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Fuzzy supervised classification of remote sensing images

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1 Author(s)
Wang, F. ; Dept. of Comput. Sci., Waterloo Univ., Ont., Canada

A fuzzy supervised classification method in which geographical information is represented as fuzzy sets is described. The algorithm consists of two major steps: the estimate of fuzzy parameters from fuzzy training data, and a fuzzy 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, resulting in a higher classification accuracy. Results of classifying a Landsat MSS image are presented, and their accuracy is analyzed

Published in:

Geoscience and Remote Sensing, IEEE Transactions on  (Volume:28 ,  Issue: 2 )