SCECA U-Net Crop Classification for UAV Remote Sensing Image

SCECA U-Net Crop Classification for UAV Remote Sensing Image | 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 SCECA U-Net Crop Classification for UAV Remote Sensing Image Hangjia yan, Gang Liu, Zhe Li, Zhi Li, Jing He This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4021067/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Accurately identifying crop distribution is beneficial for assessing crop growth,yield,and disasters. Crop identification is influenced by terrain and complex ecological environments,considering the challenges posed by traditional remote sensing methods such as cost,sensitivity to westher conditions, and limitations in accurately extracting crop festures.Therefore,we selected unmanned aerial vehicle(UAV) remote sensing images of Majiaoba Town, Mianyang City,Sichuan Province,China,aiming to identify and classify various crops in the region,including soybeans,corn,rice,ginger and walunts.We propose using theVGG-16 (Visual Geography Group Network-16) architecture as the main framework,enhancing it by integrating efficient channel ateention(ECA) modules into the U-Net model.It is worth noting that the improved model achieved an impressive F1-Score of 0.78,emphasizing its effectiveness in crop classification. Experimental results show that the method achieves a comprehensive classification accuracy of 79.44% and an average cross-combination rate of 0.64.Our approach outperforms traditional U-Net,PSPNet(Pyramid Scene Parsing Network),and deeplabv3+ models in terms of classification accuracy and metrics, highlighting its advantages in crop identification. crop classification deep learning U-Net network architecture Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 17 Aug, 2024 Reviewers agreed at journal 28 Mar, 2024 Reviews received at journal 20 Mar, 2024 Reviewers agreed at journal 12 Mar, 2024 Reviewers invited by journal 10 Mar, 2024 Editor assigned by journal 10 Mar, 2024 Submission checks completed at journal 07 Mar, 2024 First submitted to journal 06 Mar, 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. 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-4021067","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":277962850,"identity":"6be0b465-7779-4b66-b9a5-0b4f6838c7a0","order_by":0,"name":"Hangjia yan","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Hangjia","middleName":"","lastName":"yan","suffix":""},{"id":277962851,"identity":"a1a1afd0-2bc6-4333-b56b-8f4ca957fb2c","order_by":1,"name":"Gang Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/klEQVRIiWNgGAWjYBACPmYILcfGzJD4gIENzDHAq4UNqsWYn53hsQFQiwRhLVA6cWY/4zMJ4rSw8xi/+LmjlnHDYea0ih9lh+sY2Ju3STDU3MHjMB4zy94zx5kNDrOl3ew5d1iCgedYmQTDsWd4tRjwth1jMzjMk3absQ2oRSLHTIKx4TBeLYZ/247xGBzm/1YM1iL/hqAW48e8bTUSks0MacwQW3gIaWErY5ZtO2DAz8yQLNlzLl2yjSet2CLhGG4t/PyHN39821ZX38Z/IPHDjzJrfn72wxtvfKjBrQVkETAqkBSAYyoBnwYGBuYPDAx1+JWMglEwCkbByAYA7PdLXNMVu5oAAAAASUVORK5CYII=","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Gang","middleName":"","lastName":"Liu","suffix":""},{"id":277962852,"identity":"eed955b8-b5e2-4a63-a1e6-60eae883b597","order_by":2,"name":"Zhe Li","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Zhe","middleName":"","lastName":"Li","suffix":""},{"id":277962853,"identity":"3bcb4b64-4e9b-45d6-891b-ee8feb346a52","order_by":3,"name":"Zhi Li","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Zhi","middleName":"","lastName":"Li","suffix":""},{"id":277962854,"identity":"536274ab-2db1-4164-838b-a2f0582de79d","order_by":4,"name":"Jing He","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"He","suffix":""}],"badges":[],"createdAt":"2024-03-06 13:15:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4021067/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4021067/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":52397967,"identity":"953fe17f-13f7-436b-b4bc-c4ea12c72f04","added_by":"auto","created_at":"2024-03-11 05:49:41","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":25870906,"visible":true,"origin":"","legend":"","description":"","filename":"SCECAUNetCropClassificationforUAVRemoteSensingImage.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4021067/v1_covered_6e1349a7-c13a-4fe1-804e-e4d9ed168fa2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"SCECA U-Net Crop Classification for UAV Remote Sensing Image","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":"cluster-computing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Cluster Computing](https://www.springer.com/journal/10586)","snPcode":"10586","submissionUrl":"https://submission.nature.com/new-submission/10586/3","title":"Cluster Computing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"crop classification, deep learning, U-Net network architecture","lastPublishedDoi":"10.21203/rs.3.rs-4021067/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4021067/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Accurately identifying crop distribution is beneficial for assessing crop growth,yield,and disasters. 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