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Various Deep Learning Methods for Hyperspectral Images

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dc.contributor.authorSivaram M.en_US
dc.contributor.authorShobana S.J.en_US
dc.contributor.authorKhan M.en_US
dc.contributor.authorRamakrishnan J.en_US
dc.contributor.authorGoel P.M.en_US
dc.contributor.authorMaseleno A.en_US
dc.contributor.authorid55220262500en_US
dc.contributor.authorid57214569088en_US
dc.contributor.authorid57221164010en_US
dc.contributor.authorid57210390182en_US
dc.contributor.authorid57193135177en_US
dc.contributor.authorid55354910900en_US
dc.date.accessioned2023-05-29T08:07:35Z
dc.date.available2023-05-29T08:07:35Z
dc.date.issued2020
dc.descriptionImage processing; Learning systems; Remote sensing; Spectroscopy; Hyper-spectral imageries; Image processing technique; Learning methods; Learning process; Learning techniques; Models of learning; Remote sensing applications; Spectral channels; Deep learningen_US
dc.description.abstractHyperspectral imagery is widely used in remote sensing applications that take into account thousands of spectral channel compositions over a single scene. Hyperspectral imagery requires accurate models of learning to extract the hyperspectral features in an image. Due to the presence of its spatial and spectral resolution, the image learning model presents a core challenge due to its complicated nature of image frames. In order to assist it during the learning process, several attempts have been made to address its complicated nature. However, these methods failed to provide the hyperspectral imagery with a deeper understanding. Because of the presence of mixed pixels, limited training samples and redundant data, the utilization of deep learning techniques addresses the problems. The deep learning process addresses the complex image data relationship. In this paper, various deep learning methods are studied which are used for the learning of hyperspectral imagery. Initially, we present an overview on various deep learning methods for various image processing techniques. A system review is then carried out on various hyperspectral image learning models based on deep learning. � 2020 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo9213763
dc.identifier.doi10.1109/ICCIT-144147971.2020.9213763
dc.identifier.scopus2-s2.0-85098459029
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85098459029&doi=10.1109%2fICCIT-144147971.2020.9213763&partnerID=40&md5=dab878b28d3513e2b9df0c95c6e5a007
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25250
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitle2020 International Conference on Computing and Information Technology, ICCIT 2020
dc.titleVarious Deep Learning Methods for Hyperspectral Imagesen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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