Satellite imaging generally presents a trade-off between the frequency of acquisitions and the spatial resolution of the images. Super-resolution is often advanced as a way to get the best of both worlds. In this work, we investigate multi-image super-resolution of satellite image time series, i.e. how multiple images of the same area acquired at different dates can help reconstruct a higher resolution observation. In particular, we extend state-of-the-art deep single and multi-image super-resolution algorithms, such as SRDiff and HighRes-net, to deal with irregularly sampled Sentinel-2 time series. We introduce BreizhSR, a new dataset for 4x super-resolution of Sentinel-2 time series using very high-resolution SPOT-6 imagery of Brittany, a French region. We show that using multiple images significantly improves super-resolution performance, and that a well-designed temporal positional encoding allows us to perform super-resolution for different times of the series. In addition, we observe a trade-off between spectral fidelity and perceptual quality of the reconstructed HR images, questioning future directions for super-resolution of Earth Observation data.

翻译：卫星成像通常面临采集频率与图像空间分辨率之间的权衡。超分辨率技术常被视为实现两者兼得的方法。本研究探讨了卫星图像时间序列的多图像超分辨率问题，即如何利用同一区域在不同日期获取的多幅图像重建更高分辨率的观测结果。具体而言，我们扩展了当前最先进的深度单图像与多图像超分辨率算法（如SRDiff和HighRes-net），使其能够处理不规则采样的Sentinel-2时间序列。我们引入BreizhSR数据集，该数据集基于法国布列塔尼地区超高分辨率SPOT-6影像，专门用于Sentinel-2时间序列的4倍超分辨率重建。研究表明，使用多幅图像可显著提升超分辨率性能，而设计良好的时间位置编码则能实现对序列中不同时间点的超分辨率重建。此外，我们观察到重建高分辨率图像的光谱保真度与感知质量之间存在权衡，这为地球观测数据超分辨率技术的未来发展方向提出了新的思考。