Abstract
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.
Keywords
Publication details
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- Open access
- Green open access
Cite this article
APA 7
Enescu, V., Zeghina, A., Meignin, M., Viltard, N., & Mallet, C. (2026). Unsupervised Domain Adaptation for Enhanced Radiometer Image Precipitation Estimation using Conditional Flow Matching. https://omanscience.com/en/articles/unsupervised-domain-adaptation-for-enhanced-radiometer-image-precipitation-estimation-using-conditional-flow-matching
MLA 9
Enescu, Victor, et al. "Unsupervised Domain Adaptation for Enhanced Radiometer Image Precipitation Estimation using Conditional Flow Matching." https://omanscience.com/en/articles/unsupervised-domain-adaptation-for-enhanced-radiometer-image-precipitation-estimation-using-conditional-flow-matching.
Chicago (author–date)
Enescu, Victor, Assaad Zeghina, Matthieu Meignin, Nicolas Viltard, and Cécile Mallet. 2026. "Unsupervised Domain Adaptation for Enhanced Radiometer Image Precipitation Estimation using Conditional Flow Matching." https://omanscience.com/en/articles/unsupervised-domain-adaptation-for-enhanced-radiometer-image-precipitation-estimation-using-conditional-flow-matching.
Harvard
Enescu, V., Zeghina, A., Meignin, M., Viltard, N. and Mallet, C. (2026) 'Unsupervised Domain Adaptation for Enhanced Radiometer Image Precipitation Estimation using Conditional Flow Matching', Available at: https://omanscience.com/en/articles/unsupervised-domain-adaptation-for-enhanced-radiometer-image-precipitation-estimation-using-conditional-flow-matching.
Vancouver
Enescu V, Zeghina A, Meignin M, Viltard N, Mallet C. Unsupervised Domain Adaptation for Enhanced Radiometer Image Precipitation Estimation using Conditional Flow Matching. https://omanscience.com/en/articles/unsupervised-domain-adaptation-for-enhanced-radiometer-image-precipitation-estimation-using-conditional-flow-matching
IEEE
V. Enescu, A. Zeghina, M. Meignin, N. Viltard, and C. Mallet, "Unsupervised Domain Adaptation for Enhanced Radiometer Image Precipitation Estimation using Conditional Flow Matching," https://omanscience.com/en/articles/unsupervised-domain-adaptation-for-enhanced-radiometer-image-precipitation-estimation-using-conditional-flow-matching.