4CF-Net: New 3D convolutional neural network for spectral spatial classification of hyperspectral remote sensing images

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dc.authorid0000-0002-1257-8518en_US
dc.contributor.authorFırat, Hüseyin
dc.contributor.authorHanbay, Davut
dc.date.accessioned2022-12-28T11:26:51Z
dc.date.available2022-12-28T11:26:51Z
dc.date.issued2022en_US
dc.departmentDicle Üniversitesi, Diyarbakır Teknik Bilimler Meslek Yüksekokulu, Bilgisayar Teknolojileri Bölümüen_US
dc.description.abstractHyperspectral images (HSI) are contiguous band images commonly used in remote sensing. Deep learning (DL) is an effective method used in HSI classification. Convolutional neural networks (CNN) are one of the DL methods used in HSI classification. It provides automated approaches that can learn abstract features of HSIs from spectral-spatial fields. The high dimensionality of the HSIs increases the computational complexity. Therefore, most of the developed CNN models perform dimensionality reduction as a preprocessing step. Another problem in HSI classification is that spectral-spatial features must be considered in order to obtain accurate results. Because, HSI classification performance is highly dependent on spectral spatial information. In this study, a new 3D CNN model is proposed for HSI classification. The proposed method provides an effective method to simultaneously extract spectral-spatial features in HSIs. The network uses the 3D hyperspectral cube at the input. Principal component analysis is used to eliminate the dimensional redundancy in the hyperspectral cube. Then, using neighborhood extraction, spectral-spatial features are extracted effectively. The proposed method has been tested with 4 datasets. The application results were compared with 7 different DL-based methods and it was seen that our 4CF-Net method showed better classification performance.en_US
dc.identifier.citationFırat, H. ve Hanbay, D. (2022). 4CF-Net: New 3D convolutional neural network for spectral spatial classification of hyperspectral remote sensing images. Journal of the Faculty of Engineering and Architecture Gazi University, 37(1), 439-453.en_US
dc.identifier.doi10.17341/gazimmfd.901291
dc.identifier.endpage453en_US
dc.identifier.issn1300-1884
dc.identifier.issn1304-4915
dc.identifier.issue1en_US
dc.identifier.scopus2-s2.0-85119915288
dc.identifier.scopusqualityQ2
dc.identifier.startpage439en_US
dc.identifier.trdizinid1064172
dc.identifier.urihttps://dergipark.org.tr/tr/pub/gazimmfd/issue/65733/901291
dc.identifier.urihttps://hdl.handle.net/11468/11157
dc.identifier.volume37en_US
dc.identifier.wosWOS:000718898200022
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakTR-Dizin
dc.institutionauthorFırat, Hüseyin
dc.language.isoenen_US
dc.publisherGazi Universityen_US
dc.relation.ispartofJournal of the Faculty of Engineering and Architecture 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.subjectRemote sensingen_US
dc.title4CF-Net: New 3D convolutional neural network for spectral spatial classification of hyperspectral remote sensing imagesen_US
dc.title4CF-Net: New 3D convolutional neural network for spectral spatial classification of hyperspectral remote sensing images
dc.title.alternative4CF-Net: Hiperspektral uzaktan algılama görüntülerinin spektral uzamsal sınıflandırılması için yeni 3B evrişimli sinir ağıen_US
dc.title.alternative4CF-Net: Hiperspektral uzaktan algılama görüntülerinin spektral uzamsal sınıflandırılması için yeni 3B evrişimli sinir ağı
dc.typeArticleen_US

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