Improved Stereo Matching Technique for Digital Elevation Model Generation in Mountainous Terrain

Improved Stereo Matching Technique for Digital Elevation Model Generation in Mountainous Terrain | 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 Article Improved Stereo Matching Technique for Digital Elevation Model Generation in Mountainous Terrain Nidhi Dubey, Hitesh Chhinkaniwala This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8946063/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 Geospatial applications like terrain analysis, infrastructure design, and area mapping require accurate Digital Elevation Models (DEMs) derived from satellite stereo imagery. Classical photogrammetric approaches using Rational Polynomial Coefficients (RPCs) and Ground Control Points (GCPs) remain widely adopted for precise elevation modeling. Recently, Deep Learning (DL) based stereo matching methods have emerged as promising alternatives, enabling automated DEM extraction in complex terrain conditions. The proposed work presents a systematic, application-oriented comparison between conventional photogrammetric workflows and DL assisted stereo pipelines for DEM generation using CARTOSAT-1. Quantitative assessment was conducted against high-quality reference data using standard accuracy metrics, including Root Mean Square Error (RMSE), Mean Absolute Error (MAE), terrain-specific error statistics, cross-sectional elevation profile analysis, and terrain error mapping. Results indicates, incorporating GCPs significantly improves geometric accuracy, achieving vertical RMSE values within a few meters. DL based stereo approaches that operates w/o GCPs, demonstrate competitive performance in overall elevation estimation. Among the evaluated networks, GANet achieves the lowest error and highest structural consistency, especially in steep and vegetated regions. While RPC-based photogrammetric methods with GCPs provide the highest absolute accuracy. DL shows great potential for automated DEM generation where GCPs are scarce, highlighting the complementary strengths of both approaches. Physical sciences/Engineering Earth and environmental sciences/Environmental sciences Physical sciences/Mathematics and computing Digital Elevation Model Stereo Satellite Imagery Rational Polynomial Coefficients Ground Control Points Deep Learning Terrain Analysis 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-8946063","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":600953663,"identity":"e0f2337c-3f9b-4b35-b2ec-07d8c9117e00","order_by":0,"name":"Nidhi Dubey","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+0lEQVRIiWNgGAWjYDCCAzxgiofhBmP7hw8VQCYzcwPRWtoYZ5wBaWEkTgsDww0GNmbeNhCLgBa+G7kHH/Mw3JHhu93c9oB3Xm00fztQy4+KbTi1SN7ISzbmYXjGI3nnYLuB5LbjuTMOMzYw9py5jVOLwY0cM2kehsM8BjcSGyQMtx3LbQBqYWZsw6vF/DdcS+KcY7nzidBixgzV0iZxsKEmdwMhLZJn3iVLzjE4zCN5I7HZsOHYgdyNQC0H8fmF73juwQ9vKg7b891If/j4T01d7rzzhw8++FGBWwsIMPEYwNmHweQBvOqBgPEHgl1HSPEoGAWjYBSMQAAAVvBjVcQQD/wAAAAASUVORK5CYII=","orcid":"","institution":"Faculty of Engineering Sciences and Technology, Adani University","correspondingAuthor":true,"prefix":"","firstName":"Nidhi","middleName":"","lastName":"Dubey","suffix":""},{"id":600953664,"identity":"7260a602-8752-466f-9e7e-aab26e726f39","order_by":1,"name":"Hitesh Chhinkaniwala","email":"","orcid":"","institution":"Faculty of Engineering Sciences and Technology, Adani University","correspondingAuthor":false,"prefix":"","firstName":"Hitesh","middleName":"","lastName":"Chhinkaniwala","suffix":""}],"badges":[],"createdAt":"2026-02-23 10:40:05","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8946063/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8946063/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105531083,"identity":"7132744d-57b5-49cc-86ca-946624469228","added_by":"auto","created_at":"2026-03-27 05:56:56","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":350926,"visible":true,"origin":"","legend":"","description":"","filename":"ResearchPaperNatureportfoliojournal1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8946063/v1_covered_74d7d613-5211-447d-8d2f-de31c017f257.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Improved Stereo Matching Technique for Digital Elevation Model Generation in Mountainous Terrain","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","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":"Digital Elevation Model, Stereo Satellite Imagery, Rational Polynomial Coefficients, Ground Control Points, Deep Learning, Terrain Analysis","lastPublishedDoi":"10.21203/rs.3.rs-8946063/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8946063/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGeospatial applications like terrain analysis, infrastructure design, and area mapping require accurate Digital Elevation Models (DEMs) derived from satellite stereo imagery. Classical photogrammetric approaches using Rational Polynomial Coefficients (RPCs) and Ground Control Points (GCPs) remain widely adopted for precise elevation modeling. Recently, Deep Learning (DL) based stereo matching methods have emerged as promising alternatives, enabling automated DEM extraction in complex terrain conditions. The proposed work presents a systematic, application-oriented comparison between conventional photogrammetric workflows and DL assisted stereo pipelines for DEM generation using CARTOSAT-1. Quantitative assessment was conducted against high-quality reference data using standard accuracy metrics, including Root Mean Square Error (RMSE), Mean Absolute Error (MAE), terrain-specific error statistics, cross-sectional elevation profile analysis, and terrain error mapping. Results indicates, incorporating GCPs significantly improves geometric accuracy, achieving vertical RMSE values within a few meters. 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