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Nonparametric and probabilistic classification using NN-balls with environmental and remote sensing applications
SLU, Centre of Biostochastics.
Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics. SLU, Centre of Biostochastics. (Arcum)
2011 (English)In: Advances in Directional and Linear Statistics: A Festschrift for Sreenivasa Rao Jammalamadaka / [ed] M.T. Wells & A. Sengupta, Heidelberg: Physica Verlag, 2011, 201-216 p.Chapter in book (Refereed)
Abstract [en]

National and international policies today require environmental follow-upsystems 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 chapter 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 plotmethod (today named k-NN method in natural resource applications) are presented.

Place, publisher, year, edition, pages
Heidelberg: Physica Verlag, 2011. 201-216 p.
National Category
Probability Theory and Statistics
Research subject
Mathematical Statistics
URN: urn:nbn:se:umu:diva-63687DOI: 10.1007/978-3-7908-2628-9_14ISI: 000284726800014ISBN: 978-3-7908-2627-2ISBN: 978-3-7908-2628-9OAI: diva2:582324
Available from: 2013-01-04 Created: 2013-01-04 Last updated: 2016-06-20Bibliographically approved

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Yu, Jun
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