Novel view synthesis with compressed sensing as data augmentation for SAR ATR

Abstract

This work investigates the application of compressed sensing algorithms to the problem of novel view synthesis in synthetic aperture radar (SAR). We demonstrate the ability to generate new images of a SAR target from a sparse set of looks at said target, and we show that this can be used as a data augmentation technique for deep learning-based automatic target recognition (ATR). The newly synthesized views can be used both to enlarge the original, sparse training set, and in transfer learning as a source dataset for initial training of the network. The success of the approach is quantified by measuring ATR performance on the MSTAR dataset.