{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T17:38:30Z","timestamp":1790703510234,"version":"4.1.0"},"reference-count":33,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2022,11,28]],"date-time":"2022-11-28T00:00:00Z","timestamp":1669593600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012190","name":"Ministry of Science and Higher Education of the Russian Federation","doi-asserted-by":"publisher","award":["075-15-2020-801"],"award-info":[{"award-number":["075-15-2020-801"]}],"id":[{"id":"10.13039\/501100012190","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Remote sensing is increasingly recognized as a convenient tool with a wide variety of uses in agriculture. Landsat-7 has supplied multi-spectral imagery of the Earth\u2019s surface for more than 4 years and has become an important data source for a large number of research and policy-making initiatives. Unfortunately, a scan line corrector (SLC) on Landsat-7 broke down in May 2003, which caused the loss of up to 22 percent of any given scene. We present a single-image approach based on leveraging the abilities of the deep image prior method to fill in gaps using only the corrupt image. We test the ability of deep image prior to reconstruct remote sensing scenes with different levels of corruption in them. Additionally, we compare the performance of our approach with the performance of classical single-image gap-filling methods. We demonstrate a quantitative advantage of the proposed approach compared with classical gap-filling methods. The lowest-performing restoration made by the deep image prior approach reaches 0.812 in r2, while the best value for the classical approaches is 0.685. We also present the robustness of deep image prior in comparing the influence of the number of corrupted pixels on the restoration results. The usage of this approach could expand the possibilities for a wide variety of agricultural studies and applications.<\/jats:p>","DOI":"10.3390\/s22239273","type":"journal-article","created":{"date-parts":[[2022,11,29]],"date-time":"2022-11-29T02:09:58Z","timestamp":1669687798000},"page":"9273","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["A Single Image Deep Learning Approach to Restoration of Corrupted Landsat-7 Satellite Images"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7150-9184","authenticated-orcid":false,"given":"Anna","family":"Petrovskaia","sequence":"first","affiliation":[{"name":"Center for Artificial Intelligence Technology, Skolkovo Institute of Science and Technology, 121205 Moscow, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8113-1990","authenticated-orcid":false,"given":"Raghavendra","family":"Jana","sequence":"additional","affiliation":[{"name":"Center for Agro Technologies, Skolkovo Institute of Science and Technology, 121205 Moscow, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2071-2163","authenticated-orcid":false,"given":"Ivan","family":"Oseledets","sequence":"additional","affiliation":[{"name":"Center for Artificial Intelligence Technology, Skolkovo Institute of Science and Technology, 121205 Moscow, Russia"},{"name":"Marchuk Institute of Numerical Mathematics, Russian Academy of Science, 119991 Moscow, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,28]]},"reference":[{"key":"ref_1","unstructured":"Arikan, M. 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