Transparent and Collaborative AI for Segmentation-Based Hyperspectral Image Classification

Transparent and Collaborative AI for Segmentation-Based Hyperspectral 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 Transparent and Collaborative AI for Segmentation-Based Hyperspectral Image Classification Yashir Arafat This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8752959/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 Hyperspectral imaging has become a cornerstone technology across applications such as remote sensing, precision agriculture, environmental monitoring, and intelligent surveillance, due to its ability to capture rich and discriminative spectral information. Although significant advances have been made in machine learning– and deep learning–based classification techniques, their deployment in real-world settings remains constrained. Many existing approaches exhibit limited interpretability, high computational complexity, and little to no integration of human expertise. Moreover, data-driven models often struggle in scenarios with scarce labeled samples and fail to exploit valuable domain knowledge effectively. To address these limitations, this work introduces an interpretable, human-in-the-loop framework for segmentation-based hyperspectral image classification. The proposed approach combines spectral–spatial feature fusion with affinity propagation–based segmentation and a computationally efficient Extreme Learning Machine classifier. Reliability and transparency are enhanced by embedding explainability mechanisms and structured expert feedback directly within the learning process. In contrast to fully automated pipelines, the framework allows human experts to assess uncertain predictions and iteratively refine the model through guided feedback. Rigorous mathematical formulations are presented to describe feature integration, similarity computation, classifier training, and feedback-driven optimization. Extensive experiments on widely used hyperspectral benchmark datasets demonstrate that the proposed framework consistently outperforms conventional classification methods, particularly under limited training data conditions. Performance gains are evident not only in classification accuracy but also in robustness and interpretability. These results highlight the effectiveness of integrating segmentation techniques, lightweight learning models, and human-centered AI principles to build reliable hyperspectral classification systems. Overall, the proposed solution provides a scalable, transparent, and practical approach for real-world applications where expert oversight and explainability are critical. Artificial Intelligence and Machine Learning Computer Architecture and Engineering Spectral–Spatial Hyperspectral Analysis Human-in-the-Loop AI Explainable AI Clustering-Assisted Image Segmentation Extreme Learning Machine Full Text Additional Declarations The authors declare no competing interests. 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. 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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-8752959","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":583654729,"identity":"588b676d-3344-4a4b-a030-4e2a06fa042b","order_by":0,"name":"Yashir Arafat","email":"data:image/png;base64,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","orcid":"https://orcid.org/0009-0005-0315-0789","institution":"University of the Cumberlands, Ky, USA","correspondingAuthor":true,"prefix":"","firstName":"Yashir","middleName":"","lastName":"Arafat","suffix":""}],"badges":[],"createdAt":"2026-02-01 00:34:13","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-8752959/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8752959/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101728582,"identity":"e9357ad0-fcda-4e87-a8b6-60f349b484e3","added_by":"auto","created_at":"2026-02-03 05:32:22","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":259974,"visible":true,"origin":"","legend":"","description":"","filename":"TransparentandCollaborativeAIforSegmentationBasedHyperspectralImageClassification.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8752959/v1_covered_06542de6-8d9c-45cd-be05-e84af0934e99.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eTransparent and Collaborative AI for Segmentation-Based Hyperspectral Image Classification\u003c/strong\u003e\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"University of the Cumebrlands","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":"Spectral–Spatial Hyperspectral Analysis, Human-in-the-Loop AI, Explainable AI, Clustering-Assisted Image Segmentation, Extreme Learning Machine","lastPublishedDoi":"10.21203/rs.3.rs-8752959/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8752959/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHyperspectral imaging has become a cornerstone technology across applications such as remote sensing, precision agriculture, environmental monitoring, and intelligent surveillance, due to its ability to capture rich and discriminative spectral information. 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