Hyperspectral Pansharpening: Critical Review, Tools and Future Perspectives
This work addresses the need for a standardized benchmark and tools to accelerate research in hyperspectral pansharpening, which is crucial for remote sensing applications, but it is incremental as it builds on existing methods rather than introducing new ones.
The paper tackles the problem of hyperspectral pansharpening, where current methods fail to meet application demands due to technical complexity and lack of a comprehensive evaluation framework, by creating a large dataset, reimplementing state-of-the-art methods in a unified PyTorch toolbox, and conducting a critical comparative analysis that highlights limitations in quality and efficiency.
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.