Few-Shot Remote Sensing Image Scene Classification Based on Variational Meta-Learning

Few-Shot Remote Sensing Image Scene Classification Based on Variational Meta-Learning | 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 Few-Shot Remote Sensing Image Scene Classification Based on Variational Meta-Learning Jin Wang, Xunhang Shang, Bo Yang, Haipeng Ren, Hong Jiang, Hongge Yao, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6493437/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 30 Mar, 2026 Read the published version in International Journal of Machine Learning and Cybernetics → Version 1 posted 11 You are reading this latest preprint version Abstract As a typical meta-learning method, model-agnostic meta-learning has attracted widespread attention in the remote sensing field due to its flexibility and applicability to various problems. However, the diversity and uniqueness of meta-tasks are constrained by the influence of remote sensing datasets from different imaging devices, resolutions, and diverse application requirements, which affects the algorithm’s ability to handle different tasks. To address this issue, this paper introduces a new concept called ”central points” starting from the distribution of meta-parameters, within a variational meta-learning framework 7 . Based on the concept of central points, we propose a variational meta-learning framework (VML) to obtain ”task central points” through variational inference and generate model parameters from these task central points, enabling the model to adapt to different tasks. To evaluate the utility of VML, experiments were conducted on three public datasets: UC Merced, WHU-RS19, and NWPU-RESISC45. The experimental results demonstrate that compared to some common meta-learning methods, VML effectively addresses the issues of 1 task diversity and heterogeneity in remote sensing datasets, and improves the classification accuracy of remote sensing datasets. Remote sensing scene classification Few-shot learning Meta Learning Model Agnostic Meta Learning Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 30 Mar, 2026 Read the published version in International Journal of Machine Learning and Cybernetics → Version 1 posted Editorial decision: Revision requested 24 Jun, 2025 Reviews received at journal 27 May, 2025 Reviews received at journal 23 May, 2025 Reviews received at journal 13 May, 2025 Reviewers agreed at journal 10 May, 2025 Reviewers agreed at journal 08 May, 2025 Reviewers agreed at journal 08 May, 2025 Reviewers invited by journal 28 Apr, 2025 Editor assigned by journal 26 Apr, 2025 Submission checks completed at journal 25 Apr, 2025 First submitted to journal 21 Apr, 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. 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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-6493437","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":449044123,"identity":"074343b6-084e-44f2-8dc0-cbc70a262293","order_by":0,"name":"Jin Wang","email":"","orcid":"","institution":"Xi’an Technological University","correspondingAuthor":false,"prefix":"","firstName":"Jin","middleName":"","lastName":"Wang","suffix":""},{"id":449044125,"identity":"c784e147-9e44-4511-af83-3f727ebd2df6","order_by":1,"name":"Xunhang Shang","email":"","orcid":"","institution":"Xi’an Technological 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