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International Symposium on Visual Computing

ISVC 2008: Advances in Visual Computing pp 85–94Cite as

Classification of Multispectral High-Resolution Satellite Imagery Using LIDAR Elevation Data

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 5359))

Abstract

This paper studies the influence of airborne LIDAR elevation data on the classification of multispectral SPOT5 imagery over a semi-urban area; to do this, multispectral and LIDAR elevation data are integrated in a single imagery file composed of independent multiple bands. The Support Vector Machine is used to classify the imagery. A scheme of five classes was chosen; ground truth samples were then collected in two sets, one for training the classifier and the other for checking its quality after classification. The results show that the integration of LIDAR elevation data improves the classification of multispectral bands; the assessment and comparison of the classification results have been carried out using complete confusion matrices. Improvements are evident in classes with similar spectral characteristics but for which altitude is a relevant discrimination factor. An overall improvement of 28.3% was obtained, when LIDAR was included.

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Author information

Authors and Affiliations

  1. Dpto. de Matemáticas Escuela Politécnica, Universidad de Alcalá de Henares, Spain

    María C. Alonso & José A. Malpica

Authors
  1. María C. Alonso
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  2. José A. Malpica
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, University of Nevada, Reno, USA

    George Bebis

  2. NASA Ames Research Center, Moffett Field, CA, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Bahram Parvin

  4. Desert Research Institute, Reno, NV, USA

    Darko Koracin

  5. Digital Image Research Center, Kingston University, London, UK

    Paolo Remagnino

  6. Mitsubishi Electric Research Laboratories, P.O. Box 02139, Cambridge, MA, USA

    Fatih Porikli

  7. Computer and Information Science and Engineering, University of Florida, P.O. Box, FL 32611-6120, Gainsville, USA

    Jörg Peters

  8. IBM T.J. Watson Research Center, 19 Skyline Drive, NY 10532, Hawthorne, USA

    James Klosowski

  9. 128 Memorial Mall, Stewart B001, IN 47907, West Lafayette, USA

    Laura Arns

  10. Denver Museum of Nature and Space, 2001 Colorade Blvd. Denver,, CO 80205, USA

    Yu Ka Chun

  11. Departmen of Computer Science,, NC State University, Campus Box 8206, NC 27695-8206, Raleigh, USA

    Theresa-Marie Rhyne

  12. Los Alamos National Labs, P.O. Box 1663, NM 87545, Los Alamos, USA

    Laura Monroe

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© 2008 Springer-Verlag Berlin Heidelberg

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Alonso, M.C., Malpica, J.A. (2008). Classification of Multispectral High-Resolution Satellite Imagery Using LIDAR Elevation Data. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2008. Lecture Notes in Computer Science, vol 5359. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89646-3_9

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  • DOI: https://doi.org/10.1007/978-3-540-89646-3_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-89645-6

  • Online ISBN: 978-3-540-89646-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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