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Nowadays, hyper-spectral remote sensing imaging systems are able to acquire several hundreds of spectral bands. Increasing spectral bands provide the more information for land cover and separate similarity classes and classification accuracy potentially could increase. Nevertheless classification of hyper-spectral imagery by conventional classifiers suffers from Hughes phenomenon. Namely, by increasing spectral bands, for a fixed number of training samples, classification accuracy is reduced. One of the solutions for overcoming the mentioned problem is reducing the dimension of input space based on feature selection techniques. Traditional feature selection techniques have several limitations in performance and finding the global optimum subset selection of feature in hyper-spectral images. In this paper a novel feature selection algorithms based on an Ant Colony Optimization (ACO) presents. ACO techniques are based on the behavior of real ant colonies. Evaluating of obtained results from classification accuracy of AVIRIS image data set shows effectiveness of this algorithm as it achieves fewer features and higher classification accuracy rather than other non-parametric optimization methods such as Genetic Algorithm.