MTASCD: A Semantic Change Detection Model Using Remote Sensing Images

MTASCD: A Semantic Change Detection Model Using Remote Sensing Images | 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 MTASCD: A Semantic Change Detection Model Using Remote Sensing Images Yufeng Yao, Ying Wang, Yiming Zhou, Yuexing Peng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6367195/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 Change detection (CD) technique has wide applications in remote sensing-based application. Most existing works regard CD as a task of pixel-level binary classification to distinguish changed areas from unchanged ones. In order to segment the changed areas and classify ground objects before and after the change, an attention mechanism augmented semantic change detection (SCD) model, Multi-Task learning and Attention mechanism based Semantic Change Detection (MTASCD), is developed, which translates SCD into the combination of binary-classification CD task and multi-classification object segmentation (MCOS) task, and fuses the semantic features from CD and MCOS for reliable segmentation and reduction of training complexity of model. In the proposed model, a Siamese network with shared weights is designed to extract the semantic features of ground objects and separate the false changes caused by environment like season change or lighting condition. The prediction result of the CD task feeds the MCOS task, and then fuses with the bi-temporal image features at the higher feature level so as to retain more detailed context information. Attention mechanism is introduced to promote the generalization and robustness of the model. Sufficient experiments are carried out on the open Land-CD dataset, and comparative results show that MTASCD can improve the accuracy of semantic change detection, especially for small-sized objects. 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-6367195","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":443035535,"identity":"6b0bcc67-3718-4283-b50d-6f95ebfa409d","order_by":0,"name":"Yufeng Yao","email":"","orcid":"","institution":"Beijing Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Yufeng","middleName":"","lastName":"Yao","suffix":""},{"id":443035536,"identity":"251a2776-8253-4ab1-9586-dc29379085d4","order_by":1,"name":"Ying Wang","email":"","orcid":"","institution":"Beijing University of Posts and Telecommunications","correspondingAuthor":false,"prefix":"","firstName":"Ying","middleName":"","lastName":"Wang","suffix":""},{"id":443035537,"identity":"b2fb2659-4184-4bdf-9222-624ee866731f","order_by":2,"name":"Yiming Zhou","email":"","orcid":"","institution":"Beijing University of Posts and Telecommunications","correspondingAuthor":false,"prefix":"","firstName":"Yiming","middleName":"","lastName":"Zhou","suffix":""},{"id":443035538,"identity":"32d818fc-5d3b-4a48-9df6-68eb8c9937c2","order_by":3,"name":"Yuexing Peng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0UlEQVRIiWNgGAWjYPACmwQIzUa8ljSgFmbStBwmQYvBjdyDjwt+nc8zuN1/gOFD2WEG/tkNBLScOZdsPLPvdrHBncMMjDPOHWaQuHOAgJbjPWbSvD23EzfcSGZg5m07zGAgkUBAy2Ee89+8PecgWv4SpQVoCzPPjwMQLYzEaJEE+kWatyG5WPLOYYODPefSeSRuENDCBwyxzzx/7PL4bjc+fPCjzFqOfwYBLQoHeBgYGNuALAkGhgNAige/eiCQbwCp+QPRMgpGwSgYBaMAKwAApaJI3EQTMEIAAAAASUVORK5CYII=","orcid":"","institution":"Beijing University of Posts and Telecommunications","correspondingAuthor":true,"prefix":"","firstName":"Yuexing","middleName":"","lastName":"Peng","suffix":""}],"badges":[],"createdAt":"2025-04-03 08:08:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6367195/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6367195/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83548009,"identity":"93714cc4-1474-43ff-8b2a-0f1118ecd26c","added_by":"auto","created_at":"2025-05-28 09:32:25","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":11808756,"visible":true,"origin":"","legend":"","description":"","filename":"SCD4SIVP.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6367195/v1_covered_bbf8c659-5b47-4a8d-a95f-38b2517b2487.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"MTASCD: A Semantic Change Detection Model Using Remote Sensing Images","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":"","lastPublishedDoi":"10.21203/rs.3.rs-6367195/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6367195/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Change detection (CD) technique has wide applications in remote sensing-based application. 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