Synthetic Aperture Radar Physics-based Image Randomization for Identification Training - SPIRIT

Accurate classifications of air-to-ground targets of interest is extremely important. Measured data is expensive and difficult to gather for training deep learning networks. By creating synthetic images that can train deep learning networks to classify measured images, the effort and money needed for training deep learning networks for target classification is greatly reduced. This effort addresses a key technical challenge associated with training a deep learning network by augmenting a limited set of measured data with synthetic Synthetic Aperture Radar (SAR) data to train a deep learning network to classify military tactical vehicles. To account for the differences between synthetic and measured SAR data, this effort performs extensive data augmentation using synthetic data to create target and background variability. The goal is to create variability in a physically realistic way so that high classification performance is achieved when training with synthetic data. In addition, architecture modifications are also investigated to assess their contribution to performance.