Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
Springer Nature Link
Log in

New Methods to Generate Neutral Images for Spatial Pattern Recognition

  • Conference paper
  • First Online:
Geographic Information Science (GIScience 2002)

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

Included in the following conference series:

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Anselin, L. (1995), Local Indicators of Spatial Association-LISA, Geographical Analysis, 27:93–115.

    Article  Google Scholar 

  2. Banzhaf, W., Nordin, P., Keller, R., and Francone, F. (1998), Genetic Programming, An Introduction, Morgan Kaufmann Publischers, Inc., San Francisco, CA.

    MATH  Google Scholar 

  3. Goovaerts, P. (1997), Geostatistics for Natural Resources Evaluation, Oxford University Press, New York, NY.

    Google Scholar 

  4. Gustafson, E. (1998), Quantifying Landscape Spatial Pattern: What is the State of the Art? Ecosystems (1): 143–156.

    Article  MathSciNet  Google Scholar 

  5. Moran, P. (1950), Notes on Continuous Stochastic Phenomena, Biometrika 37: 17–23.

    MATH  MathSciNet  Google Scholar 

  6. Pebesma, E. and Heuvelink, G. (1999), Latin Hypercube Sampling of Gaussian Random Fields, Technometrics, 41: 303–312.

    Article  Google Scholar 

  7. Rosenberg, M. (1998), The Bearing Correlogram: A New Method of Analyzing Directional Spatial Autocorrelation, Geographical Analysis, 33(3): 267–278

    Google Scholar 

  8. Verly, G. (1986), MultiGaussian Kriging—A Complete Case Study, in: R. Ramani, ed., Proceedings of the 19th International APCOM Symposium: Society of Mining Engineers, Littleton, CO, pp. 283–298.

    Google Scholar 

  9. Waller, L. and Jacquez, G. (1995), Disease Models Implicit in Statistical Tests of Disease Clustering, Epidemiology, 6(6): 584–590.

    Article  Google Scholar 

  10. Womble, W. (1951), Differential Systematics, Science, 114, 315–322.

    Article  Google Scholar 

Download references

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
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. 1Geoffrey Jacquez
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Pierre Goovaerts
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Andreas Kaufmann
    View author publications

    You can also search for this author in PubMed Google Scholar

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

Rights and permissions

Reprints and permissions

Copyright information

© 2002 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

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

Download citation

  • 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

Publish with us

Policies and ethics

Search

Navigation