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Some issues in contextual fuzzy c-means classification of remotely sensed data for land cover mapping
A DUTTA, A KUMAR, S SARKAR
Published in SPRINGER
2010
Volume: 38
   
Issue: 1
Pages: 109 - 118
Abstract
Earlier for the hard classification techniques contextual information was used to improve classification accuracy. While modelling the spatial contextual information for hard classifiers using Markov Random Field it has been found that Metropolis algorithm is easier to program and it performs better in comparison to the Gibbs sampler. In the present study it has been found that incase of soft contextual classification Metropolis algorithm fails to sample from a random field efficiently and from the analysis it was found that Metropolis algorithm is not suitable for soft contextual classification due to the high dimensionality of the soft outputs. © 2010 Indian Society of Remote Sensing.
About the journal
JournalData powered by TypesetJournal of the Indian Society of Remote Sensing
PublisherData powered by TypesetSPRINGER
ISSN0255-660X