Comparative study of hyperspectral image classification by multidimensional Convolutional Neural Network approaches to improve accuracy

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dc.authorid0000-0003-1228-9852en_US
dc.authorscopusid56780324500en_US
dc.contributor.authorOrtaç, Gizem
dc.contributor.authorOzcan G.
dc.date.accessioned2022-01-27T06:27:23Z
dc.date.available2022-01-27T06:27:23Z
dc.date.issued2021en_US
dc.departmentBTÜ, Rektörlüğe Bağlı Birimler Araştırma Merkezleri, Araştırma Merkezleri Yayın Koleksiyonuen_US
dc.description.abstractThis study presents multidimensional deep learning approaches on hyperspectral images. Storing, processing and interpreting hyperspectral data is very difficult due to its complexity and processing load. Consequently, conventional classifiers are not feasible to extract distinctive features. In order to present efficient classifiers, we utilize deep learning and present Convolutional Neural Network (CNN) approaches. In this study, we evaluate one-dimensional, two-dimensional and three-dimensional convolution model approaches that can present efficient classification performance. Within the scope of the study, samples of widely used hyperspectral data sets are classified by using one-dimensional, two-dimensional and three-dimensional convolutional neural networks by extracting spatial, spectral and spatial-spectral features. All the features provided by hyperspectral sensors are included in the classification by using both separate and joint spectral and spatial features. As a result, our studies have shown that our three-dimensional Convolutional Neural Networks have achieved higher classification rates compared to the state of art models. The accuracy performance of our three dimensional convolutional neural network is able to converge to 100% during classification.en_US
dc.identifier.doi10.1016/j.eswa.2021.115280en_US
dc.identifier.issn09574174
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://hdl.handle.net/20.500.12885/1810
dc.identifier.volume182en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorOrtaç, Gizem
dc.language.isoenen_US
dc.publisherElsevier Ltden_US
dc.relation.ispartofExpert Systems with Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectConvolutional neural networksen_US
dc.subjectDeep learningen_US
dc.subjectHyperspectral image classificationen_US
dc.subjectHyperspectral imagingen_US
dc.subjectMulti-dimensional CNNen_US
dc.titleComparative study of hyperspectral image classification by multidimensional Convolutional Neural Network approaches to improve accuracyen_US
dc.typeArticleen_US

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