Fuzzy clustering methods in multispectral satellite image segmentation

Segmentation method for subject processing the multispectral satellite images based on fuzzy clustering and preliminary non-linear filtering is r epresented. Three fuzzy clustering algorithms, namely Fuzzy C-means, GustafsonKessel, and Gath-Geva have been utilized. The experimental results obta ined using these algorithms with and without preliminary nonlinear filtering to segment multispectral Landsat images have approved that segmentation based o n fuzzy clustering provides good-looking discrimination of different land cover types. Implementations of Fuzzy C-means, Gustafson-Kessel, and Gath-Geva a lgorithms have got linear computational complexity depending on initial cluster amoun t and image size for single iteration step. They assume internal parallel imple mentation. The preliminary processing of source channels with nonlinear filter provides more clear cluster discrimination and has as a consequence mo re clear segment outlining.