{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T07:13:03Z","timestamp":1772349183734,"version":"3.50.1"},"reference-count":57,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2024,6,8]],"date-time":"2024-06-08T00:00:00Z","timestamp":1717804800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"project PIGNOLETTO\u2014Call HUB Ricerca e Innovazione","award":["E41B20000050007"],"award-info":[{"award-number":["E41B20000050007"]}]},{"name":"project PIGNOLETTO\u2014Call HUB Ricerca e Innovazione","award":["POR FESR 2014-2020"],"award-info":[{"award-number":["POR FESR 2014-2020"]}]},{"name":"Programma Operativo Regionale, Fondo Europeo di Sviluppo Regionale\u2014Regional Operational Programme, European Regional Development Fund","award":["E41B20000050007"],"award-info":[{"award-number":["E41B20000050007"]}]},{"name":"Programma Operativo Regionale, Fondo Europeo di Sviluppo Regionale\u2014Regional Operational Programme, European Regional Development Fund","award":["POR FESR 2014-2020"],"award-info":[{"award-number":["POR FESR 2014-2020"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Hyperspectral pansharpening is crucial for the improvement of the usability of images in various applications. However, it remains underexplored due to a scarcity of data. The primary goal of pansharpening is to enhance the spatial resolution of hyperspectral images by reconstructing missing spectral information without compromising consistency with the original data. This paper addresses the data gap by presenting a new hyperspectral dataset specifically designed for pansharpening and the evaluation of several deep learning strategies using this dataset. The new dataset has two crucial features that make it invaluable for deep learning hyperspectral pansharpening research. (1) It presents the highest cardinality of images in the state of the art, making it the first statistically relevant dataset for hyperspectral pansharpening evaluation, and (2) it includes a wide variety of scenes, ensuring robust generalization capabilities for various approaches. The data, collected by the ASI PRISMA satellite, cover about 262,200 km2 and their heterogeneity is ensured by a random sampling of the Earth\u2019s surface. The analysis of the deep learning methods consists in the adaptation of these approaches to the PRISMA hyperspectral data and the quantitative and qualitative evaluation of their performance in this new scenario. The investigation included two settings: Reduced Resolution (RR) to evaluate the techniques in a controlled environment and Full Resolution (FR) for a real-world evaluation. In addition, for the sake of completeness, we have also included machine-learning-free approaches in both scenarios. Our comprehensive analysis reveals that data-driven neural network methods significantly outperform traditional approaches, demonstrating a superior adaptability and performance in hyperspectral pansharpening under both RR and FR protocols.<\/jats:p>","DOI":"10.3390\/rs16122079","type":"journal-article","created":{"date-parts":[[2024,6,10]],"date-time":"2024-06-10T08:49:03Z","timestamp":1718009343000},"page":"2079","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Deep Learning Hyperspectral Pansharpening on Large-Scale PRISMA Dataset"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8505-1581","authenticated-orcid":false,"given":"Simone","family":"Zini","sequence":"first","affiliation":[{"name":"Department of Informatics, Systems and Communication, University of Milano-Bicocca, 20126 Milan, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3967-8957","authenticated-orcid":false,"given":"Mirko Paolo","family":"Barbato","sequence":"additional","affiliation":[{"name":"Department of Informatics, Systems and Communication, University of Milano-Bicocca, 20126 Milan, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7432-4284","authenticated-orcid":false,"given":"Flavio","family":"Piccoli","sequence":"additional","affiliation":[{"name":"Department of Informatics, Systems and Communication, University of Milano-Bicocca, 20126 Milan, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9112-0574","authenticated-orcid":false,"given":"Paolo","family":"Napoletano","sequence":"additional","affiliation":[{"name":"Department of Informatics, Systems and Communication, University of Milano-Bicocca, 20126 Milan, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"256","DOI":"10.1016\/j.inffus.2020.07.004","article-title":"Machine learning information fusion in Earth observation: A comprehensive review of methods, applications and data sources","volume":"63","author":"Ghamisi","year":"2020","journal-title":"Inf. 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