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.

翻译：概率扩散模型（Probabilistic Diffusion Models, PDMs）近期作为一类极具前景的生成模型崭露头角，在自然图像生成中取得了优异性能。然而，它们在非自然图像（如基于雷达的卫星数据）上的表现仍 largely 未知。生成大量合成（尤其是带标签）卫星数据对于实现基于深度学习的（干涉）合成孔径雷达数据处理与分析至关重要。本文利用PDMs生成了多个基于雷达的卫星图像数据集。我们证明PDMs能够成功生成具有复杂且逼真结构的图像，但采样时间仍是一个问题。具体而言，在MNIST等简单图像数据集上效果良好的加速采样策略，在我们的雷达数据集上失效。我们提供了一个简单且通用的开源工具 https://github.com/thomaskerdreux/PDM_SAR_InSAR_generation，支持在单个GPU上使用任意数据集训练、采样和评估PDMs。