Comparison of 3D CNN based deep learning architectures using hyperspectral images

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dc.contributor.authorFirat, Huseyin
dc.contributor.authorHanbay, Davut
dc.date.accessioned2024-04-24T17:18:16Z
dc.date.available2024-04-24T17:18:16Z
dc.date.issued2023
dc.departmentDicle Üniversitesien_US
dc.description.abstractHyperspectral images (HSI) are 3-dimensional (3D) image cubes with two spatial and one spectral dimensions. The development of deep learning methods has had a significant impact on HSI classification. Especially convolutional neural network (CNN) based methods are getting more attention in this field. In this study, we make use of the deep learning architectures LeNet5, AlexNet, VGG16, GoogleNet and ResNet50, which are among the successful examples of CNN for the HSI classification problem. We use a 3D CNN-based hybrid approach when using these architectures. Because, using 3D CNN, spectral-spatial features are extracted simultaneously. In this case, the classification accuracy of HSIs is increased with the spectral-spatial-based deep learning architecture. However, in the proposed model, principal component analysis (PCA) is used as a preprocessing technique for optimal band extraction from HSIs. After applying PCA, 3D cubes are obtained by neighborhood extraction and given to the input of deep learning architectures. Indian pines, Salinas, Botswana and HyRANK-Loukia datasets were used to compare the classification performances of 3D CNN-based deep learning architectures. As a result of the applications, the best classification accuracy was obtained with VGG16 architectures in Indian pines dataset, ResNet50 in Botswana dataset, VGG16 in HyRANK-Loukia dataset, LeNet5 and VGG16 architectures in Salinas dataset.en_US
dc.identifier.doi10.17341/gazimmfd.977688
dc.identifier.endpage534en_US
dc.identifier.issn1300-1884
dc.identifier.issn1304-4915
dc.identifier.issue1en_US
dc.identifier.scopus2-s2.0-85136577943
dc.identifier.scopusqualityQ2
dc.identifier.startpage521en_US
dc.identifier.trdizinid1159655
dc.identifier.urihttps://doi.org/10.17341/gazimmfd.977688
dc.identifier.urihttps://search.trdizin.gov.tr/yayin/detay/1159655
dc.identifier.urihttps://hdl.handle.net/11468/18696
dc.identifier.volume38en_US
dc.identifier.wosWOS:000835332900041
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakTR-Dizin
dc.language.isotren_US
dc.publisherGazi Univ, Fac Engineering Architectureen_US
dc.relation.ispartofJournal of The Faculty of Engineering and Architecture of Gazi University
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectHyperspectral Image Classificationen_US
dc.subjectDeep Learningen_US
dc.subject3d Convolutional Neural Networken_US
dc.subjectPrincipal Component Analysisen_US
dc.titleComparison of 3D CNN based deep learning architectures using hyperspectral imagesen_US
dc.titleComparison of 3D CNN based deep learning architectures using hyperspectral images
dc.typeArticleen_US

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