolis cedex, France

    Christophe Samson, Laure Blanc-Féraud & Josiane Zerubia

  2. Laboratoire J.A. Dieudonné, Umr 6621 du Cnrs, Université de Nice Sophia, Antipolis, France

    Gilles Aubert

Authors
  1. Christophe Samson
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  2. Laure Blanc-Féraud
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  3. Gilles Aubert
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  4. Josiane Zerubia
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Editor information

Editors and Affiliations

  1. Instituto de Telecomunicacoes and Instituto Superior Tecnico, 1049-001, Lisboa, Portugal

    Mário Figueiredo

  2. INRIA, 2004 Route des Lucioles, 06902 Sophia-Antipolis cedex, France

    Josiane Zerubia

  3. Department of Computer Science and Engineering, Michigan State University, East Lansing, MI, 48824, USA

    Anil K. Jain

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© 2001 Springer-Verlag Berlin Heidelberg

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Samson, C., Blanc-Féraud, L., Aubert, G., Zerubia, J. (2001). Two Variational Models for Multispectral Image Classification. In: Figueiredo, M., Zerubia, J., Jain, A.K. (eds) Energy Minimization Methods in Computer Vision and Pattern Recognition. EMMCVPR 2001. Lecture Notes in Computer Science, vol 2134. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44745-8_23

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  • DOI: https://doi.org/10.1007/3-540-44745-8_23

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-42523-6

  • Online ISBN: 978-3-540-44745-0

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