{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T03:13:36Z","timestamp":1771038816025,"version":"3.50.1"},"reference-count":72,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2024,8,16]],"date-time":"2024-08-16T00:00:00Z","timestamp":1723766400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In recent years, deep learning techniques for pansharpening multiresolution images have gained increasing interest. Due to the lack of ground truth data, most deep learning solutions rely on synthetic reduced-resolution data for supervised training. This approach has limitations due to the statistical mismatch between real full-resolution and synthetic reduced-resolution data, which affects the models\u2019 generalization capacity. Consequently, there has been a shift towards unsupervised learning frameworks for pansharpening deep learning-based techniques. Unsupervised schemes require defining sophisticated loss functions with at least two components: one for spectral quality, ensuring consistency between the pansharpened image and the input multispectral component, and another for spatial quality, ensuring consistency between the output and the panchromatic input. Despite promising results, there has been limited investigation into the interaction and balance of these loss terms to ensure stability and accuracy. This work explores how unsupervised spatial and spectral consistency losses can be reliably combined preserving the outocome quality. By examining these interactions, we propose a general rule for balancing the two loss components to enhance the stability and performance of unsupervised pansharpening models. Experiments on three state-of-the-art algorithms using WorldView-3 images demonstrate that methods trained with the proposed framework achieve good performance in terms of visual quality and numerical indexes.<\/jats:p>","DOI":"10.3390\/rs16163014","type":"journal-article","created":{"date-parts":[[2024,8,16]],"date-time":"2024-08-16T12:40:07Z","timestamp":1723812007000},"page":"3014","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["An Unsupervised CNN-Based Pansharpening Framework with Spectral-Spatial Fidelity Balance"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6577-6879","authenticated-orcid":false,"given":"Matteo","family":"Ciotola","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering and Information Technology (DIETI), University Federico II, Via Claudio 21, 80125 Naples, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-7186-4110","authenticated-orcid":false,"given":"Giuseppe","family":"Guarino","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Information Technology (DIETI), University Federico II, Via Claudio 21, 80125 Naples, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6458-9107","authenticated-orcid":false,"given":"Giuseppe","family":"Scarpa","sequence":"additional","affiliation":[{"name":"Department of Engineering, University Parthenope, Centro Direzionale ISOLA C4, 80133 Naples, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1109\/MGRS.2020.3019315","article-title":"A New Benchmark Based on Recent Advances in Multispectral Pansharpening: Revisiting Pansharpening With Classical and Emerging Pansharpening Methods","volume":"9","author":"Vivone","year":"2021","journal-title":"IEEE Geosci. 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