Self-Supervised Uncertainty Estimation For Super-Resolution of Satellite Images

Zhe Zheng; Valéry Dewil; Pablo Arias

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arXiv · 2603.14074

Self-Supervised Uncertainty Estimation For Super-Resolution of Satellite Images

Zhe Zheng·Valéry Dewil·Pablo Arias

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Abstract

Super-resolution (SR) of satellite imagery is challenging due to the lack of paired low-/high-resolution data. Recent self-supervised SR methods overcome this limitation by exploiting the temporal redundancy in burst observations, but they lack a mechanism to quantify uncertainty in the reconstruction. In this work, we introduce a novel self-supervised loss that allows to estimate uncertainty in image super-resolution without ever accessing the ground-truth high-resolution data. We adopt a decision-theoretic perspective and show that minimizing the corresponding Bayesian risk yields the posterior mean and variance as optimal estimators. We validate our approach on a synthetic SkySat L1B dataset and demonstrate that it produces calibrated uncertainty estimates comparable to supervised methods. Our work bridges self-supervised restoration with uncertainty quantification, making a practical framework for uncertainty-aware image reconstruction.

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BibTeX RIS

Zhe Zheng, Valéry Dewil, Pablo Arias. 2026-03-14. Self-Supervised Uncertainty Estimation For Super-Resolution of Satellite Images. https://arxiv.org/abs/2603.14074

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