FCEU-Net: Enhancing Remote Sensing Image Segmentation via Fusion Block, UpResBlock, and Edge Loss

FCEU-Net: Enhancing Remote Sensing Image Segmentation via Fusion Block, UpResBlock, and Edge Loss | 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 FCEU-Net: Enhancing Remote Sensing Image Segmentation via Fusion Block, UpResBlock, and Edge Loss Ning Ran, Haoyu Zhang, Shaokang Zhang, Zhou He, Jinyuan Hao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6687854/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 In the realm of remote sensing image (RSI) semantic segmentation, achieving high accuracy remains challenging due to imbalanced class distributions and high similarity among certain categories. This paper introduces an innovative model, named FCEU-Net, that enhances key feature representation while suppressing irrelevant features through three pivotal modules: the Fusion Block, UpResBlock, and Edge Loss. In particular, the Fusion Block reduces information redundancy and enhances feature capture flexibility, while the UpResBlock preserves multi-scale features through feature concatenation. The Edge Loss is specifically designed to emphasize edge regions within the image. Experimental results on the ISPRS Vaihingen and Potsdam datasets demonstrate that FCEU-Net achieves an MIoU of 73.16% and 77.25%, with m-F1 score of 84.20% and 87.01%, respectively , confirming its effectiveness for accurate RSI semantic segmentation. The code is available at https://github.com/zhang1haoyu/FCEU-Net Remote Sensing Images (RSI) Semantic Segmentation Multi-scale Feature Loss Function 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-6687854","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":466413863,"identity":"c2a9d5c2-9b1d-4d3a-82c4-c2efc372e566","order_by":0,"name":"Ning Ran","email":"","orcid":"","institution":"Hebei University","correspondingAuthor":false,"prefix":"","firstName":"Ning","middleName":"","lastName":"Ran","suffix":""},{"id":466413864,"identity":"65ee56ed-5a88-4b7d-b769-1298b77c0d87","order_by":1,"name":"Haoyu Zhang","email":"","orcid":"","institution":"Hebei University","correspondingAuthor":false,"prefix":"","firstName":"Haoyu","middleName":"","lastName":"Zhang","suffix":""},{"id":466413865,"identity":"ec245113-b466-493b-a3a9-5dd9e67d55f1","order_by":2,"name":"Shaokang Zhang","email":"","orcid":"","institution":"Hebei University","correspondingAuthor":false,"prefix":"","firstName":"Shaokang","middleName":"","lastName":"Zhang","suffix":""},{"id":466413866,"identity":"15f74d27-88da-44cb-a795-675c2fee870a","order_by":3,"name":"Zhou He","email":"","orcid":"","institution":"Shaanxi University of Science \u0026 Technology","correspondingAuthor":false,"prefix":"","firstName":"Zhou","middleName":"","lastName":"He","suffix":""},{"id":466413867,"identity":"33141c02-3220-49f1-a91b-605d42b335c0","order_by":4,"name":"Jinyuan Hao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAp0lEQVRIiWNgGAWjYBACA4YDjI8ZG0DMBOK1MBuTqoWBTZo0LeaMZ8yqC3ccZuBnzzFg+LmDCC2WDWfMbs88c5hBsueNAWPvGWIcdgCohbftMIPBjRwDZsY2IrUUg7TYk6SFGWyLBPFajhVL855J55E486zgYC9RWm4c3viZd4e1HH978sYHP4nRwiBxwgBE8YCIA8RoYGDgb39AnMJRMApGwSgYuQAAJys4VNt8pLMAAAAASUVORK5CYII=","orcid":"","institution":"HBU-UCLan School of Media, Hebei University","correspondingAuthor":true,"prefix":"","firstName":"Jinyuan","middleName":"","lastName":"Hao","suffix":""}],"badges":[],"createdAt":"2025-05-17 15:23:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6687854/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6687854/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":96604858,"identity":"6db4b1ba-39ac-40c1-9168-92f1d873fa2f","added_by":"auto","created_at":"2025-11-24 09:15:23","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3510657,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6687854/v1_covered_8792e157-2080-47b5-bfbe-9ef803bfe862.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"FCEU-Net: Enhancing Remote Sensing Image Segmentation via Fusion Block, UpResBlock, and Edge Loss","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 (RSI), Semantic Segmentation, Multi-scale Feature, Loss Function","lastPublishedDoi":"10.21203/rs.3.rs-6687854/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6687854/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"In the realm of remote sensing image (RSI) semantic segmentation, achieving high accuracy remains challenging due to imbalanced class distributions and high similarity among certain categories. 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