Multipath feature fusion for hyperspectral image classification based on hybrid 3D/2D CNN and squeeze-excitation network

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dc.authoridari, ali/0000-0002-5071-6790
dc.contributor.authorAri, Ali
dc.date.accessioned2024-08-04T20:53:21Z
dc.date.available2024-08-04T20:53:21Z
dc.date.issued2023
dc.departmentİnönü Üniversitesien_US
dc.description.abstractHyperspectral Images (HSI) are commonly used for classification thanks to their rich spectral feature information along with their spatial feature information. Convolutional Neural Network (CNN) based deep learning methods are commonly used in HSI classification (HSIC) applications to process the high nonlinearity and high dimensionality of HSI. This study proposes a method consisting of a combination of multipath Hybrid CNN and a Squeeze and Excitation (SE) network for HSIC. Features extracted with different kernel sizes in the multipath method are used together to extract richer feature information from HSI in this proposed method (PM). In the Hybrid CNN used in PM, 3D CNN was used to extract the spectral-spatial features. However, computational complexity increases with 3D CNN. Computational complexity is decreased with the use of Hybrid CNN. In addition, 2D CNN used in Hybrid CNN provides more spatial feature information to be extracted. However, in this study, 2D depthwise separable convolution (DSC) was used instead of 2D CNN. By using 2D DSC instead of standard 2D CNN, computational cost and the number of trainable parameters is significantly decreased. Finally, the PM is combined with the SE network to advance the HSIC accuracies. The SE network is designed to enhance the representation quality of CNN. WHU-Hi-HongHu (WHHH), WHU-Hi-HanChuan (WHHC), and WHU-Hi-LongKou (WHLK) datasets were used to evaluate the classification accuracies of the PM. Using a 5% training sample with WHLK, WHHC and WHHH, OA values of 99.86%, 97.51% and 97.64% were obtained. Furthermore, the PM was compared with the latest technology methods in the literature and outperformed all methods.en_US
dc.identifier.doi10.1007/s12145-022-00929-x
dc.identifier.endpage191en_US
dc.identifier.issn1865-0473
dc.identifier.issn1865-0481
dc.identifier.issue1en_US
dc.identifier.scopus2-s2.0-85145836391en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage175en_US
dc.identifier.urihttps://doi.org/10.1007/s12145-022-00929-x
dc.identifier.urihttps://hdl.handle.net/11616/101099
dc.identifier.volume16en_US
dc.identifier.wosWOS:000909976000002en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer Heidelbergen_US
dc.relation.ispartofEarth Science Informaticsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectHyperspectral image classificationen_US
dc.subjectDepthwise separable convolutionen_US
dc.subjectSqueeze and excitation networken_US
dc.subjectHybrid 3D/2D CNNen_US
dc.titleMultipath feature fusion for hyperspectral image classification based on hybrid 3D/2D CNN and squeeze-excitation networken_US
dc.typeArticleen_US

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