Title :
Selecting parameter values for mahalanobis distance fuzzy classifiers
Author :
Deer, Peter ; Eklund, Peter
Author_Institution :
Knowledge, Visualization & Ordering Lab., Griffith Univ., Gold Coast, Qld., Australia
Abstract :
The fuzzy c-means clustering algorithm, and a related supervised classifier, require the a priori selection of a weighting parameter called the fuzzy exponent. This paper investigates suitable values of this fuzzy exponent using the criterion that fuzzy set memberships reflect class proportions in the mixed pixels of a remotely sensed image.
Keywords :
fuzzy set theory; image classification; remote sensing; fuzzy c-means clustering; fuzzy exponent; fuzzy image classification; fuzzy set theory; pattern recognition; remote sensing; supervised classifier; weighting parameter; Australia; Clustering algorithms; Covariance matrix; Fuzzy control; Fuzzy sets; Gold; Laboratories; Pattern recognition; Pixel; Visualization;
Conference_Titel :
Fuzzy Systems, 2001. The 10th IEEE International Conference on
Print_ISBN :
0-7803-7293-X
DOI :
10.1109/FUZZ.2001.1009011
