A study of parameter values for a Mahalanobis distance fuzzy classifier

Peter J Deer, Peter Eklund

Research output: Journal Article or Conference Article in JournalJournal articleResearchpeer-review

Abstract

A supervised Mahalanobis distance fuzzy classifier (and the related fuzzy c-means clustering algorithm) requires the a priori selection of a weighting parameter called the fuzzy exponent. Guidance in the existing literature on an appropriate value is not definitive. This paper attempts to rigorously justify previous experimental findings on suitable values for this fuzzy exponent, using the criterion that fuzzy set memberships reflect class proportions in the mixed pixels of a remotely sensed image.
Original languageUndefined/Unknown
JournalFuzzy Sets and Systems
Volume137
Issue number2
Pages (from-to)191-213
Number of pages23
ISSN0165-0114
Publication statusPublished - 2003
Externally publishedYes

Keywords

  • Fuzzy classification
  • Image Processing

Cite this