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International Workshop on Multiple Classifier Systems

MCS 2001: Multiple Classifier Systems pp 269–278Cite as

Combining Supervised Remote Sensing Image Classifiers Based on Individual Class Performances

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2096))

Abstract

This article focuses on the use of multiple classifier systems (MCSs) based on dynamic classifier selection. Four implementation strategies of MCSs are compared: majority voting, belief networks, and two designs based on dynamic classifier selection. Experimental results indicate that the direction taken by Woods et al. [1] is the best alternative for remote sensing applications for which the classifier-dependent posterior distributions are unknown.

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References

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Author information

Authors and Affiliations

  1. Space Applications Institute, Joint Research Centre, TP262, I-21020, Ispra (VA), Italy

    Paul C. Smits

Authors
  1. Paul C. Smits
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Editor information

Editors and Affiliations

  1. Centre for Vision, Speech and Signal Processing, University of Surrey, Guildford, Surrey, GU2 7XH, UK

    Josef Kittler

  2. Department of Electrical and Electronic Engineering, University of Cagliari, Piazza d’Armi, 09123, Cagliari, Italy

    Fabio Roli

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© 2001 Springer-Verlag Berlin Heidelberg

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Smits, P.C. (2001). Combining Supervised Remote Sensing Image Classifiers Based on Individual Class Performances. In: Kittler, J., Roli, F. (eds) Multiple Classifier Systems. MCS 2001. Lecture Notes in Computer Science, vol 2096. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48219-9_27

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  • DOI: https://doi.org/10.1007/3-540-48219-9_27

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-42284-6

  • Online ISBN: 978-3-540-48219-2

  • eBook Packages: Springer Book Archive

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