SAMPLE with a Side of MSTAR: Extending SAMPLE with Outliers and Target Variants from MSTAR

Deep learning is a technology applied to a host of problems in the decade since its introduction. Of particular interest for both defense and civil applications is the technology of automatic target recognition, which is a subset of visual detection and classification. However, these classification algorithms must be robust to out-of-library confusers and able to generalize across a variety of target types. In this paper, we augment the existing Synthetic and Measured Paired Labeled Experiment dataset of synthetic aperture radar images with the remainder of the public MSTAR dataset and define a set of experiments to encourage the development of traits beyond simple classification accuracy for target recognition algorithms.