Enhancing hyperspectral remote sensing image classification using robust learning technique

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dc.authoridHameed, Alaa Ali/0000-0002-8514-9255
dc.authorwosidHameed, Alaa Ali/ABI-8417-2020
dc.contributor.authorHameed, Alaa Ali
dc.date.accessioned2024-05-19T14:39:15Z
dc.date.available2024-05-19T14:39:15Z
dc.date.issued2024
dc.departmentİstinye Üniversitesien_US
dc.description.abstractAdvanced sensor tech integrates into diverse applications, including remote sensing, robotics, and IoT. Combining artificial intelligence (AI) with sensors enhances their capabilities, creating smart sensors, revolutionizing remote sensing and Internet of Things (IoT). This synergy forms a potent technology in the field. This study carries out a comprehensive analysis of the progress made in Hyperspectral sensors and AI-based classification techniques that are employed in remote sensing fields that utilize hyperspectral images. The classification of images obtained from Hyperspectral Sensors (HSS) has emerged as a prominent research subject within the domain of remote sensing. HSS offer a wealth of information across numerous spectral bands, supporting diverse applications such as land cover classification, environmental monitoring, agricultural assessment, change detection, and more. However, the abundance of data present in HSS also poses the challenge called the curse of dimensionality. The reduction of data dimensionality is crucial before applying any machine learning model to achieve optimal results. The present study introduces a new hybrid strategy combining the Back-Propagation algorithm with a variable adaptive momentum (BPVAM) and principal component analysis (PCA) for the purpose of classifying hyperspectral images. PCA is first applied to obtain an optimal set of discriminative features by eliminating highly correlated and redundant features. These features are then fed into the BPVAM model for classification. The addition of the momentum term in the weight update equation of the backpropagation algorithm helped achieve faster convergence with high accuracy. The proposed model was subjected to evaluation through experiments conducted on two benchmark datasets. These results indicated that the hybrid model based on BPVAM with PCA is an efficient technique for HSS classification.en_US
dc.identifier.doi10.1016/j.jksus.2023.102981
dc.identifier.issn1018-3647
dc.identifier.issn2213-686X
dc.identifier.issue1en_US
dc.identifier.scopus2-s2.0-85175651647en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org10.1016/j.jksus.2023.102981
dc.identifier.urihttps://hdl.handle.net/20.500.12713/4736
dc.identifier.volume36en_US
dc.identifier.wosWOS:001124743600001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofJournal of King Saud University Scienceen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmz20240519_kaen_US
dc.subjectBackpropagation With Variable Adaptive Momentumen_US
dc.subjectPrincipal Component Analysisen_US
dc.subjectHyperspectral Image Classificationen_US
dc.subjectDimensionality Reductionen_US
dc.subjectHyperspectral Sensorsen_US
dc.titleEnhancing hyperspectral remote sensing image classification using robust learning techniqueen_US
dc.typeArticleen_US

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