Optimal Hierarchical Remote Sensing Image Clustering Using Imperialist Competitive Algorithm
In this paper a novel technique is proposed for automated hierarchical clustering of remote sensing images. Traditionally, in an unsupervised clustering the number of clusters needs to be fixed and usually the RGB values of pixels are used to compute similarity measure for assigning them to specific classes. The proposed algorithm is basically a two-phase process. At the first phase the original data set is decomposed into water and land cover classes using near Infrared band’s information. At the second phase Imperialist Competitive Algorithm is used to determine the number and centers of land cover clusters using RGB information. The optimization is based on K-Means and an additional term for improving the accuracy of clustering. The method is applied on 2 artificial data sets and an IKONOS image of a part of city Tehran. Results obtained from applying this method for both artificial data sets and RS images, indicate promising ability of this method in clustering of images.
KeywordsLand Cover Cluster Center Competitive Algorithm Imperialist Competitive Algorithm Image Cluster
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