Hyperspectral image classification method based on squeeze-and-excitation networks, depthwise separable convolution and multibranch feature fusion

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dc.authorid0000-0003-4585-4168en_US
dc.contributor.authorAsker, Mehmet Emin
dc.date.accessioned2023-10-20T06:28:48Z
dc.date.available2023-10-20T06:28:48Z
dc.date.issued2023en_US
dc.departmentDicle Üniversitesi, Diyarbakır Teknik Bilimler Meslek Yüksekokulu, Elektrik ve Enerji Bölümüen_US
dc.description.abstractHyperspectral image classification (HSIC) is a hot topic discussed by most researchers. In recent years, deep learning and especially CNN have provided very good results in HSIC. However, there is still a need to develop new deep learning-based methods for HSIC. In this study, a new CNN-based method is proposed to reduce the number of trainable parameters and increase HSIC accuracy. The proposed method consists of 3 branches. Squeeze-and-excitation network (SENet) in the first branch, a hybrid method consisting of the combination of 3D CNN and 2D DSC in the second branch, and 2D DSC in the third branch are used. The main purpose of using a multi-branch network structure is to further enrich the features extracted from HSI. SENet used in the first branch are integrated into the proposed method as they increase the classification performance while minimally increasing the total number of parameters. In the second and third branches, hybrid CNN methods consisting of 3D CNN and 2D Depthwise separable convolution were used. With the hybrid CNN, the number of trainable parameters is reduced and the classification performance is increased. In order to analyze the classification performance of the proposed method, applications were carried out on the WHU-Hi-HanChuan, WHU-Hi-LongKou and Indian pines datasets. As a result of the applications, 97.45%, 99.84% and 96.31% overall accuracy values were obtained, respectively. In addition, the proposed method was compared with nine different methods developed in recent years from the literature and it was seen that it obtained the best classification result.en_US
dc.identifier.citationAsker, M. E. (2023). Hyperspectral image classification method based on squeeze-and-excitation networks, depthwise separable convolution and multibranch feature fusion. Earth Science Informatics, 16(2), 1427-1448.en_US
dc.identifier.doi10.1007/s12145-023-00982-0
dc.identifier.endpage1448en_US
dc.identifier.issn1865-0473
dc.identifier.issue2en_US
dc.identifier.scopus2-s2.0-85149224548
dc.identifier.scopusqualityQ2
dc.identifier.startpage1427en_US
dc.identifier.urihttps://link.springer.com/article/10.1007/s12145-023-00982-0
dc.identifier.urihttps://hdl.handle.net/11468/12891
dc.identifier.volume16en_US
dc.identifier.wosWOS:000942651800001
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorAsker, Mehmet Emin
dc.language.isoenen_US
dc.publisherSpringer Science and Business Mediaen_US
dc.relation.ispartofEarth Science Informatics
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subject3D/2D Convolution neural networken_US
dc.subjectClassificationen_US
dc.subjectDepthwise separable convolutionen_US
dc.subjectHyperspectral imagesen_US
dc.subjectSqueeze and excitation networken_US
dc.titleHyperspectral image classification method based on squeeze-and-excitation networks, depthwise separable convolution and multibranch feature fusionen_US
dc.titleHyperspectral image classification method based on squeeze-and-excitation networks, depthwise separable convolution and multibranch feature fusion
dc.typeArticleen_US

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