Abstract—Recently, a novel evolutionary global search strategy called Imperialist Competitive Algorithm (ICA) has proven its superior capabilities in optimization problems. This paper presents an application of ICA in automated clustering of remote sensing images. The proposed algorithm is basically a hierarchical two-phase process. At the first phase the original data set is decomposed into water bodies and land cover classes using near Infrared band’s information. At the second phase, ICA has been applied to determine the number and centers of the land cover clusters using RGB band’s information during an unsupervised clustering. The optimization is based on Fuzzy C-Means and an additional term for improving the accuracy of clustering. The method is applied on pan-sharpened IKONOS images of Tehran and 4 artificial data sets with different properties. Results obtained from applying the proposed method for both artificial data sets and RS image, indicate promising ability of this method in clustering data with unknown cluster number. Also the results show that the achieved overall accuracy can be available, better than 78% in comparison with other applied methods.
Index Terms—Imperialist Competitive Algorithm, Remote Sensing, Hierarchical, Fuzzy C-Means Clustering
S. Karami is with the corresponding author and can be contacted (e-mail: Karami_samaneh@yahoo.com).
S. B. Shokouhi is with the international scientific societies such as IEEE, IEICE and also Iranian Machine Vision and Image Processing (MVIP).
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Cite: S. Karami and Sh.B. Shokouhi, "Application of Imperialist Competitive Algorithm for Automated Classification of Remote Sensing Images,"
International Journal of Computer Theory and Engineering vol. 4, no. 2, pp. 137-143, 2012.
