Abstract
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.
Abstract (translated)
卫星成像通常在采集频率和图像空间分辨率之间存在权衡。超分辨率通常通过实现两者之间的平衡来解决这一问题。在这项工作中，我们研究了卫星图像时间序列的多图像超分辨率，即不同日期获取的同一区域的多张图像如何帮助重建更高分辨率的观测。 特别是，我们将先进的深度单图像和多图像超分辨率算法，如SRDiff和HighRes-net，扩展到处理采样不规则的Sentinel-2时间序列。我们引入了BreizhSR，一个使用非常高分辨率的神话2图像的4x超分辨率数据集，该数据集来自法国的一个地区。我们证明了使用多张图像 significantly 改善了超分辨率性能，并且通过设计良好的时间序列的定位编码，我们能够为不同时间序列进行超分辨率。 此外，我们观察到复像分辨率和图像质量之间存在权衡，这引发了关于地球观测数据超分辨率未来方向的思考。
URL
https://arxiv.org/abs/2404.16409
