  The performance of neural network models is often limited by the availability
of big data sets. To treat this problem, we survey and develop novel synthetic
data generation and augmentation techniques for enhancing low/zero-sample
learning in satellite imagery. In addition to extending synthetic data
generation approaches, we propose a hierarchical detection approach to improve
the utility of synthetic training samples. We consider existing techniques for
producing synthetic imagery--3D models and neural style transfer--as well as
introducing our own adversarially trained reskinning network, the
GAN-Reskinner, to blend 3D models. Additionally, we test the value of synthetic
data in a two-stage, hierarchical detection/classification model of our own
construction. To test the effectiveness of synthetic imagery, we employ it in
the training of detection models and our two stage model, and evaluate the
resulting models on real satellite images. All modalities of synthetic data are
tested extensively on practical, geospatial analysis problems. Our experiments
show that synthetic data developed using our approach can often enhance
detection performance, particularly when combined with some real training
images. When the only source of data is synthetic, our GAN-Reskinner often
boosts performance over conventionally rendered 3D models and in all cases the
hierarchical model outperforms the baseline end-to-end detection architecture.
