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Support Vector Machines for Road Extraction from Remotely Sensed Images

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Computer Analysis of Images and Patterns (CAIP 2003)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2756))

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Abstract

Support Vector Machines (SVMs) have received considerable attention from the pattern recognition community in recent years. They have been successfully applied to many classic recognition problems with results comparable or even superior to traditional classifiers such as decision trees, neural networks, maximum likelihood classifiers, etc. This paper presents encouraging experimental results from applying SVMs to the problem of road recognition and extraction from remotely sensed images using edge-based features.

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References

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Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, University of New South Wales, Sydney, NSW 2052, Australia

    Neil Yager & Arcot Sowmya

Authors
  1. Neil Yager
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  2. Arcot Sowmya
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Editor information

Editors and Affiliations

  1. Institute of Mathematics and Computing Science, University of Groningen, P.O.Box 800, 9700 AV, Groningen, The Netherlands

    Nicolai Petkov

  2. Institute of Mathematics and Computing Science, University of Groningen, Blauwborgje 3, 9747 AC, Groningen, The Netherlands

    Michel A. Westenberg

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© 2003 Springer-Verlag Berlin Heidelberg

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Yager, N., Sowmya, A. (2003). Support Vector Machines for Road Extraction from Remotely Sensed Images. In: Petkov, N., Westenberg, M.A. (eds) Computer Analysis of Images and Patterns. CAIP 2003. Lecture Notes in Computer Science, vol 2756. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45179-2_36

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  • DOI: https://doi.org/10.1007/978-3-540-45179-2_36

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40730-0

  • Online ISBN: 978-3-540-45179-2

  • eBook Packages: Springer Book Archive

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