Contextual reclassification of multispectral images: a Markov Random Field approach
2002 (English)In: Information Processes, ISSN 1819-5822, Vol. 2, no 1, 12-21 p.Article in journal (Refereed) Published
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.
Monte Carlo study, contextual classification, Markov random field, ICM, Gibbs sampler, spatial autocorrelation, multi-spectral imagery, remote sensing.
Probability Theory and Statistics
Research subject Mathematical Statistics
IdentifiersURN: urn:nbn:se:umu:diva-63694OAI: oai:DiVA.org:umu-63694DiVA: diva2:582368