SAR Target Recognition with Deep Learning

The automated detection and classification of objects in imagery is an important topic for many applications in remote sensing. These can include the counting of cars and ships and the tracking of military vehicles for the defense and intelligence industry. Synthetic aperture radar (SAR) provides day/night and all-weather imaging capabilities. SAR is a powerful data source for Deep Learning (DL) algorithms to provide automatic target recognition (ATR) capabilities. DL classification was shown to be extremely effective on multi-spectral satellite imagery during the IARPA Functional Map of the World (fMoW). In our work we look to extend these techniques to SAR. We start by applying ResNet-18 to the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset. The MSTAR program, sponsored by DARPA and AFRL, consists of SAR collections of military style targets using an aerial X-band radar with one-foot resolution. We achieved an overall classification accuracy of 99% on 10 different classes of targets, confirming previously published results. We then extend this classifier to investigate an emerging target and the effects of limited training data on system performance.