Multispectral Satellite Image Segmentation Using Fuzzy Clustering and Nonlinear Filtering Methods

Segmentation method for processing the multispectral satellite images based on fuzzy clustering and nonlinear filtering is represented. Three fuzzy clustering algorithms, namely Fuzzy C-means, Gustafson-Kessel, and Gath-Gevawith and without preliminary processing have been tested. The experimental results obtained using these algorithms with and without preliminary nonlinear filtering the source Landsat channels have approved that segmentation based on fuzzy clustering provides good-looking discrimination of different land cover types. The preliminary processing of source channels with nonlinear filter provides more clear cluster discrimination and has as a consequence more clear segment outlining.