Enhancing Satellite Images of FACSAT-1 through Generative Adversarial Networks for Super-Resolution

Enhancing Satellite Images of FACSAT-1 through Generative Adversarial Networks for Super-Resolution | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Enhancing Satellite Images of FACSAT-1 through Generative Adversarial Networks for Super-Resolution Paola Zarate, Christian Arroyo, Jesús López, Jorge Jiménez This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3847860/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Satellite images have diverse applications across scientific, commercial, and other domains. As a result, institutions are increasingly deploying Earth observation satellites to cater to their specific needs. This is the case of the Colombian Aerospace Force, which launched its nanosatellite, the FACSAT-1, to contribute to developing the space sector in Colombia. However, in some cases, captured images may need more quality and resolution for their intended purposes. Numerous image processing tools have been developed to enhance and optimize satellite imagery to address this challenge. Deep learning techniques, particularly Generative Adversarial Networks, have recently shown significant advancements in image processing. This paper investigates the widespread application of Generative Networks for satellite and aerial imagery, specifically focusing on super-resolution tasks. Super-resolution involves increasing the resolution of satellite images by up to four times their original size. The study presents the implementation and training of four different generative models and evaluates their performance using qualitative and quantitative measures. Two metrics, namely the maximum signal-to-noise ratio and the structural similarity index, are employed for comparative analysis. By assessing the output of each generative model, this research aims to determine their efficacy in enhancing satellite imagery resolution. Artificial Neural Network Deep Learning Generative Models satellite images Super-Resolution Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3847860","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":266193633,"identity":"0fbcf299-6140-4ccc-8dac-dc52ebac7ca7","order_by":0,"name":"Paola Zarate","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9ElEQVRIiWNgGAWjYHACxgNw5gcbsEDjARxK4QCqgJmBcUYaWEsD8VqYedJQRLADc/bDBw78YKiTN28/f/CxTYKdPIPYYaAtNTY4tVj2pCUc7GFgM5xzJpnZOCch2bBBOhGo5VgaTi0GB3IMDvAw8DDOYEhmk879wZzAANLC2HAYt5bzbwwO/mGQsJ/B/5j9t0VCPRFabuQYHOZhMEicIZHMxsyQcJiwFssZzxIOyxgkJM+QeGws2ZNw3LANpCUBj1/M+ZMPPnxTUWc7gz/x4YcfCdXy/NLpDx98wBNiBkgkBLCBiAScGlAVj4JRMApGwSjADgBn2FLR39kYxQAAAABJRU5ErkJggg==","orcid":"","institution":"Research Center in Aerospace Technologies","correspondingAuthor":true,"prefix":"","firstName":"Paola","middleName":"","lastName":"Zarate","suffix":""},{"id":266193634,"identity":"47ab0da9-06e8-459b-aadf-a76ed73b4209","order_by":1,"name":"Christian Arroyo","email":"","orcid":"","institution":"Research Center in Aerospace Technologies","correspondingAuthor":false,"prefix":"","firstName":"Christian","middleName":"","lastName":"Arroyo","suffix":""},{"id":266193635,"identity":"a9a6f2c6-0874-44fc-9d8a-c9403ad9a291","order_by":2,"name":"Jesús López","email":"","orcid":"","institution":"Universidad Autónoma de Occidente","correspondingAuthor":false,"prefix":"","firstName":"Jesús","middleName":"","lastName":"López","suffix":""},{"id":266193636,"identity":"53328d2d-054f-4dbd-b62d-4ceb6dfc7aaa","order_by":3,"name":"Jorge Jiménez","email":"","orcid":"","institution":"Colombian Aerospace Force","correspondingAuthor":false,"prefix":"","firstName":"Jorge","middleName":"","lastName":"Jiménez","suffix":""}],"badges":[],"createdAt":"2024-01-09 09:44:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3847860/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3847860/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":53376829,"identity":"c775cc26-a46a-49b4-9d18-dab0de287bb6","added_by":"auto","created_at":"2024-03-25 09:22:37","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":685865,"visible":true,"origin":"","legend":"","description":"","filename":"SuperResolutionSpringerNatureLaTeXTemplate.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3847860/v1_covered_0ba99219-807e-4878-95b1-43fb435d5fef.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Enhancing Satellite Images of FACSAT-1 through Generative Adversarial Networks for Super-Resolution","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Artificial Neural Network, Deep Learning, Generative Models, satellite images, Super-Resolution","lastPublishedDoi":"10.21203/rs.3.rs-3847860/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3847860/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSatellite images have diverse applications across scientific, commercial, and other domains. 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