Panchromatic and multispectral image fusion for remote sensing and earth observation: Concepts, taxonomy, literature review, evaluation methodologies and challenges ahead

Panchromatic and multispectral image fusion, termed pan-sharpening, is to merge the spatial and spectral information of the source images into a fused one, which has a higher spatial and spectral resolution and is more reliable for downstream tasks compared with any of the source images. It has been...

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Autores: Zhang, Kai, Zhang, Feng, Wan, Wenbo, Yu, Hui, Sun, Jiande, Del Ser Lorente, Javier, Elyan, Eyad, Hussain, Amir
Tipo de recurso: artículo
Fecha de publicación:2023
País:España
Institución:Universidad del País Vasco
Repositorio:Addi. Archivo Digital para la Docencia y la Investigación
OAI Identifier:oai:addi.ehu.eus:10810/60301
Acceso en línea:http://hdl.handle.net/10810/60301
Access Level:acceso abierto
Palabra clave:image fusion
pan-sharpening
image quality evaluation
multispectral image
panchromatic image
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spelling Panchromatic and multispectral image fusion for remote sensing and earth observation: Concepts, taxonomy, literature review, evaluation methodologies and challenges aheadZhang, KaiZhang, FengWan, WenboYu, HuiSun, JiandeDel Ser Lorente, JavierElyan, EyadHussain, Amirimage fusionpan-sharpeningimage quality evaluationmultispectral imagepanchromatic imagePanchromatic and multispectral image fusion, termed pan-sharpening, is to merge the spatial and spectral information of the source images into a fused one, which has a higher spatial and spectral resolution and is more reliable for downstream tasks compared with any of the source images. It has been widely applied to image interpretation and pre-processing of various applications. A large number of methods have been proposed to achieve better fusion results by considering the spatial and spectral relationships among panchromatic and multispectral images. In recent years, the fast development of artificial intelligence (AI) and deep learning (DL) has significantly enhanced the development of pan-sharpening techniques. However, this field lacks a comprehensive overview of recent advances boosted by the rise of AI and DL. This paper provides a comprehensive review of a variety of pan-sharpening methods that adopt four different paradigms, i.e., component substitution, multiresolution analysis, degradation model, and deep neural networks. As an important aspect of pan-sharpening, the evaluation of the fused image is also outlined to present various assessment methods in terms of reduced-resolution and full-resolution quality measurement. Then, we conclude this paper by discussing the existing limitations, difficulties, and challenges of pan-sharpening techniques, datasets, and quality assessment. In addition, the survey summarizes the development trends in these areas, which provide useful methodological practices for researchers and professionals. Finally, the developments in pan-sharpening are summarized in the conclusion part. The aim of the survey is to serve as a referential starting point for newcomers and a common point of agreement around the research directions to be followed in this exciting area.This work was supported in part by the Natural Science Foundation of China (61901246), the China Postdoctoral Science Foundation, China Grant (2019TQ0190, 2019M662432), the Scientific Research Leader Studio of Ji’nan (2021GXRC081), and Joint Project for Smart Computing of Shandong Natural Science Foundation, China (ZR2020LZH015). Amir Hussain acknowledges the support of the UK Engineering and Physical Sciences Research Council (EPSRC)-Grants Ref. EP/M026981/1, EP/T021063/1, EP/T024917/1. Hui Yu acknowledges the support of Royal Society, UK (NIF/R1/180909). J. Del Ser would like to thank the Spanish Centro para el Desarrollo Tecnologico Industrial (CDTI, Ministry of Science and Innovation) through the “Red Cervera” Programme (AI4ES project), as well as by the Basque Government, Spain through the ELKARTEK program and the Consolidated Research Group MATHMODE (Ref. IT1456-22).Elsevier202320232023info:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10810/60301reponame:Addi. Archivo Digital para la Docencia y la Investigacióninstname:Universidad del País VascoIngléshttps://www.sciencedirect.com/science/article/pii/S1566253522002755?via%3Dihubinfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/3.0/es/© 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)Atribución 3.0 Españaoai:addi.ehu.eus:10810/603012026-06-18T09:23:17Z
dc.title.none.fl_str_mv Panchromatic and multispectral image fusion for remote sensing and earth observation: Concepts, taxonomy, literature review, evaluation methodologies and challenges ahead
title Panchromatic and multispectral image fusion for remote sensing and earth observation: Concepts, taxonomy, literature review, evaluation methodologies and challenges ahead
spellingShingle Panchromatic and multispectral image fusion for remote sensing and earth observation: Concepts, taxonomy, literature review, evaluation methodologies and challenges ahead
Zhang, Kai
image fusion
pan-sharpening
image quality evaluation
multispectral image
panchromatic image
title_short Panchromatic and multispectral image fusion for remote sensing and earth observation: Concepts, taxonomy, literature review, evaluation methodologies and challenges ahead
title_full Panchromatic and multispectral image fusion for remote sensing and earth observation: Concepts, taxonomy, literature review, evaluation methodologies and challenges ahead
title_fullStr Panchromatic and multispectral image fusion for remote sensing and earth observation: Concepts, taxonomy, literature review, evaluation methodologies and challenges ahead
title_full_unstemmed Panchromatic and multispectral image fusion for remote sensing and earth observation: Concepts, taxonomy, literature review, evaluation methodologies and challenges ahead
title_sort Panchromatic and multispectral image fusion for remote sensing and earth observation: Concepts, taxonomy, literature review, evaluation methodologies and challenges ahead
dc.creator.none.fl_str_mv Zhang, Kai
Zhang, Feng
Wan, Wenbo
Yu, Hui
Sun, Jiande
Del Ser Lorente, Javier
Elyan, Eyad
Hussain, Amir
author Zhang, Kai
author_facet Zhang, Kai
Zhang, Feng
Wan, Wenbo
Yu, Hui
Sun, Jiande
Del Ser Lorente, Javier
Elyan, Eyad
Hussain, Amir
author_role author
author2 Zhang, Feng
Wan, Wenbo
Yu, Hui
Sun, Jiande
Del Ser Lorente, Javier
Elyan, Eyad
Hussain, Amir
author2_role author
author
author
author
author
author
author
dc.subject.none.fl_str_mv image fusion
pan-sharpening
image quality evaluation
multispectral image
panchromatic image
topic image fusion
pan-sharpening
image quality evaluation
multispectral image
panchromatic image
description Panchromatic and multispectral image fusion, termed pan-sharpening, is to merge the spatial and spectral information of the source images into a fused one, which has a higher spatial and spectral resolution and is more reliable for downstream tasks compared with any of the source images. It has been widely applied to image interpretation and pre-processing of various applications. A large number of methods have been proposed to achieve better fusion results by considering the spatial and spectral relationships among panchromatic and multispectral images. In recent years, the fast development of artificial intelligence (AI) and deep learning (DL) has significantly enhanced the development of pan-sharpening techniques. However, this field lacks a comprehensive overview of recent advances boosted by the rise of AI and DL. This paper provides a comprehensive review of a variety of pan-sharpening methods that adopt four different paradigms, i.e., component substitution, multiresolution analysis, degradation model, and deep neural networks. As an important aspect of pan-sharpening, the evaluation of the fused image is also outlined to present various assessment methods in terms of reduced-resolution and full-resolution quality measurement. Then, we conclude this paper by discussing the existing limitations, difficulties, and challenges of pan-sharpening techniques, datasets, and quality assessment. In addition, the survey summarizes the development trends in these areas, which provide useful methodological practices for researchers and professionals. Finally, the developments in pan-sharpening are summarized in the conclusion part. The aim of the survey is to serve as a referential starting point for newcomers and a common point of agreement around the research directions to be followed in this exciting area.
publishDate 2023
dc.date.none.fl_str_mv 2023
2023
2023
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10810/60301
url http://hdl.handle.net/10810/60301
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv https://www.sciencedirect.com/science/article/pii/S1566253522002755?via%3Dihub
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by/3.0/es/
Atribución 3.0 España
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by/3.0/es/
Atribución 3.0 España
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:Addi. Archivo Digital para la Docencia y la Investigación
instname:Universidad del País Vasco
instname_str Universidad del País Vasco
reponame_str Addi. Archivo Digital para la Docencia y la Investigación
collection Addi. Archivo Digital para la Docencia y la Investigación
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.300719

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