  Conditional Generative Adversarial Nets (CGANs) need a significantly huge
dataset with a detailed pixel-wise annotation to generate high-quality images.
Unfortunately, any amount of missing pixel annotations may significantly impact
the result not only locally, but also in annotated areas. To the best of our
knowledge, such a challenge has never been investigated in the broader field of
GANs. In this paper, we take the first step in this direction to study the
problem of CGAN-based satellite image synthesis given partially annotated
images. We first define the problem of image synthesis using partially
annotated data, and we discuss a scenario in which we face such a challenge. We
then propose an effective solution called detail augmentation to address this
problem. To do so, we tested two different approaches to augment details to
compensate for missing pixel-wise annotations. In the first approach, we
augmented the original images with their Canny edges to using the CGAN to
compensate for the missing annotations. The second approach, however, attempted
to assign a color to all pixels with missing annotation. Eventually, a
different CGAN was trained to translate the new feature images into a final
output.

Computer Vision and Pattern Recognition