In the present paper, an efficient method for three dimensional aircraft pattern recognition is introduced. In this method, a set of simple area based features extracted from silhouette of aerial vehicles are used to recognize an aircraft type from its optical or infrared images taken by a CCD camera or a FLIR sensor. These images can be taken from any direction and distance relative to the flying aircraft. A multilayer perceptron neural network has been used for the purpose of aircraft classification. The network training has been carried out using a library of images generated by a 3D model of each aircraft. The neural network is successfully trained and used to recognize and classify arbitrary real aircraft images. The results show more than 90% accuracy in ideal conditions and very good robustness in the presence of noise.
saghafi, F., Khansari Zadeh, S. (2629). Aircraft Visual Identification by Neural Networks. Journal of Aerospace Science and Technology, 5(3), 123-128.
MLA
fariborz saghafi; Seyed Mohammad Khansari Zadeh. "Aircraft Visual Identification by Neural Networks". Journal of Aerospace Science and Technology, 5, 3, 2629, 123-128.
HARVARD
saghafi, F., Khansari Zadeh, S. (2629). 'Aircraft Visual Identification by Neural Networks', Journal of Aerospace Science and Technology, 5(3), pp. 123-128.
VANCOUVER
saghafi, F., Khansari Zadeh, S. Aircraft Visual Identification by Neural Networks. Journal of Aerospace Science and Technology, 2629; 5(3): 123-128.
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