Aircraft recognition using modular extreme learning machine

In this paper, a novel recognition scheme is proposed for identifying the aircrafts of different types based on multiple modular neural network classifiers. Three moment invariants including Hu moments, Zernike moments and Wavelet moments are extracted from the characteristics exhibited by aircrafts and used as the input variables of each modular neural network respectively. Each modular neural network consists of multiple single-hidden layer feedforward networks which are trained using the extreme learning machine and different clustering data subsets. A clustering and selection method is used to get the classification rate of each modular neural network and then based on their weighted sum the final classification output is obtained. The proposed recognition scheme is finally evaluated by recognizing six different types of aircraft models and the simulation results show the superiority of the proposed method compared with the single ELM classifier and other classification algorithms.