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

Abstract

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.

A combination of the ICA-ANN model to predict air-overpressure resulting from blasting

“…In the study conducted by Nazari-Shirkouhi et al [52], ICA was used to solve the integrated product mix-outsourcing optimization problem. Application of ICA algorithm in automated clustering of remote sensing images was presented by Karami and Shokouhi [53]. In addition, this algorithm was applied to determine the optimum weights and biases used in ANNs [22,23,54].…”

Interaction Enhanced Imperialist Competitive Algorithms

“…Imperialist Competitive Algorithm (ICA) is a new evolutionary algorithm based on the meta-heuristic of human's socio-political evolution. It was proposed by Atashpaz-Gargari and Lucas in 2007 [10], and since its inception, it has been applied to solve many optimization problems in scheduling [11][12][13][14][15], classification [16,17] and machinery design [18][19][20][21][22][23][24][25].…”