{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T01:48:51Z","timestamp":1776131331095,"version":"3.50.1"},"reference-count":27,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2021,10,10]],"date-time":"2021-10-10T00:00:00Z","timestamp":1633824000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Canada Research Chair in 300 Northern and Planetary Geological Remote Sensing","award":["950-232175"],"award-info":[{"award-number":["950-232175"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Training a deep learning model requires highly variable data to permit reasonable generalization. If the variability in the data about to be processed is low, the interest in obtaining this generalization seems limited. Yet, it could prove interesting to specialize the model with respect to a particular theme. The use of enhanced super-resolution generative adversarial networks (ERSGAN), a specific type of deep learning architecture, allows the spatial resolution of remote sensing images to be increased by \u201challucinating\u201d non-existent details. In this study, we show that ESRGAN create better quality images when trained on thematically classified images than when trained on a wide variety of examples. All things being equal, we further show that the algorithm performs better on some themes than it does on others. Texture analysis shows that these performances are correlated with the inverse difference moment and entropy of the images.<\/jats:p>","DOI":"10.3390\/rs13204044","type":"journal-article","created":{"date-parts":[[2021,10,10]],"date-time":"2021-10-10T21:37:49Z","timestamp":1633901869000},"page":"4044","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Model Specialization for the Use of ESRGAN on Satellite and Airborne Imagery"],"prefix":"10.3390","volume":"13","author":[{"given":"\u00c9tienne","family":"Clabaut","sequence":"first","affiliation":[{"name":"D\u00e9partement de G\u00e9omatique Appliqu\u00e9e, Universit\u00e9 de Sherbrooke, Sherbrooke, QC J1K 2R1, Canada"}]},{"given":"Myriam","family":"Lemelin","sequence":"additional","affiliation":[{"name":"D\u00e9partement de G\u00e9omatique Appliqu\u00e9e, Universit\u00e9 de Sherbrooke, Sherbrooke, QC J1K 2R1, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1867-7530","authenticated-orcid":false,"given":"Micka\u00ebl","family":"Germain","sequence":"additional","affiliation":[{"name":"D\u00e9partement de G\u00e9omatique Appliqu\u00e9e, Universit\u00e9 de Sherbrooke, Sherbrooke, QC J1K 2R1, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1487-2945","authenticated-orcid":false,"given":"Yacine","family":"Bouroubi","sequence":"additional","affiliation":[{"name":"D\u00e9partement de G\u00e9omatique Appliqu\u00e9e, Universit\u00e9 de Sherbrooke, Sherbrooke, QC J1K 2R1, Canada"}]},{"given":"Tony","family":"St-Pierre","sequence":"additional","affiliation":[{"name":"XEOS Imaging Inc., Qu\u00e9bec, QC G1P 4P5, Canada"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Transon, J., d\u2019Andrimont, R., Maugnard, A., and Defourny, P. 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