  In recent years, diffusion models (DMs) have become a popular method for
generating synthetic data. By achieving samples of higher quality, they quickly
became superior to generative adversarial networks (GANs) and the current
state-of-the-art method in generative modeling. However, their potential has
not yet been exploited in radar, where the lack of available training data is a
long-standing problem. In this work, a specific type of DMs, namely denoising
diffusion probabilistic model (DDPM) is adapted to the SAR domain. We
investigate the network choice and specific diffusion parameters for
conditional and unconditional SAR image generation. In our experiments, we show
that DDPM qualitatively and quantitatively outperforms state-of-the-art
GAN-based methods for SAR image generation. Finally, we show that DDPM profits
from pretraining on largescale clutter data, generating SAR images of even
higher quality.
