AIRCRAFT CLASSIFICATION USING IMAGE PROCESSING TECHNIQUES AND ARTIFICIAL NEURAL NETWORKS ADIL GURSEL KARACOR Turkish Air Forces Command Central Facilities Department, 06100, Ankara, Turkey ERDAL TORUN R&D Department of Ministry of National Defense 06100 Ankara, Turkey RASIT ABAY Department of Computer Engineering TUAF Academy, Istanbul 34809, Turkey Identifying the type of an approaching aircraft, should it be a helicopter, a ¯ghter jet or a passenger plane, is an important task in both military and civilian practices. The task in question is normally done by using radar or RF signals. In this study, we suggest an alternative method that introduces the use of a still image instead of RF or radar data. The image was transformed to a binary black and white image, using a Matlab script which utilizes Image Processing Toolbox commands of Matlab, in order to extract the necessary features. The extracted image data of four di®erent types of aircraft was fed into a three-layered feed forward arti¯cial neural network for classi¯cation. Satisfactory results were achieved as the rate of successful classi¯cation turned out to be 97% on average. Keywords : Aircraft classi¯cation; image processing; arti¯cial neural network. 1. Introduction The task of identifying type and model of an approaching aircraft is an important process in military as well as commercial practices. This information could be useful for the pilot of an aircraft who would be anxious to know what is approaching; it should also be useful for the people on the ground (control tower, etc.). This iden- tifying task is normally done by using radar or RF signals. Considering the absence or failure of radar and/or radio devices, recognition from a simple photograph would certainly be helpful. In this study, we suggest an alternative method that introduces the use of a still image instead of RF or radar data. International Journal of Pattern Recognition and Arti¯cial Intelligence Vol. 25, No. 8 (2011) 1321À1335 # . c World Scienti¯c Publishing Company DOI: 10.1142/S0218001411009044 1321