TITLE: Deep Learning Hyperspectral Pansharpening on large scale PRISMA dataset 

ABSTRACT: In this work, we assess several deep learning strategies for hyperspectral
pansharpening. First, we present a new dataset with a greater extent than any
other in the state of the art. This dataset, collected using the ASI PRISMA
satellite, covers about 262200 km2, and its heterogeneity is granted by
randomly sampling the Earth's soil. Second, we adapted several state of the art
approaches based on deep learning to fit PRISMA hyperspectral data and then
assessed, quantitatively and qualitatively, the performance in this new
scenario. The investigation has included two settings: Reduced Resolution (RR)
to evaluate the techniques in a supervised environment and Full Resolution (FR)
for a real-world evaluation. The main purpose is the evaluation of the
reconstruction fidelity of the considered methods. In both scenarios, for the
sake of completeness, we also included machine-learning-free approaches. From
this extensive analysis has emerged that data-driven neural network methods
outperform machine-learning-free approaches and adapt better to the task of
hyperspectral pansharpening, both in RR and FR protocols.
In this work, we assess several deep learning strategies for hyperspectral
pansharpening. First, we present a new dataset with a greater extent than any
other in the state of the art. This dataset, collected using the ASI PRISMA
satellite, covers about 262200 km2, and its heterogeneity is granted by
randomly sampling the Earth's soil. Second, we adapted several state of the art
approaches based on deep learning to fit PRISMA hyperspectral data and then
assessed, quantitatively and qualitatively, the performance in this new
scenario. The investigation has included two settings: Reduced Resolution (RR)
to evaluate the techniques in a supervised environment and Full Resolution (FR)
for a real-world evaluation. The main purpose is the evaluation of the
reconstruction fidelity of the considered methods. In both scenarios, for the
sake of completeness, we also included machine-learning-free approaches. From
this extensive analysis has emerged that data-driven neural network methods
outperform machine-learning-free approaches and adapt better to the task of
hyperspectral pansharpening, both in RR and FR protocols.
Deep Learning Hyperspectral Pansharpening on large scale PRISMA dataset
http://arxiv.org/abs/2307.11666v2