  In this work we demonstrate that generative adversarial networks (GANs) can
be used to generate realistic pervasive changes in remote sensing imagery, even
in an unpaired training setting. We investigate some transformation quality
metrics based on deep embedding of the generated and real images which enable
visualization and understanding of the training dynamics of the GAN, and may
provide a useful measure in terms of quantifying how distinguishable the
generated images are from real images. We also identify some artifacts
introduced by the GAN in the generated images, which are likely to contribute
to the differences seen between the real and generated samples in the deep
embedding feature space even in cases where the real and generated samples
appear perceptually similar.
