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

  • Bo Ranneby
  • Jun Yu


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.


Feature Vector Feature Space Linear Discriminant Analysis Multispectral Image Area Estimate 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  1. 1.Centre of BiostochasticsSwedish University of Agricultural SciencesUmeåSweden

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