A Deep Learning SAR Target Classification Experiment on MSTAR Dataset

Abstract

Yet another deep learning method is proposed in this paper for the problem of automated target recognition (ATR) in Synthetic Aperture Radar (SAR) images. Deep convolutional neural networks (CNN) classifiers have been demonstrated on benchmark datasets like MSTAR to outperform classical machine learning algorithms based on formal feature extraction. Most of these deep learning solutions use amplitude information only and reverse the SAR classification problem to a simple image classification task. In this paper we analyze the potential of using additional radar information, such as phase information, in the deep learning process.