  Probabilistic Diffusion Models (PDMs) have recently emerged as a very
promising class of generative models, achieving high performance in natural
image generation. However, their performance relative to non-natural images,
like radar-based satellite data, remains largely unknown. Generating large
amounts of synthetic (and especially labelled) satellite data is crucial to
implement deep-learning approaches for the processing and analysis of
(interferometric) satellite aperture radar data. Here, we leverage PDMs to
generate several radar-based satellite image datasets. We show that PDMs
succeed in generating images with complex and realistic structures, but that
sampling time remains an issue. Indeed, accelerated sampling strategies, which
work well on simple image datasets like MNIST, fail on our radar datasets. We
provide a simple and versatile open-source
https://github.com/thomaskerdreux/PDM_SAR_InSAR_generation to train, sample and
evaluate PDMs using any dataset on a single GPU.

Computer Vision and Pattern Recognition;Machine Learning;Image and Video Processing