Generation of SAR Images Using Deep Learning

Synthetic aperture radars (SAR) are a type of high-resolution radars that are used for terrestrial and airborne imaging. They use a moving source to simulate a large antenna aperture, thereby providing a high-quality map of the surroundings. They are popular due to their wide variety of applications in airborne naval and defense systems as well as topography and seismology. In defense applications, they are mainly used for reconnaissance and mapping of enemy targets such as armored vehicles, tanks, and runways. In recent times, there has been a heavy need for machine learning algorithms that are capable of automatically recognizing the targets detected by the SAR without human input. Such algorithms demand large-scale labeled datasets for training, validation and testing. Due to the nature of the SAR, acquisition and collection of these labeled datasets is quite a tedious and expensive task requiring a lot of man hours. Thus, alternate methods to produce new SAR image data are needed. In this paper, generative adversarial networks (GAN) are used to generate new spotlight SAR images from a limited pre-existing dataset. To quantitatively verify the effectiveness of the images produced by the GAN, a novel convolutional neural network (CNN) is devised and trained on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset images.