A Mahalanobis distance fuzzy classifier

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.