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International Conference on Geographic Information Science

GIScience 2002: Geographic Information Science pp 181–195Cite as

New Methods to Generate Neutral Images for Spatial Pattern Recognition

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2478))

Abstract

Three new methods are developed to generate neutral spatial models for pattern recognition on raster data. The first method employs Genetic Programming (GP), the second Sequential Gaussian Simulation (SGS), and the third Conditional Pixel Swapping (CPS) in order to produce sets of “neutral images” that provide a probabilistic assessment of how unlikely an observed spatial pattern on a target image is under the null hypothesis. The sets of neutral images generated by the three methods are found to preserve different aspects of spatial autocorrelation on the target image. This preliminary research demonstrates the feasibility of using neutral image generation in spatial pattern recognition.

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Author information

Authors and Affiliations

  1. Biomedware, Inc., 516 N. State Street, Ann Arbor, MI, 48104, USA

    Niels Liebisch & Andreas Kaufmann

  2. Department of Civil and Environmental Engineering, University of Michigan, Ann Arbor, MI, 48109, USA

    Pierre Goovaerts

Authors
  1. Niels Liebisch
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  2. 1Geoffrey Jacquez
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  3. Pierre Goovaerts
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  4. Andreas Kaufmann
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Editor information

Editors and Affiliations

  1. Department of Spatial Information Science and Engineering, National Center for Geographic Information and Analysis, Orono, ME, 04469-5711, USA

    Max J. Egenhofer

  2. Department of Computer Science, University of Maine, Orono, ME, 04469-5711, USA

    Max J. Egenhofer

  3. National Center for Geographic Information and Analysis Department of Geography, University at Buffalo, 105 Wilkeson Quad., Box 610023, Buffalo, NY, 14261-0023, USA

    David M. Mark

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© 2002 Springer-Verlag Berlin Heidelberg

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Liebisch, N., Jacquez, 1., Goovaerts, P., Kaufmann, A. (2002). New Methods to Generate Neutral Images for Spatial Pattern Recognition. In: Egenhofer, M.J., Mark, D.M. (eds) Geographic Information Science. GIScience 2002. Lecture Notes in Computer Science, vol 2478. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45799-2_13

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  • DOI: https://doi.org/10.1007/3-540-45799-2_13

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-44253-0

  • Online ISBN: 978-3-540-45799-2

  • eBook Packages: Springer Book Archive

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