Spectral Similarity Based Multiscale Spatial-Spectral Preprocessing Framework for Hyperspectral Image Classification

Spectral Similarity Based Multiscale Spatial-Spectral Preprocessing Framework for 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 Spectral Similarity Based Multiscale Spatial-Spectral Preprocessing Framework for Hyperspectral Image Classification Hasan Ali AKYÜREK, Barış KOÇER This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4984951/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 30 Aug, 2024 Read the published version in Traitement du Signal → Version 1 posted You are reading this latest preprint version Abstract Hyperspectral imaging represents an advanced technology that offers an extensive array of spectral data concerning various materials. Each pixel within a hyperspectral image encompasses reflectance or transmittance values spanning a spectrum of wavelengths, thereby constructing a spectral signature or spectral curve. Despite the high spectral resolution inherent in hyperspectral images, their spatial resolution frequently remains limited, resulting in a mixture of spectral information within the spectral signatures. This situation presents a significant obstacle to achieving precise hyperspectral image classification, given that both spectral and spatial information play pivotal roles in this endeavor. In the present investigation, a novel spectral-spatial preprocessing strategy is introduced, employing a multiscale filtering technique based on spectral similarity to enhance the accuracy of hyperspectral image classification. The methodology entails performing a neighborhood operation for each target pixel vector, predicated on their spectral resemblance. This operation assigns higher priority to more similar pixels within the neighborhood window to establish the new spectral curve of the pixel of interest. The resultant spectral curves effectively amalgamate both spatial and spectral information and are subsequently utilized during the classification process instead of the original spectral curves. The study incorporates established spectral similarity metrics alongside an innovative metric grounded in Fréchet distance to calculate spectral similarities. The outcomes derived from these metrics are juxtaposed to assess their efficacy in ameliorating the accuracy of hyperspectral image classification. Moreover, the classification performance is evaluated utilizing kernel extreme learning machine and support vector classifiers across four distinct hyperspectral image datasets. The findings underscore that, particularly when confronted with constraints related to small sample sizes, the proposed spectral-spatial preprocessing technique markedly enhances the classification accuracy of hyperspectral images. Artificial Intelligence and Machine Learning Fréchet distance hyperspectral image classification multiscale filtering spatial-spectral preprocessing spectral angle mapper spectral correlation measure spectral information divergence spectral similarity Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Published Journal Publication published 30 Aug, 2024 Read the published version in Traitement du Signal → 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. 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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-4984951","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":345796860,"identity":"5eb417e0-37f6-494a-bf03-8654f26dde26","order_by":0,"name":"Hasan Ali AKYÜREK","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9klEQVRIiWNgGAWjYBACAwY2CM3PAGIdgAoSpUWyAUVLAhFaDA4Qq8Wc/ViaNO8eG2Pj473PHvw4Y5fHwN68TYLxxz2cWix70o5J8zxLMzM7c9zcsOdGcjEDz7EyCYaEYtwOO5DeJs1z4LCN2Y00NgmeD8yJDRI5ZkAtuF1mcP45SMt/G+P5z9gk/3yoT2yQf0NAyw2Qww4cMDOQYGOT5rlxGGgLDyEtz5It5xxINpY4k8ZuLHPmeGIbT1qxRUIaPoelGd54c8DOsL/9GNvDN8eqE/vZD2+88cEGtxYgYJFA4YLjCa8GBgbmD/jlR8EoGAWjYMQDAHSWU69ehp87AAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-0520-9888","institution":"Faculty of Aviation and Space Sciences, Necmettin Erbakan University, Konya 42080, Turkey","correspondingAuthor":true,"prefix":"","firstName":"Hasan","middleName":"Ali","lastName":"AKYÜREK","suffix":""},{"id":345797198,"identity":"a327678c-4365-49fb-a5f8-bc7e59af65da","order_by":1,"name":"Barış KOÇER","email":"","orcid":"","institution":"Faculty of Engineering and Natural Sciences, Konya Technical University, Konya 42250, Turkey","correspondingAuthor":false,"prefix":"","firstName":"Barış","middleName":"","lastName":"KOÇER","suffix":""}],"badges":[],"createdAt":"2024-08-27 13:40:40","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-4984951/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4984951/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.18280/ts.410410","type":"published","date":"2024-08-31T00:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":64187428,"identity":"42473046-7a68-40c1-a0c5-d8905b0b54ec","added_by":"auto","created_at":"2024-09-09 16:38:48","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1320319,"visible":true,"origin":"","legend":"","description":"","filename":"TS27827finalproofhighlighted.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4984951/v1_covered_1566afce-7a1e-480b-813c-7077d0b9ff67.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eSpectral Similarity Based Multiscale Spatial-Spectral Preprocessing Framework for Hyperspectral Image Classification\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"Necmettin Erbakan University","isAcceptedByJournal":true,"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":"Fréchet distance, hyperspectral image classification, multiscale filtering, spatial-spectral preprocessing, spectral angle mapper, spectral correlation measure, spectral information divergence, spectral similarity","lastPublishedDoi":"10.21203/rs.3.rs-4984951/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4984951/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHyperspectral imaging represents an advanced technology that offers an extensive array of spectral data concerning various materials. 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