A Mahalanobis distance fuzzy classifier

Author

Deer, P.J. ; Eklund, P.W. ; Norman, B.D.

Author_Institution

Div. of Inf. Technol., Defence Sci. & Technol. Organ., Salisbury, SA, Australia

fYear

1996

fDate

18-20 Nov 1996

Firstpage

220

Lastpage

223

Abstract

Traditional `hard´ classification techniques are inappropriate for classifying remotely sensed imagery. Class `boundaries´ in the natural environment are not distinct and a single pixel may exhibit spectral characteristics related to a number of classes. Fuzzy set theory was introduced to address the issue of the `vagueness´ of class or set membership. An unsupervised approach to fuzzy classification uses the fuzzy c-means algorithm. The paper reports on a related supervised approach in which training sets are selected, then the fuzzy class memberships are determined by the reciprocal of the Mahalanobis distance from these training class means

Keywords

fuzzy set theory; image classification; learning (artificial intelligence); remote sensing; Mahalanobis distance fuzzy classifier; class boundaries; class vagueness; fuzzy c-means algorithm; fuzzy class memberships; fuzzy set theory; natural environment; pixel; remotely sensed imagery classification; set membership vagueness; spectral characteristics; supervised fuzzy classification; training class means; training set selection; Classification algorithms; Clouds; Computer science; Fuzzy set theory; Fuzzy sets; Image classification; Image processing; Information technology; Pixel; Remote sensing;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Information Systems, 1996., Australian and New Zealand Conference on

Conference_Location

Adelaide, SA

Print_ISBN

0-7803-3667-4

Type

conf

DOI

10.1109/ANZIIS.1996.573940

Filename

573940