无需真实高分辨率数据，实现哨兵5号高光谱图像的自监督超分辨。
Self-Supervised Super-Resolution for Sentinel-5P Hyperspectral Images
- 基于SURE与等变成像约束，利用卫星退化模型和信噪比信息进行自监督训练。
- 在合成与真实数据上均达到与监督方法相当的分辨率提升效果。
- 适合大气遥感、环境监测等领域，尤其适用于无真值数据场景。
哨兵5号（Sentinel-5P，S5P）在大气监测中具有关键作用，但其空间分辨率限制了细粒度分析。现有超分辨率（SR）方法依赖于合成低分辨率（LR）数据的监督学习，因真实高分辨率（HR）数据不可得，限制了其在真实观测中的适用性。本文提出一种针对S5P的自监督高光谱超分辨框架，可在无HR真值条件下训练。该方法结合斯坦无偏风险估计（SURE）与等变成像约束，引入基于信噪比（SNR）元数据推导的S5P退化算子和噪声统计。同时设计了深度可分离卷积U-Net架构，兼顾效率与光谱保真度。在两种设置下评估：(i) LR-HR，使用合成LR数据与监督方法直接对比；(ii) GT-SHR，超分辨图像超越原始空间分辨率且无HR参考。多波段结果表明，自监督模型性能接近监督方法，保持强一致性。定性分析显示空间细节优于双三次插值，与EMIT数据验证确认重建结构具备物理合理性。代码已开源。
原文摘要 · Abstract (English)
Sentinel-5P (S5P) plays a critical role in atmospheric monitoring; however, its spatial resolution limits fine-scale analysis. Existing super-resolution (SR) approaches rely on supervised learning with synthetic low-resolution (LR) data, since true high-resolution (HR) data do not exist, limiting their applicability to real observations. We propose a self-supervised hyperspectral SR framework for S5P that enables training without HR ground truth. The method combines Stein's Unbiased Risk Estimator (SURE) with an equivariant imaging constraint, incorporating the S5P degradation operator and noise statistics derived from signal-to-noise ratio (SNR) metadata. We also introduce depthwise separable convolution U-Net architectures designed for efficiency and spectral fidelity. The framework is evaluated in two settings: (i) LR-HR, where synthetic LR data are used for direct comparison with supervised learning, and (ii) GT-SHR, where super-resolved images surpass the native spatial resolution without HR reference. Results across multiple bands show that self-supervised models achieve performance comparable to supervised methods while maintaining strong consistency. Qualitative analysis shows improved spatial detail over bicubic interpolation, and validation with EMIT data confirms that reconstructed structures are physically meaningful. Code is available at https://github.com/hyamomar/Sentinel-5P-Super-Resolution/tree/main/self_supervised
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