An improved algorithm for supervised fuzzyC-means clustering of remotely sensed data

Abstract

This paper describes an improved algorithm for fuzzyc-means clustering of remotely sensed data, by which the degree of fuzziness of the resultant classification is decreased as comparing with that by a conventional algorithm: that is, the classification accuracy is increased. This is achieved by incorporating covariance matrices at the level of individual classes rather than assuming a global one. Empirical results from a fuzzy classification of an Edinburgh suburban land cover confirmed the improved performance of the new algorithm for fuzzyc-means clustering, in particular when fuzziness is also accommodated in the assumed reference data.