DBXAI : Unlocking the Spectral insights for Hyperspectral Landcover Images with CNN-XAI Synergy

DBXAI : Unlocking the Spectral insights for Hyperspectral Landcover Images with CNN-XAI Synergy | 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 DBXAI : Unlocking the Spectral insights for Hyperspectral Landcover Images with CNN-XAI Synergy Soma Mitra, Soumadip Dey, Mauparna Nandan, Suman Lata Tripathi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8217240/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 Such vast spectral data embedded in HSI challenges model interpretation and its efficiency. Hence, finding minimum spectral information is difficult in order to improve the efficiency of DNN models. In this respect, the performance of three CNN models, namely SSRN, RESNET34, and HybridSN, has been evaluated in the Indian Pines dataset and achieved high accuracies of 99.80%, 97.56%, and 99.56%, correspondingly using all bands. Using the explainable AI methods DeepLIFT, LRP, and GradientSHAP, the 14 most important bands were selected. The strong performance of the model remained high at 98.0%, 98.26%, and 96.82%, respectively. When further aggregation of bands across the XAI methods was performed, higher accuracies of 99. 46%, 98. 24%, and 98. 97% were achieved. In this way, a multimodal dataset that combined CNN-extracted features with XAI-derived band importance enabled classical machine learning models such as SVC, RF, and KNN to achieve superlative accuracy, with RF reaching 99.99%. This work is an example of how the combination of CNNs and XAI could provide useful and interpretable HSI classification, highlighting a trade-off between accuracy, interpretability, and computational efficiency. GIS Deep Learning CNN XAI RF SVM DeepLIFT LRP GradientSHAP 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-8217240","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":563502448,"identity":"2e7cdf55-70fe-425f-bfab-8459d18f3d0e","order_by":0,"name":"Soma Mitra","email":"","orcid":"","institution":"Brainware University","correspondingAuthor":false,"prefix":"","firstName":"Soma","middleName":"","lastName":"Mitra","suffix":""},{"id":563502449,"identity":"7cc3cef9-d6dd-4d80-8e11-834ce95ebc01","order_by":1,"name":"Soumadip Dey","email":"","orcid":"","institution":"Brainware 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