Automatic Target Recognition in Synthetic Aperture Radar image using multiresolution analysis and classifiers combination

Automatic target recognition (ATR) is an important capability for defense application. ATR removes the human operator from the process of target acquisition and classification, reducing the reaction time to possible threats and can be used to gun target engagement. This paper presents one technique used to solve the automatic target recognition problem in synthetic aperture radars (SAR) images, that is independent of target pose in the images. The classification is performed by a combination of three different classifiers the minimum distance classifier (MDC), the quadratic Gaussian classifier (QGC) and a multilayer perceptron (MLP) neural network, using a voting architecture.