MuS2: A Real-World Benchmark for Sentinel-2 Multi-Image Super-Resolution
This addresses the problem of limited spatial resolution in free satellite imagery for remote sensing researchers, though it is incremental as it focuses on benchmarking rather than algorithmic innovation.
The authors tackled the lack of real-world benchmarks for multi-image super-resolution in remote sensing by introducing the MuS2 benchmark, which uses Sentinel-2 and WorldView-2 imagery to provide an end-to-end evaluation procedure.
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.