Classification of hyperspectral remote sensing images using different dimension reduction methods with 3D/2D CNN

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dc.authoridHanbay, Davut/0000-0003-2271-7865
dc.authoridFIRAT, Huseyin/0000-0002-1257-8518
dc.authorwosidHanbay, Davut/AAG-8511-2019
dc.authorwosidFIRAT, Huseyin/ABB-7417-2021
dc.contributor.authorFirat, Huseyin
dc.contributor.authorAsker, Mehmet Emin
dc.contributor.authorHanbay, Davut
dc.date.accessioned2024-08-04T20:51:36Z
dc.date.available2024-08-04T20:51:36Z
dc.date.issued2022
dc.departmentİnönü Üniversitesien_US
dc.description.abstractThe high dimensionality of hyperspectral remote sensing images (HRSI) affects the classification performance. Therefore, most HRSI classification methods use dimension reduction methods as a solution for high dimensionality. It is aimed to extract useful features with dimension reduction methods. At the end of this process, the data dimension is reduced and the transaction cost is decreased. In this study, LDA, PCA, IPCA, ICA, SPCA, RPCA and SVD dimension reduction methods were applied as a preprocessing step to improve HRSI classification performance. Since HRSI is volumetric data and has a spectral dimension, 2D CNN cannot extract good distinguishing features from spectral dimensions. Because 2D CNN only considers spatial information. With 3D CNN, spectral-spatial features are extracted simultaneously. However, 3D CNN increases the computational cost. Therefore, in this study, Hybrid 3D/2D CNN method is used together with dimension reduction methods. Hybrid CNN method consists of a combination of 3D CNN, 2D CNN and depthwise separable convolution. While 3D CNN extracts common spectral-spatial features, more spatial features are learned with 2D CNN used after 3D CNN. With depthwise separable convolution, it reduces the number of parameters and overfitting is prevented. The applications performed on the frequently used HRSI benchmark datasets show that the classification performance of the proposed method is better than the compared methods. In addition, Indian pines, HyRANK-Loukia, Botswana and Pavia of University datasets are used to examine the effect of dimension reduction methods used together with the hybrid 3D/2D CNN method on classification performance. As a result of the applications, the best classification accuracies were obtained in PCA, LDA and IPCA with Indian pines, PCA with Pavia of university, PCA and IPCA with Salinas, PCA, RPCA and LDA dimension reduction methods with HyRANK-Loukia.en_US
dc.identifier.doi10.1016/j.rsase.2022.100694
dc.identifier.issn2352-9385
dc.identifier.scopus2-s2.0-85123241188en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.rsase.2022.100694
dc.identifier.urihttps://hdl.handle.net/11616/100425
dc.identifier.volume25en_US
dc.identifier.wosWOS:000760108100003en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofRemote Sensing Applications-Society and Environmenten_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectHyperspectral image classificationen_US
dc.subjectDimension reductionen_US
dc.subjectRemote sensingen_US
dc.subject3D/2D convolutional neural networken_US
dc.subjectDepthwise separable convolutionen_US
dc.titleClassification of hyperspectral remote sensing images using different dimension reduction methods with 3D/2D CNNen_US
dc.typeArticleen_US

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