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In practice, the classification algorithms and the initialization of the clusters and the initial centroid of clusters have great influence on the st abili y of the algorithms, dealing time and classification results. Some algorithms are used co mm nly in data classification, but their disadvantages are low accuracy and unstability such as k-Means algorithm, c-Means algorithm, Iso-data algorithm. This paper proposes a method of combining fuzzy probability and fuzzy clustering algorithm to overcome these disadvantage s. The method consists of two steps, first to calculate the number of clusters and the centroid o f clusters based fuzzy probability, then to use fuzzy clustering algorithm to land-cover classifica t on. The results showed that, the accuracy of the land cover classification using multispectral s tellite images according to the developed method significantly increases compared with variou s algorithms such as k-Means, Iso-data. Keyword: satellite imagery, probability, fuzzy c-means clus tering.

COMBINING FUZZY PROBABILITY AND FUZZY CLUSTERING FOR MULTISPECTRAL SATELLITE IMAGERY CLASSIFICATION