{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T19:06:30Z","timestamp":1770750390052,"version":"3.50.0"},"reference-count":72,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2023,8,15]],"date-time":"2023-08-15T00:00:00Z","timestamp":1692057600000},"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>Image resizing (IR) has a crucial role in remote sensing (RS), since an image\u2019s level of detail depends on the spatial resolution of the acquisition sensor; its design limitations; and other factors such as (a) the weather conditions, (b) the lighting, and (c) the distance between the satellite platform and the ground targets. In this paper, we assessed some recent IR methods for RS applications (RSAs) by proposing a useful open framework to study, develop, and compare them. The proposed framework could manage any kind of color image and was instantiated as a Matlab package made freely available on Github. Here, we employed it to perform extensive experiments across multiple public RS image datasets and two new datasets included in the framework to evaluate, qualitatively and quantitatively, the performance of each method in terms of image quality and statistical measures.<\/jats:p>","DOI":"10.3390\/rs15164039","type":"journal-article","created":{"date-parts":[[2023,8,15]],"date-time":"2023-08-15T11:09:44Z","timestamp":1692097784000},"page":"4039","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["An Open Image Resizing Framework for Remote Sensing Applications and Beyond"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9446-4452","authenticated-orcid":false,"given":"Donatella","family":"Occorsio","sequence":"first","affiliation":[{"name":"Department of Mathematics and Computer Science, University of Basilicata, Viale dell\u2019Ateneo Lucano 10, 85100 Potenza, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6044-5237","authenticated-orcid":false,"given":"Giuliana","family":"Ramella","sequence":"additional","affiliation":[{"name":"National Research Council (C.N.R.), Institute for Applied Computing \u201cMauro Picone\u201d, Via P. Castellino, 111, 80131 Naples, Italy"}]},{"given":"Woula","family":"Themistoclakis","sequence":"additional","affiliation":[{"name":"National Research Council (C.N.R.), Institute for Applied Computing \u201cMauro Picone\u201d, Via P. Castellino, 111, 80131 Naples, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Richards, J.A. (1999). Remote Sensing Digital Image Analysis, Springer.","DOI":"10.1007\/978-3-662-03978-6"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"128791","DOI":"10.1016\/j.jhydrol.2022.128791","article-title":"Naive Bayes classification-based surface water gap-filling from partially contaminated optical remote sensing image","volume":"616","author":"Bai","year":"2023","journal-title":"J. Hydrol."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Massi, A., Ortolani, M., Vitulano, D., Bruni, V., and Mazzanti, P. (2023). 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