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Various Deep Learning Methods for Hyperspectral Images

Image 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 learning

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Main Authors: Sivaram M., Shobana S.J., Khan M., Ramakrishnan J., Goel P.M., Maseleno A.
Other Authors: 55220262500
Format: Conference Paper
Published: Institute of Electrical and Electronics Engineers Inc. 2023
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spelling my.uniten.dspace-252502023-05-29T16:07:35Z Various Deep Learning Methods for Hyperspectral Images Sivaram M. Shobana S.J. Khan M. Ramakrishnan J. Goel P.M. Maseleno A. 55220262500 57214569088 57221164010 57210390182 57193135177 55354910900 Image 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 learning Hyperspectral 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. Final 2023-05-29T08:07:35Z 2023-05-29T08:07:35Z 2020 Conference Paper 10.1109/ICCIT-144147971.2020.9213763 2-s2.0-85098459029 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098459029&doi=10.1109%2fICCIT-144147971.2020.9213763&partnerID=40&md5=dab878b28d3513e2b9df0c95c6e5a007 https://irepository.uniten.edu.my/handle/123456789/25250 9213763 Institute of Electrical and Electronics Engineers Inc. Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description Image 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 learning
author2 55220262500
author_facet 55220262500
Sivaram M.
Shobana S.J.
Khan M.
Ramakrishnan J.
Goel P.M.
Maseleno A.
format Conference Paper
author Sivaram M.
Shobana S.J.
Khan M.
Ramakrishnan J.
Goel P.M.
Maseleno A.
spellingShingle Sivaram M.
Shobana S.J.
Khan M.
Ramakrishnan J.
Goel P.M.
Maseleno A.
Various Deep Learning Methods for Hyperspectral Images
author_sort Sivaram M.
title Various Deep Learning Methods for Hyperspectral Images
title_short Various Deep Learning Methods for Hyperspectral Images
title_full Various Deep Learning Methods for Hyperspectral Images
title_fullStr Various Deep Learning Methods for Hyperspectral Images
title_full_unstemmed Various Deep Learning Methods for Hyperspectral Images
title_sort various deep learning methods for hyperspectral images
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2023
_version_ 1806427294900682752
score 13.223943

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