Lightweight Super-Resolution Reconstruction Architecture of Remote Sensing Images Using a Residual Hierarchical Transformer Network  

Lightweight Super-Resolution Reconstruction Architecture of Remote Sensing Images Using a Residual Hierarchical Transformer Network | 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 Article Lightweight Super-Resolution Reconstruction Architecture of Remote Sensing Images Using a Residual Hierarchical Transformer Network Bo Huang, Jian Lin, Qingtang Chen, Yiqing Cao, Liaoni Wu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7445248/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 23 Dec, 2025 Read the published version in Scientific Reports → Version 1 posted 12 You are reading this latest preprint version Abstract Remote Sensing image super-resolution technology aims to enhance spatial details, and it is of great significance for the high-quality interpretation of satellite imagery. Recently, Transformer-based models have shown competitive performance in single image super-resolution (SISR). However, current transformer-based SR approaches often employ window self-attention with fixed small window sizes, limiting the receptive filed to a single scale and preventing the network from gathering multi-scale information such as local textures and repetitive patterns, impeding the model’s ability to remote sensing images. Moreover, the quadratic computational complexity resulting from global self-attention, rendering it inefficient for addressing RSISR tasks that involve processing high-resolution images. To address these issues, we proposed a vision transformer architecture called residual hierarchical transformer network (RHTN). Specifically, we have developed a residual hierarchical transformer block (RHTB) as a building block in RHTN. In the RHTB, we introduce a novel spatial-channel self-attention mechanism characterized by linear complexity relative to window dimensions. This design optimally harvests both spatial structural information and channel-wise features from the hierarchical window framework while maintaining computational tractability. Then, we adopt the spatial-gate feed-forward network to further model additional non-linear spatial information. We conducted comprehensive experiments on multiple benchmark datasets, demonstrating the superior performance of our proposed RHTN in terms of quantitative metrics and visual quality when compared to state-of-the-art methods. Physical sciences/Engineering Physical sciences/Mathematics and computing Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 23 Dec, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 19 Sep, 2025 Reviews received at journal 16 Sep, 2025 Reviews received at journal 13 Sep, 2025 Reviews received at journal 12 Sep, 2025 Reviewers agreed at journal 10 Sep, 2025 Reviewers agreed at journal 10 Sep, 2025 Reviewers agreed at journal 10 Sep, 2025 Reviewers invited by journal 10 Sep, 2025 Editor assigned by journal 10 Sep, 2025 Editor invited by journal 05 Sep, 2025 Submission checks completed at journal 27 Aug, 2025 First submitted to journal 27 Aug, 2025 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-7445248","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":515846016,"identity":"ca9de33d-11f1-4b86-8b68-a18ce6328a0e","order_by":0,"name":"Bo Huang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzUlEQVRIiWNgGAWjYBACAxDB2MAgB+GykaDFGKKaFC2JDURrMZdIfvbw6w6b9O3yPQYMH8oOM/DPbsCvxXJGmrmx7Jm03J1tPAaMM84dZpC4c4CAw24kmElLth3O3XCMx4CZt+0wg4FEAiEt6d9AWtINQFr+Eqclx0zyY9vhBLAWRqK0nHlTJs14Js1wZ1tawcGec+k8EjcIaTmevk3y5w4beXPmwxsf/CizluOfQUALCDDzMEAi6AAQ8xBWDwSMP6BaRsEoGAWjYBRgBQDwHkKMQpFnAAAAAABJRU5ErkJggg==","orcid":"","institution":"Putian University","correspondingAuthor":true,"prefix":"","firstName":"Bo","middleName":"","lastName":"Huang","suffix":""},{"id":515846017,"identity":"93d695d9-dba3-4022-a1aa-98863553dcc3","order_by":1,"name":"Jian Lin","email":"","orcid":"","institution":"Putian University","correspondingAuthor":false,"prefix":"","firstName":"Jian","middleName":"","lastName":"Lin","suffix":""},{"id":515846018,"identity":"59a6849b-a4fc-4cac-9559-71365f59bca2","order_by":2,"name":"Qingtang Chen","email":"","orcid":"","institution":"Putian University","correspondingAuthor":false,"prefix":"","firstName":"Qingtang","middleName":"","lastName":"Chen","suffix":""},{"id":515846019,"identity":"e6495058-78ae-4936-bec7-12b42e496e4d","order_by":3,"name":"Yiqing Cao","email":"","orcid":"","institution":"Putian University","correspondingAuthor":false,"prefix":"","firstName":"Yiqing","middleName":"","lastName":"Cao","suffix":""},{"id":515846020,"identity":"32f4cbaf-8074-402a-942b-5298c1b31b76","order_by":4,"name":"Liaoni Wu","email":"","orcid":"","institution":"Xiamen University","correspondingAuthor":false,"prefix":"","firstName":"Liaoni","middleName":"","lastName":"Wu","suffix":""}],"badges":[],"createdAt":"2025-08-24 09:23:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7445248/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7445248/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-28042-1","type":"published","date":"2025-12-23T15:57:32+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":99172274,"identity":"53520d86-5dd8-4d8e-8b6c-993c994320e8","added_by":"auto","created_at":"2025-12-29 16:06:56","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":26418188,"visible":true,"origin":"","legend":"","description":"","filename":"manuscriptv1.1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7445248/v1_covered_558849d4-bb40-41ec-bdbc-0e8711fd78e0.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Lightweight Super-Resolution Reconstruction Architecture of Remote Sensing Images Using a Residual Hierarchical Transformer Network ","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7445248/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7445248/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Remote Sensing image super-resolution technology aims to enhance spatial details, and it is of great significance for the high-quality interpretation of satellite imagery. 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