A Benchmarking Protocol for Pansharpening: Dataset, Preprocessing, and Quality Assessment
Gemine Vivone, Mauro Dalla Mura, Andrea Garzelli, Fabio Pacifici
Abstract
Comparative evaluation is a requirement for reproducible science and objective assessment of new algorithms. Reproducible research in the field of pansharpening of very high resolution images is a difficult task due to the lack of openly available reference datasets and protocols. The contribution of this article is threefold, and it defines a benchmarking framework to evaluate pansharpening algorithms. First, it establishes a reference dataset, named PAirMax, composed of 14 panchromatic and multispectral image pairs collected over heterogeneous landscapes by different satellites. Second, it standardizes various image preprocessing steps, such as filtering, upsampling, and band coregistration, by providing a reference implementation. Third, it details the quality assessment protocols for reproducible algorithm evaluation.