High-resolution satellite imagery is a key element for many Earth monitoring applications. Satellites such as Sentinel-2 feature characteristics that are favorable for super-resolution algorithms such as aliasing and band-misalignment. Unfortunately the lack of reliable high-resolution (HR) ground truth limits the application of deep learning methods to this task. In this work we propose L1BSR, a deep learning-based method for single-image super-resolution and band alignment of Sentinel-2 L1B 10m bands. The method is trained with self-supervision directly on real L1B data by leveraging overlapping areas in L1B images produced by adjacent CMOS detectors, thus not requiring HR ground truth. Our self-supervised loss is designed to enforce the super-resolved output image to have all the bands correctly aligned. This is achieved via a novel cross-spectral registration network (CSR) which computes an optical flow between images of different spectral bands. The CSR network is also trained with self-supervision using an Anchor-Consistency loss, which we also introduce in this work. We demonstrate the performance of the proposed approach on synthetic and real L1B data, where we show that it obtains comparable results to supervised methods.

翻译：高分辨率卫星影像对于诸多地球监测应用至关重要。哨兵二号等卫星具备混叠与波段错位等有利于超分辨率算法的特性。然而，可靠高分辨率（HR）地面真值的缺乏限制了深度学习方法在该任务中的应用。本文提出L1BSR——一种基于深度学习的单幅图像超分辨率与波段对齐方法，专用于哨兵二号L1B 10米波段影像。该方法通过利用相邻CMOS探测器产生的L1B图像中的重叠区域，直接在真实L1B数据上以自监督方式进行训练，无需高分辨率地面真值。我们的自监督损失函数旨在强制超分辨率输出图像的所有波段实现正确对齐，这一目标通过创新性的跨光谱配准网络（CSR）实现，该网络可计算不同光谱波段图像间的光流。CSR网络同样采用自监督方式训练，使用本文提出的锚点一致性损失函数。我们在合成数据与真实L1B数据上验证了所提方法的性能，结果表明其可获得与监督方法相当的结果。