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Contextual reclassification of multispectral images: a Markov Random Field approach
SLU, Centre of Biostochastics.
SLU, Centre of Biostochastics.
2002 (English)In: Information Processes, ISSN 1819-5822, Vol. 2, no 1, 12-21 p.Article in journal (Refereed) Published
Abstract [en]

This work presents methods for multispectral image classification using the contextual classifiersbased on Markov Random Field (MRF) models. Performance of some conventional classification methods is evaluated, through a Monte Carlo study, with or without using the contextual reclassification. Spatial autocorrelation is present in the computer-generated data on a true scene. The total misclassification rates for varying strengths of autocorrelation and for different methods are compared. The results indicate that the combination of the spectral-contextual classifiers can improve to a great extent the accuracyof conventional non-contextual classification methods. It is also shown how the most complicated cases can be handled by the Gibbs sampler.

Place, publisher, year, edition, pages
2002. Vol. 2, no 1, 12-21 p.
Keyword [en]
Monte Carlo study, contextual classification, Markov random field, ICM, Gibbs sampler, spatial autocorrelation, multi-spectral imagery, remote sensing.
National Category
Probability Theory and Statistics
Research subject
Mathematical Statistics
Identifiers
URN: urn:nbn:se:umu:diva-63694OAI: oai:DiVA.org:umu-63694DiVA: diva2:582368
Available from: 2013-01-04 Created: 2013-01-04 Last updated: 2013-10-08Bibliographically approved

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http://www.jip.ru/2002/TETER.pdf

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Yu, Jun
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CiteExportLink to record
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Citation style
  • apa
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  • asciidoc
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