Insufficient image spatial resolution is a serious limitation in many practical scenarios, especially when acquiring images at a finer scale is infeasible or brings higher costs. This is inherent to remote sensing, including Sentinel-2 satellite images that are available free of charge at a high revisit frequency, but whose spatial resolution is limited to 10 m ground sampling distance. The resolution can be increased with super-resolution algorithms, in particular when performed from multiple images captured at subsequent revisits of a satellite, taking advantage of information fusion that leads to enhanced reconstruction accuracy. One of the obstacles in multi-image super-resolution consists in the scarcity of real-world benchmarks - commonly, simulated data are exploited which do not fully reflect the operating conditions. In this paper, we introduce a new MuS2 benchmark for super-resolving multiple Sentinel-2 images, with WorldView-2 imagery used as the high-resolution reference. Within MuS2, we publish the first end-to-end evaluation procedure for this problem which we expect to help the researchers in advancing the state of the art in multi-image super-resolution.
翻译:图像空间分辨率不足在许多实际情景中是一个严重的限制,特别是在获取微幅图像不可行或成本较高的情况下,这是遥感所固有的,包括哨兵2号卫星图像,这些图像在高重访频率下免费提供,但其空间分辨率限于10米的地面取样距离。通过超分辨率算法可以提高分辨率,特别是利用随后在卫星重访时拍摄的多个图像,利用信息聚合,提高重建精确度。多图像超级分辨率中的一个障碍是缺乏现实世界基准,通常,模拟数据被利用,不能充分反映操作条件。在本文件中,我们为超分辨率解多哨兵2号图像引入一个新的 MuS2基准,使用WorldView-2图像作为高分辨率参考。在Mus2中,我们公布了这一问题的第一个端到端评价程序,我们期望该程序有助于研究人员推进多图像超分辨率的艺术状态。