A Large-Scale Benchmark Data Set for Evaluating Pansharpening Performance: Overview and Implementation

Pansharpening aims to sharpen a lowspatial-resolution (LR) multispectral (MS) image using a high-spatial-resolution (HR) panchromatic (Pan) image to obtain the HR MS image. It has been a fundamental and active research topic in remote sensing, and pansharpening methods have been developed for nearly 40 years. While the performance evaluation of pansharpening methods is still based on a small number of individual images, datadriven pansharpening approaches are attracting increasing attention. However, few publicly available benchmark data sets for pansharpening are available, especially large-scale ones. This has been a serious limitation for the future development of pansharpening methods.

Automated summary

Pansharpening is a fundamental and active research topic in remote sensing that aims to sharpen low-spatial-resolution multispectral images using high-spatial-resolution panchromatic images. While performance evaluation is currently limited to individual images, data-driven approaches are gaining attention. The lack of publicly available benchmark datasets, especially large-scale ones, is a serious limitation for the future development of pansharpening methods.