Enhanced Image Satellite Classification by Using Stacked Learning Model (SLM)

Enhanced Image Satellite Classification by Using Stacked Learning Model (SLM) | 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 Enhanced Image Satellite Classification by Using Stacked Learning Model (SLM) Ahmed M.H. Darghaoth, Ammar Sameer Anaz, Raid Rafi Omar Al-Nima This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5455986/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 One of the common challenges in image satellite classification prediction models is obtaining high accuracy, and from this perspective, the primary objective of this paper is to implement the Stacked Learning Model (SLM) technique with the aim of enhancing the accuracy of image classification. Academic research and practical applications have widely used the suggested model, which has made significant strides in the field of image classification. By using a SLM, the model combined the results of three base models to improve the predictive ability and accuracy of image classification. This approach had been applied in a series of sequential steps designed to improve the performance of individual models and their integration. The proposed model uses the EuroSat dataset, which includes images. These images are of high resolution and cover various land use and land cover classes across Europe. They have been evenly distributed across six classes: residential, permanent crop, pasture, industrial, highway, and forest. This data has been augmented to reduce the problem of overfitting and improve the model’s ability to generalize. The model achieved a classification accuracy of 92.35% after training, confirming its robustness in image classification applications, particularly in areas where accuracy is crucial. Various comparisons, even with state-of-the-art models, will be provided. Images Classification Stacked Learning Model Satellite Images Meta-model 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-5455986","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":381809783,"identity":"e9b87e2d-aa4c-49d6-95d7-40c0dfa17be1","order_by":0,"name":"Ahmed M.H. 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Academic research and practical applications have widely used the suggested model, which has made significant strides in the field of image classification. By using a SLM, the model combined the results of three base models to improve the predictive ability and accuracy of image classification. This approach had been applied in a series of sequential steps designed to improve the performance of individual models and their integration. The proposed model uses the EuroSat dataset, which includes images. These images are of high resolution and cover various land use and land cover classes across Europe. They have been evenly distributed across six classes: residential, permanent crop, pasture, industrial, highway, and forest. This data has been augmented to reduce the problem of overfitting and improve the model\u0026rsquo;s ability to generalize. 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