A Comprehensive Benchmarking Framework for Sentinel-2 Sharpening: Methods, Dataset, and Evaluation Metrics

The advancement of super-resolution and sharpening algorithms for satellite images has significantly expanded the potential applications of remote sensing data. In the case of Sentinel-2, despite significant progress, the lack of standardized datasets and evaluation protocols has made it difficult to fairly compare existing methods and advance the state of the art. This work introduces a comprehensive benchmarking framework for Sentinel-2 sharpening, designed to address these challenges and foster future research. It analyzes several state-of-the-art sharpening algorithms, selecting representative methods ranging from traditional pansharpening to ad hoc model-based optimization and deep learning approaches. All selected methods have been re-implemented within a consistent Python-based (Version 3.10) framework and evaluated on a suitably designed, large-scale Sentinel-2 dataset. This dataset features diverse geographical regions, land cover types, and acquisition conditions, ensuring robust training and testing scenarios. The performance of the sharpening methods is assessed using both reference-based and no-reference quality indexes, highlighting strengths, limitations, and open challenges of current state-of-the-art algorithms. The proposed framework, dataset, and evaluation protocols are openly shared with the research community to promote collaboration and reproducibility.