  Hyperspectral pansharpening consists of fusing a high-resolution panchromatic
band and a low-resolution hyperspectral image to obtain a new image with high
resolution in both the spatial and spectral domains. These remote sensing
products are valuable for a wide range of applications, driving ever growing
research efforts. Nonetheless, results still do not meet application demands.
In part, this comes from the technical complexity of the task: compared to
multispectral pansharpening, many more bands are involved, in a spectral range
only partially covered by the panchromatic component and with overwhelming
noise. However, another major limiting factor is the absence of a comprehensive
framework for the rapid development and accurate evaluation of new methods.
This paper attempts to address this issue.
  We started by designing a dataset large and diverse enough to allow reliable
training (for data-driven methods) and testing of new methods. Then, we
selected a set of state-of-the-art methods, following different approaches,
characterized by promising performance, and reimplemented them in a single
PyTorch framework. Finally, we carried out a critical comparative analysis of
all methods, using the most accredited quality indicators. The analysis
highlights the main limitations of current solutions in terms of
spectral/spatial quality and computational efficiency, and suggests promising
research directions.
  To ensure full reproducibility of the results and support future research,
the framework (including codes, evaluation procedures and links to the dataset)
is shared on https://github.com/matciotola/hyperspectral_pansharpening_toolbox,
as a single Python-based reference benchmark toolbox.
