Efficient Remote Sensing Image Classification using the Novel STConvNeXt Convolutional Network

Efficient Remote Sensing Image Classification using the Novel STConvNeXt Convolutional 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 Efficient Remote Sensing Image Classification using the Novel STConvNeXt Convolutional Network Bo Liu, Chenmei Zhan, Cheng Guo, Xiaobo Liu, Shufen Ruan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5624469/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 11 Mar, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Remote sensing image classification poses significant challenges due to complex spatial organization, high inter-class similarity, and large intra-class variance. To address these issues, we propose STConvNeXt, a novel pure convolutional neural network specifically tailored for efficient remote sensing image classification. STConvNeXt integrates a split-based mobile convolutional module, a tree structure, and a fast pyramid pooling module to achieve residual connectivity. Additionally, we introduce a threshold loss function to stabilize model training and improve classification accuracy. Comprehensive experiments on multiple remote sensing datasets demonstrate that STConvNeXt achieves a 56.49% reduction in parameters and a 49.89% decrease in computational load compared to ConvNeXt, while maintaining state-of-the-art classification accuracy. Our results highlight the effectiveness of STConvNeXt in extracting robust features from remote sensing images, advancing the frontiers of deep learning-based remote sensing analysis. Earth and environmental sciences/Climate sciences Earth and environmental sciences/Environmental sciences Earth and environmental sciences/Natural hazards Earth and environmental sciences/Planetary science Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 11 Mar, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 04 Feb, 2025 Reviews received at journal 04 Feb, 2025 Reviewers agreed at journal 29 Jan, 2025 Reviews received at journal 24 Jan, 2025 Reviewers agreed at journal 24 Jan, 2025 Reviewers invited by journal 14 Jan, 2025 Editor assigned by journal 14 Jan, 2025 Editor invited by journal 20 Dec, 2024 Submission checks completed at journal 19 Dec, 2024 First submitted to journal 11 Dec, 2024 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-5624469","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":392838583,"identity":"46b665e2-3127-49e2-a581-533f874295d1","order_by":0,"name":"Bo Liu","email":"","orcid":"","institution":"Wuhan Textile University","correspondingAuthor":false,"prefix":"","firstName":"Bo","middleName":"","lastName":"Liu","suffix":""},{"id":392838584,"identity":"c729eeae-f012-4e86-aeda-7203602b38b3","order_by":1,"name":"Chenmei Zhan","email":"","orcid":"","institution":"Wuhan Textile University","correspondingAuthor":false,"prefix":"","firstName":"Chenmei","middleName":"","lastName":"Zhan","suffix":""},{"id":392838585,"identity":"dc7d0e61-d3d7-4ba5-84e5-f8e1442ef101","order_by":2,"name":"Cheng Guo","email":"","orcid":"","institution":"Wuhan Textile University","correspondingAuthor":false,"prefix":"","firstName":"Cheng","middleName":"","lastName":"Guo","suffix":""},{"id":392838586,"identity":"9c1c6d60-a20d-4550-81a1-5e507c876c85","order_by":3,"name":"Xiaobo Liu","email":"","orcid":"","institution":"China University of Geosciences","correspondingAuthor":false,"prefix":"","firstName":"Xiaobo","middleName":"","lastName":"Liu","suffix":""},{"id":392838587,"identity":"db3f64f1-2dcf-44c8-ae2b-50431d4488ca","order_by":4,"name":"Shufen Ruan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAu0lEQVRIiWNgGAWjYDAC5gMg0gbC4SFKC1sCiEyDqCZFy2EStMi38T6T+LnjfOJ+iQTGB2/bGOTNCWkxOMZuJtl75nZij0QCs+HcNgbDnQ2EtMi3sUnwtt3OBWphk+ZtY0gwOEDQYWxskn/bzoG0sP8mSgvDMTaQ4QfAtjATpcXgGBuztWxbcn3PmYfNknPOSRhuIMJhjDffttkZs7cnH/zwpsxGnrDDEICxAUhIEK9+FIyCUTAKRgFuAADH1Tg1s2iCLAAAAABJRU5ErkJggg==","orcid":"","institution":"Wuhan Textile University","correspondingAuthor":true,"prefix":"","firstName":"Shufen","middleName":"","lastName":"Ruan","suffix":""}],"badges":[],"createdAt":"2024-12-11 12:53:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5624469/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5624469/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-92629-x","type":"published","date":"2025-03-11T15:58:25+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":78689282,"identity":"cc71a8a9-5ef4-4d0b-8ea6-1e8622e115df","added_by":"auto","created_at":"2025-03-17 16:12:30","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1646829,"visible":true,"origin":"","legend":"","description":"","filename":"STConvNeXt.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5624469/v1_covered_75b48111-363a-464d-bc68-b7677481f10c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Efficient Remote Sensing Image Classification using the Novel STConvNeXt Convolutional 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-5624469/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5624469/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Remote sensing image classification poses significant challenges due to complex spatial organization, high inter-class similarity, and large intra-class variance. 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