U-SET: Uncertainty-Aware SAR-to-EO Translation

Minyoung Jeon, Hyun-Ho Kim, Juheon Park, Doochun Seo, Jaehyup Lee

Abstract: Synthetic aperture radar (SAR) imagery has become increasingly vital across diverse applications, including military surveillance, environmental monitoring, and disaster response. However, interpreting SAR images poses challenges for non-experts owing to their distinct imaging characteristics, such as speckle noise and structural distortions. Additionally, inherent properties of SAR imaging and temporal disparities frequently lead to local misalignments between paired SAR and electro-optical (EO) images. To mitigate these issues, we introduce U-SET, an uncertainty-aware framework for SAR-to-EO translation that explicitly models pixel-wise uncertainty, facilitating locally adaptive learning. By leveraging the uncertainty estimation capabilities of deep-learning models, U-SET effectively prioritizes structurally complex and ambiguous regions during training. Comprehensive evaluations on our newly compiled KOMPSAT dataset demonstrate that U-SET achieves state-of-the-art performance, outperforming existing methods across six image quality metrics in both quantitative and qualitative assessments.

External IDs:dblp:journals/spl/JeonKPSL25