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.
@article{arxiv.2210.02745,
title = {MuS2: A Real-World Benchmark for Sentinel-2 Multi-Image Super-Resolution},
author = {Pawel Kowaleczko and Tomasz Tarasiewicz and Maciej Ziaja and Daniel Kostrzewa and Jakub Nalepa and Przemyslaw Rokita and Michal Kawulok},
journal= {arXiv preprint arXiv:2210.02745},
year = {2023}
}