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SEGMENTATION OF SATELLITE IMAGES BY MODIFIED MOUNTAIN CLUSTERING

Segmentation of satellite images is an important issue in various applications. Though clustering techniques have been in vogue for many years, they have not been too effective because of several problems such as selection of the number of clusters. This proposed work tackles this problem by having a validity measure coupled with the new clustering technique. This method treats each point in the data set, which is the map of all possible color combinations in the given image, as a potential cluster center and estimates its potential with respect to the other data elements. The point with the maximum value of potential is considered to be a cluster center and then its effect is removed from the other points of the data set. This procedure is repeated to determine the different cluster centers. At the same time we compute the compactness and the minimum separation among all the cluster centers, also the validity function as the ratio of these quantities. The validity function can be used in making a choice of the number of clusters. This technique has been compared to the Fuzzy C-means technique and the results have been shown for a sample color image of satellite data.