MS3D-CoordAttention algorithm forHyperspectral Remote Sensing Image Classification

MS3D-CoordAttention algorithm forHyperspectral Remote Sensing Image Classification | 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 MS3D-CoordAttention algorithm forHyperspectral Remote Sensing Image Classification Shuai Yang, Ken Fu, Ying Zhang, Fei Yang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7504097/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 11 You are reading this latest preprint version Abstract In recent years, the rapid advancement of remote sensing technology has furnished a robust data foundation for landcover classification through hyperspectral image. However, conventional classification methods often struggle with effective feature extraction and achieve limited accuracy when processing such data. Further challenges, including the scarcity of samples, spectral variability within classes, and spectral similarity between different classes, limit the efficacy of classification. To address these issues, this paper introduces a landcover classification approach based on the MS3D-CoordAttention framework. The method employs a 3D-CNN for deep feature extraction from hyperspectral images and uses weighted fusion of multi-scale features to generate enriched remote sensing data. Experimental results on the Indian Pines, Pavia University, and Botswana datasets demonstrate that the enhanced method substantially outperforms traditional approaches in both accuracy and F1-score. It also shows the improvements in training efficiency. This study provides valuable technical insights for automated landcover classification, laying a strong foundation for scalable ecological conservation, more effective resource management, and informed environmental policymaking. Landcover Classification Hyperspectral Image MS3D-CoordAttention Accuracy F1-score Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 15 Feb, 2026 Reviews received at journal 04 Jan, 2026 Reviews received at journal 26 Dec, 2025 Reviewers agreed at journal 23 Dec, 2025 Reviewers agreed at journal 15 Dec, 2025 Reviewers agreed at journal 04 Nov, 2025 Reviewers agreed at journal 28 Oct, 2025 Reviewers invited by journal 27 Oct, 2025 Editor assigned by journal 04 Sep, 2025 Submission checks completed at journal 04 Sep, 2025 First submitted to journal 31 Aug, 2025 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. 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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-7504097","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":539752427,"identity":"b77f2534-6f97-4490-8af7-93fafa3c7716","order_by":0,"name":"Shuai Yang","email":"","orcid":"","institution":"China University of Geosciences (Beijing)","correspondingAuthor":false,"prefix":"","firstName":"Shuai","middleName":"","lastName":"Yang","suffix":""},{"id":539752428,"identity":"50559ec4-bf66-4481-9f51-27ed52c97e40","order_by":1,"name":"Ken 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