发表机构
LATMOS/IPSL, UVSQ Université Paris-Saclay, Sorbonne Université, CNRS; Inria Paris（LATMOS/IPSL，UVSQ巴黎-萨克雷大学，索邦大学，法国国家科学研究中心; 法国国家信息与自动化研究所巴黎中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种基于条件流匹配的无监督域自适应方法，利用流匹配模型中的确定性常微分方程实现卫星辐射计图像的域对齐，并在GPM-Core星座上验证了其提升降水估计的效果。
AI 中文摘要
深度生成网络最近在利用复杂的文本提示进行精确图像和视频编辑方面取得了前所未有的性能。然而，此类模型的有效性在很大程度上依赖于访问非常大的监督和标注图像数据集，而这些数据集可能非常难以获得。对于卫星仪器来说尤其如此，它们很少与标注数据重叠，并且在罕见的重叠情况下也会遭受域偏移。在本文中，我们研究了流匹配模型在卫星辐射计图像无监督域自适应中的潜力。我们的主要贡献是一种新颖的无监督方法，该方法通过利用流匹配模型中基于不同卫星仪器条件化的确定性常微分方程的部分来实现精确的域对齐。我们方法的一个关键优势是，由于扰动在理论上是双射的，它能够在适应任何域的同时保留基本信息。在GPM-Core星座上进行的大量实验表明，我们的条件域自适应的优势，特别是在改善来自辐射计图像的降水估计方面。
英文摘要
Deep generative networks have recently achieved unprecedented performance in precise image and video editing using sophisticated textual prompts. However, the effectiveness of such models heavily depends on access to very large supervised and annotated image datasets, which can be very difficult to obtain. This is particularly true for satellite instruments, which very rarely overlap with labelled data, and suffer from domain shifts in the rare occasions they do. In this paper, we investigate the potential of flow matching models for unsupervised domain adaptation of satellite radiometer images. Our main contribution is a novel unsupervised method that achieves precise domain alignment by leveraging parts of the deterministic ordinary differential equations in flow matching models, conditioned on different satellite instruments. A key strength of our approach is its ability to preserve essential information while adapting across any domains since the perturbations are in theory bijective. Extensive experiments conducted on the GPM-Core constellation show the benefit of our conditional domain adaptation, particularly in improving rain precipitation estimation from radiometer imagery.
