Fuzzy Supervised Classification of Remote Sensing Images

Abstruct-In the conventional remote sensing supervised classification, training information and classification results are represented in a one-pixel-one-class method. Class mixture cannot be taken into consideration in training a classifier and in determining pixels’ membership. The expressive limitation has reduced the classification accuracy level and led to the poor extraction of information. This paper describes a fuzzy supervised classification method in which geographical information is represented as fuzzy sets. The algorithm consists of two major steps: The estimate of fuzzy parameters from fuzzy training data, and afuzzy partition of spectral space. Partial membership of pixels allows component cover classes of mixed pixels to be identified and more accurate statistical parameters to be generated, and, in turn, a higher classification accuracy to be achieved. Results of classifying a Landsat MSS image are presented and their accuracy is analyzed. Keywords-Remote sensing, fuzzy set theory, image classification, information representation.