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Nonparametric and Probabilistic Classification Using NN-balls with Environmental and Remote Sensing Applications

In: Advances in Directional and Linear Statistics

Author

Listed:
  • Bo Ranneby

    (Swedish University of Agricultural Sciences, Centre of Biostochastics)

  • Jun Yu

Abstract

National and international policies today require environmental follow-up systems that detect, in a quality assured way, changes over time in land use and landscape indicators. Questions related to environmental health and spatial patterns call for new statistical tools. We present in this paper some new developments on the classification of land use by using multispectral and multitemporal satellite images, based on techniques of nearest neighbour balls. The probabilistic classifiers introduced are useful for measuring uncertainty at pixel level and obtaining reliable area estimates locally. Also some theoretical considerations for the reference sample plot method (today named k-NN method in natural resource applications) are presented.

Suggested Citation

  • Bo Ranneby & Jun Yu, 2011. "Nonparametric and Probabilistic Classification Using NN-balls with Environmental and Remote Sensing Applications," Springer Books, in: Martin T. Wells & Ashis SenGupta (ed.), Advances in Directional and Linear Statistics, chapter 0, pages 201-216, Springer.
    • Handle: RePEc:spr:sprchp:978-3-7908-2628-9_14
    DOI: 10.1007/978-3-7908-2628-9_14
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