Multiscale Context-Aware Network for Remote Sensing Images Semantic Segmentation

Multiscale Context-Aware Network for Remote Sensing Images Semantic Segmentation | 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 Multiscale Context-Aware Network for Remote Sensing Images Semantic Segmentation Ze Wang, Jin Qin, Chuhua Huang, Yongjun Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6236533/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 Remote sensing images typically exhibit significant scale and appearance variations, requiring deep learning-based semantic segmentation methods to effectively capture local and global contextual information to improve segmentation accuracy and handle complex scenes. Although CNN-based methods have been widely applied, they excel at capturing local details but are limited in modeling global context. By relying on multi-head self-attention, transformers can effectively capture global dependencies but often incur high computational costs. In this paper, we propose a Multiscale Context-Aware Network (MCANet) that combines CNN and Transformer architectures to comprehensively and efficiently model multi-scale local and global contextual relationships. Specifically, an Adaptive Feature Enhancement Module (AFEM) is designed to enhance local contextual representations of multiscale features in the encoder using large-kernel strip convolutions and frequency-adaptive weighting. Meanwhile, we develop a Multi-scale Global-context Transformer Block (MGTB) in the decoder to efficiently extract global contextual information across different scales. Furthermore, the Feature Fusion Module (FFM) is introduced to integrate the local context enhanced by AFEM and the global context generated by MGTB, thus further promoting the joint learning of local and global information. Extensive quantitative and qualitative experiments conducted on the public Vaihingen and Potsdam datasets demonstrate that the proposed MCANet achieves superior segmentation performance compared to existing mainstream methods. Remote sensing images Semantic segmentation Convolutional neural network(CNN) Transformer 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-6236533","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":432921899,"identity":"28eb8c01-6f14-4ce7-9c85-f3a29412bd5d","order_by":0,"name":"Ze Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA60lEQVRIiWNgGAWjYDACZjBKADETHwAJHj5StDw2AGlhI9IikBbGZxIgHkEtBsd5D78uqEiLNmdvTqv8mmMnw8bA/PDRDTxaJJv50qxnnMnJ3dlzLO227LZkoMPYjI1z8GjhZ+YxM+Ztq8jdcCMn7bbkNmagFh42aXxa2MBa/oG05H8rltxWT1gL0Bbjx7wNOUAtCWmMH7cdJqxFspnHjJnnWFruhjMHkqUZtx3nYWMm4BeD82eMP/PUJOduON6Q+PHntmp7fvbmh4/xaQF5RwLGYuYBk/iVg5V8gLEYfxBWPQpGwSgYBSMQAABSEkUEh4A6HQAAAABJRU5ErkJggg==","orcid":"","institution":"Guizhou University","correspondingAuthor":true,"prefix":"","firstName":"Ze","middleName":"","lastName":"Wang","suffix":""},{"id":432921900,"identity":"b4e16252-7679-4bad-870c-3edc06c4cb55","order_by":1,"name":"Jin Qin","email":"","orcid":"","institution":"Guizhou University","correspondingAuthor":false,"prefix":"","firstName":"Jin","middleName":"","lastName":"Qin","suffix":""},{"id":432921901,"identity":"b391d8ef-5ba0-4909-8a7e-3f888594ce5e","order_by":2,"name":"Chuhua Huang","email":"","orcid":"","institution":"Guizhou University","correspondingAuthor":false,"prefix":"","firstName":"Chuhua","middleName":"","lastName":"Huang","suffix":""},{"id":432921902,"identity":"6c8eca27-765c-4108-91c1-e240de5661ea","order_by":3,"name":"Yongjun Zhang","email":"","orcid":"","institution":"Guizhou University","correspondingAuthor":false,"prefix":"","firstName":"Yongjun","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2025-03-16 08:38:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6236533/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6236533/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":91333435,"identity":"82eca23a-53e0-47b8-83b5-35133113f29f","added_by":"auto","created_at":"2025-09-15 11:23:53","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":753399,"visible":true,"origin":"","legend":"","description":"","filename":"MCANet.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6236533/v1_covered_b2a17786-e384-4943-a315-b92ed00edc37.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Multiscale Context-Aware Network for Remote Sensing Images Semantic Segmentation","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":"Remote sensing images, Semantic segmentation, Convolutional neural network(CNN), Transformer","lastPublishedDoi":"10.21203/rs.3.rs-6236533/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6236533/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eRemote sensing images typically exhibit significant scale and appearance variations, requiring deep learning-based semantic segmentation methods to effectively capture local and global contextual information to improve segmentation accuracy and handle complex scenes. Although CNN-based methods have been widely applied, they excel at capturing local details but are limited in modeling global context. By relying on multi-head self-attention, transformers can effectively capture global dependencies but often incur high computational costs. In this paper, we propose a Multiscale Context-Aware Network (MCANet) that combines CNN and Transformer architectures to comprehensively and efficiently model multi-scale local and global contextual relationships. Specifically, an Adaptive Feature Enhancement Module (AFEM) is designed to enhance local contextual representations of multiscale features in the encoder using large-kernel strip convolutions and frequency-adaptive weighting. Meanwhile, we develop a Multi-scale Global-context Transformer Block (MGTB) in the decoder to efficiently extract global contextual information across different scales. Furthermore, the Feature Fusion Module (FFM) is introduced to integrate the local context enhanced by AFEM and the global context generated by MGTB, thus further promoting the joint learning of local and global information. Extensive quantitative and qualitative experiments conducted on the public Vaihingen and Potsdam datasets demonstrate that the proposed MCANet achieves superior segmentation performance compared to existing mainstream methods.\u003c/p\u003e","manuscriptTitle":"Multiscale Context-Aware Network for Remote Sensing Images Semantic Segmentation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-01 10:35:06","doi":"10.21203/rs.3.rs-6236533/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"76fc65b7-e0b4-4713-8297-853a9e5bf370","owner":[],"postedDate":"April 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-15T11:23:36+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-01 10:35:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6236533","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6236533","identity":"rs-6236533","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}