Application of Imperialist Competitive Algorithm for Automated Classification of Remote Sensing Images
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.