Using colorization to bridge the synthetic-measured gap

This paper explores the use of colorization as a data augmentation and its applications in bridging the synthetic-measured gap. A current problem in Synthetic Aperture Radar Automatic Target Recognition (SAR ATR) is training deep learning networks on largely synthetic data and transferring the knowledge to the measured domain. Data augmentations, such as colorization, can make the deep learning models more robust to the shift in domain when used during training, leading to improved performance over traditional synthetic data. Our approach utilizes a lossless colorization augmentation and applies it to various ResNet-based architectures1 to improve the SAR ATR performance when trained on limited measured data.