  Recent advances in Generative Adversarial Learning allow for new modalities
of image super-resolution by learning low to high resolution mappings. In this
paper we present our work using Generative Adversarial Networks (GANs) with
applications to overhead and satellite imagery. We have experimented with
several state-of-the-art architectures. We propose a GAN-based architecture
using densely connected convolutional neural networks (DenseNets) to be able to
super-resolve overhead imagery with a factor of up to 8x. We have also
investigated resolution limits of these networks. We report results on several
publicly available datasets, including SpaceNet data and IARPA Multi-View
Stereo Challenge, and compare performance with other state-of-the-art
architectures.
