High resolution remote sensing imagery is used in broad range of tasks, including detection and classification of objects. High-resolution imagery is however expensive, while lower resolution imagery is often freely available and can be used by the public for range of social good applications. To that end, we curate a multi-spectral multi-image super-resolution dataset, using PlanetScope imagery from the SpaceNet 7 challenge as the high resolution reference and multiple Sentinel-2 revisits of the same imagery as the low-resolution imagery. We present the first results of applying multi-image super-resolution (MISR) to multi-spectral remote sensing imagery. We, additionally, introduce a radiometric consistency module into MISR model the to preserve the high radiometric resolution of the Sentinel-2 sensor. We show that MISR is superior to single-image super-resolution and other baselines on a range of image fidelity metrics. Furthermore, we conduct the first assessment of the utility of multi-image super-resolution on building delineation, showing that utilising multiple images results in better performance in these downstream tasks.

翻译：高分辨率遥感图像用于范围广泛的任务,包括探测和分类物体。高分辨率图像费用昂贵,而低分辨率图像通常免费提供,公众可以将低分辨率图像用于各种社会公益应用。为此,我们利用空间网7号挑战中的PlanetScope图像作为高分辨率参考,并使用与低分辨率图像相同的多个Sentinel-2重访。我们介绍了对多光谱遥感图像应用多图像超分辨率(MISR)的第一批结果。我们还在MISR模型中引入了辐射测量一致性模块,以维护Sentinel-2传感器的高辐射度分辨率。我们表明,MISR优于单一图像超分辨率和一系列图像忠实度指标上的其他基线。此外,我们首次评估多图像超分辨率对建筑划界的效用,显示将多种图像用于这些下游任务的更好表现。