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Deep Learning Meets Hyperspectral Image Analysis: A Multidisciplinary Review

Modern hyperspectral imaging systems produce huge datasets potentially conveying a great abundance of information; such a resource, however, poses many challenges in the analysis and interpretation of these data. Deep learning approaches certainly offer a great variety of opportunities for solving c...

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Detalles Bibliográficos
Autores principales: Signoroni, Alberto, Savardi, Mattia, Baronio, Annalisa, Benini, Sergio
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8320953/
https://www.ncbi.nlm.nih.gov/pubmed/34460490
http://dx.doi.org/10.3390/jimaging5050052
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author Signoroni, Alberto
Savardi, Mattia
Baronio, Annalisa
Benini, Sergio
author_facet Signoroni, Alberto
Savardi, Mattia
Baronio, Annalisa
Benini, Sergio
author_sort Signoroni, Alberto
collection PubMed
description Modern hyperspectral imaging systems produce huge datasets potentially conveying a great abundance of information; such a resource, however, poses many challenges in the analysis and interpretation of these data. Deep learning approaches certainly offer a great variety of opportunities for solving classical imaging tasks and also for approaching new stimulating problems in the spatial–spectral domain. This is fundamental in the driving sector of Remote Sensing where hyperspectral technology was born and has mostly developed, but it is perhaps even more true in the multitude of current and evolving application sectors that involve these imaging technologies. The present review develops on two fronts: on the one hand, it is aimed at domain professionals who want to have an updated overview on how hyperspectral acquisition techniques can combine with deep learning architectures to solve specific tasks in different application fields. On the other hand, we want to target the machine learning and computer vision experts by giving them a picture of how deep learning technologies are applied to hyperspectral data from a multidisciplinary perspective. The presence of these two viewpoints and the inclusion of application fields other than Remote Sensing are the original contributions of this review, which also highlights some potentialities and critical issues related to the observed development trends.
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spelling pubmed-83209532021-08-26 Deep Learning Meets Hyperspectral Image Analysis: A Multidisciplinary Review Signoroni, Alberto Savardi, Mattia Baronio, Annalisa Benini, Sergio J Imaging Review Modern hyperspectral imaging systems produce huge datasets potentially conveying a great abundance of information; such a resource, however, poses many challenges in the analysis and interpretation of these data. Deep learning approaches certainly offer a great variety of opportunities for solving classical imaging tasks and also for approaching new stimulating problems in the spatial–spectral domain. This is fundamental in the driving sector of Remote Sensing where hyperspectral technology was born and has mostly developed, but it is perhaps even more true in the multitude of current and evolving application sectors that involve these imaging technologies. The present review develops on two fronts: on the one hand, it is aimed at domain professionals who want to have an updated overview on how hyperspectral acquisition techniques can combine with deep learning architectures to solve specific tasks in different application fields. On the other hand, we want to target the machine learning and computer vision experts by giving them a picture of how deep learning technologies are applied to hyperspectral data from a multidisciplinary perspective. The presence of these two viewpoints and the inclusion of application fields other than Remote Sensing are the original contributions of this review, which also highlights some potentialities and critical issues related to the observed development trends. MDPI 2019-05-08 /pmc/articles/PMC8320953/ /pubmed/34460490 http://dx.doi.org/10.3390/jimaging5050052 Text en © 2019 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ).
spellingShingle Review
Signoroni, Alberto
Savardi, Mattia
Baronio, Annalisa
Benini, Sergio
Deep Learning Meets Hyperspectral Image Analysis: A Multidisciplinary Review
title Deep Learning Meets Hyperspectral Image Analysis: A Multidisciplinary Review
title_full Deep Learning Meets Hyperspectral Image Analysis: A Multidisciplinary Review
title_fullStr Deep Learning Meets Hyperspectral Image Analysis: A Multidisciplinary Review
title_full_unstemmed Deep Learning Meets Hyperspectral Image Analysis: A Multidisciplinary Review
title_short Deep Learning Meets Hyperspectral Image Analysis: A Multidisciplinary Review
title_sort deep learning meets hyperspectral image analysis: a multidisciplinary review
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8320953/
https://www.ncbi.nlm.nih.gov/pubmed/34460490
http://dx.doi.org/10.3390/jimaging5050052
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