  Acquisition of Synthetic Aperture Sonar (SAS) datasets is bottlenecked by the
costly deployment of SAS imaging systems, and even when data acquisition is
possible,the data is often skewed towards containing barren seafloor rather
than objects of interest. We present a novel pipeline, called SAS GAN, which
couples an optical renderer with a generative adversarial network (GAN) to
synthesize realistic SAS images of targets on the seafloor. This coupling
enables high levels of SAS image realism while enabling control over image
geometry and parameters. We demonstrate qualitative results by presenting
examples of images created with our pipeline. We also present quantitative
results through the use of t-SNE and the Fr\'echet Inception Distance to argue
that our generated SAS imagery potentially augments SAS datasets more
effectively than an off-the-shelf GAN.
