Comparative analysis of convolutional neural networks and support vector machines for automatic target recognition

Nowadays automatic methods based on artificial intelligence are rapidly growing. In the paper, a problem of automatic target recognition in synthetic aperture radar images is described. It is demonstrated, that two different machine learning instruments can provide very high classification accuracy. In particular, support vector machines with proper optimization and developed local feature set gives competitive results. Secondly, a novel architecture of convolutional neural network is proposed. Important practical aspects of both methods are analyzed. Experimental results for MSTAR are given.