无需真实高分辨率数据，实现卫星图像超分辨的不确定性估计。
Self-Supervised Uncertainty Estimation For Super-Resolution of Satellite Images
- 基于决策理论设计自监督损失，同时预测图像和不确定性
- 在合成SkySat数据上达到与有监督方法相当的校准不确定性
- 适合需要可靠重建置信度的遥感图像分析场景
卫星图像超分辨因缺乏成对的高低分辨率数据而面临挑战。现有自监督方法利用快速序列观测中的时间冗余克服此限制，但无法量化重建结果的不确定性。本文提出一种新型自监督损失，可在不接触真实高分辨率数据的情况下估计图像超分辨的不确定性。我们从决策理论出发，证明最小化贝叶斯风险可使后验均值和方差成为最优估计器。我们在合成的SkySat L1B数据集上验证了该方法，结果表明其生成的不确定性估计具有良好的校准性，与有监督方法相当。本工作将自监督复原与不确定性量化相结合，构建了一个面向实际应用的不确定性感知图像重建框架。
原文摘要 · Abstract (English)
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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