PSCSC-Net: A Deep Coupled Convolutional Sparse Coding Network for Pansharpening

Given a low-resolution multispectral (MS) image and a high-resolution panchromatic image, the task of pansharpening is to generate a high-resolution MS image. Deep learning (DL)-based methods receive extensive attention recently. Different from the existing DL-based methods, this article proposes a...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	IEEE transactions on geoscience and remote sensing Ročník 60; s. 1 - 16
Hlavní autor:	Yin, Haitao
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York IEEE 2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Algorithms Artificial neural networks Coding Convolutional sparse coding (CSC) Deep learning deep neural network deep unfolding High resolution Image resolution Iterative algorithms Iterative methods Machine learning Neural networks Optimization Pansharpening Resolution Satellites Signal resolution Spatial resolution Training
ISSN:	0196-2892, 1558-0644
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Abstract	Given a low-resolution multispectral (MS) image and a high-resolution panchromatic image, the task of pansharpening is to generate a high-resolution MS image. Deep learning (DL)-based methods receive extensive attention recently. Different from the existing DL-based methods, this article proposes a novel deep neural network for pansharpening inspired by the learned iterative soft thresholding algorithm. First, a coupled convolutional sparse coding-based pansharpening (PSCSC) model and related traditional optimization algorithm are proposed. Then, following the procedures of traditional algorithm for solving PSCSC, an interpretable end-to-end deep pansharpening network is developed using a deep unfolding strategy. The designed deep architecture can also be understood in the view of details injection (DI)-based scheme. This work offers a solution that integrates the DL-, DI-, and variational optimization-based schemes into a framework. The experimental results on the reduced- and full-scale datasets demonstrate that the proposed deep pansharpening network outperforms popular traditional methods and some current DL-based methods.
AbstractList	Given a low-resolution multispectral (MS) image and a high-resolution panchromatic image, the task of pansharpening is to generate a high-resolution MS image. Deep learning (DL)-based methods receive extensive attention recently. Different from the existing DL-based methods, this article proposes a novel deep neural network for pansharpening inspired by the learned iterative soft thresholding algorithm. First, a coupled convolutional sparse coding-based pansharpening (PSCSC) model and related traditional optimization algorithm are proposed. Then, following the procedures of traditional algorithm for solving PSCSC, an interpretable end-to-end deep pansharpening network is developed using a deep unfolding strategy. The designed deep architecture can also be understood in the view of details injection (DI)-based scheme. This work offers a solution that integrates the DL-, DI-, and variational optimization-based schemes into a framework. The experimental results on the reduced- and full-scale datasets demonstrate that the proposed deep pansharpening network outperforms popular traditional methods and some current DL-based methods.
Author	Yin, Haitao
Author_xml	– sequence: 1 givenname: Haitao orcidid: 0000-0003-2975-2188 surname: Yin fullname: Yin, Haitao email: haitaoyin@njupt.edu.cn organization: College of Automation and College of Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing, China
BookMark	eNp9kMFOwzAMhiM0JLbBAyAulTh3xEmbJtymAgMJsYntXqWZCx2lKUkL4u1ptYkDB06W7O-37G9CRrWtkZBzoDMAqq42i-f1jFEGM06l5MCPyBjiWIZURNGIjCkoETKp2AmZeL-jFKIYkjFZrtbpOg2fsL0O5sENYhOktmsq3Pa1_rRV15a21lWwbrTz2De3Zf0S9PyXdW9BYV2w0rV_1a7Bup-ckuNCVx7PDnVKNne3m_Q-fFwuHtL5Y2iY4m0IQiZyKwRSExkuERVnWsVFYoROVKRzjCNgmuaGKgQuihxMlEDOCsGVyfmUXO7XNs5-dOjbbGc719_pMyZAUZAxiJ5K9pRx1nuHRWbKVg8PtU6XVQY0G-Rlg7xskJcd5PVJ-JNsXPmu3fe_mYt9pkTEX15FgiUq4T_bmXtd
CODEN	IGRSD2
CitedBy_id	crossref_primary_10_1109_TGRS_2024_3460105 crossref_primary_10_1109_TGRS_2023_3281829 crossref_primary_10_1109_TGRS_2023_3329736 crossref_primary_10_1109_MGRS_2022_3187652 crossref_primary_10_3390_rs17010013 crossref_primary_10_1109_MGRS_2022_3171836 crossref_primary_10_1109_ACCESS_2022_3143847 crossref_primary_10_1109_TGRS_2022_3154435 crossref_primary_10_1016_j_inffus_2022_12_026 crossref_primary_10_3390_s24051410 crossref_primary_10_1109_JSTARS_2024_3408806 crossref_primary_10_1109_TGRS_2023_3261386 crossref_primary_10_1109_TGRS_2021_3139190
Cites_doi	10.14358/pers.74.2.193 10.1109/LGRS.2017.2761021 10.1109/LGRS.2017.2736020 10.1109/TIP.2020.3007824 10.1109/TIP.2014.2333661 10.1109/JSTARS.2018.2794888 10.1109/36.763274 10.1109/ICCV.2017.193 10.1109/TGRS.2007.907604 10.1109/LGRS.2004.834804 10.1109/TSP.2017.2733447 10.1109/MGRS.2016.2561021 10.1109/TGRS.2017.2675961 10.1109/JSTARS.2020.3021074 10.1109/ICCV.2015.212 10.1109/LGRS.2019.2904526 10.1109/TPAMI.2020.3027563 10.1109/CVPR.2016.90 10.1109/TGRS.2009.2029094 10.1109/TGRS.2002.803623 10.1109/TIP.2018.2819501 10.1109/JSTARS.2017.2697445 10.1109/LGRS.2004.836784 10.1109/TSMCB.2012.2198810 10.1109/ICCV.2019.00259 10.1109/TGRS.2020.2974806 10.1109/ACCESS.2019.2959238 10.1109/TGRS.2019.2906073 10.1109/TIP.2019.2944270 10.1109/TGRS.2007.901007 10.1137/13094829X 10.1109/TGRS.2019.2928715 10.1109/TGRS.2018.2817393 10.3390/rs8070594 10.1109/MGRS.2018.2890023 10.1109/JSTARS.2016.2546061 10.1109/TIP.2015.2495260 10.1109/LGRS.2014.2376034 10.1016/j.inffus.2018.11.014 10.1109/TGRS.2014.2361734 10.1109/TGRS.2010.2067219 10.1109/TPAMI.2019.2904255 10.1109/TGRS.2005.856106 10.1109/TGRS.2016.2614367 10.1109/JSTARS.2019.2898574 10.14358/PERS.72.5.591 10.1137/130928625 10.1109/MGRS.2020.2976696 10.1109/TGRS.2015.2504261 10.1109/TGRS.2012.2230332 10.1109/JSTARS.2013.2283236 10.1109/TGRS.2014.2354471 10.1080/014311698215973 10.1109/TGRS.2020.3007884 10.1785/0220190028 10.1109/JSTARS.2019.2953140 10.1109/LGRS.2014.2331291 10.3390/rs11192315 10.3390/rs8100797
ContentType	Journal Article
Copyright	Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2022
Copyright_xml	– notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2022
DBID	97E RIA RIE AAYXX CITATION 7UA 8FD C1K F1W FR3 H8D H96 KR7 L.G L7M
DOI	10.1109/TGRS.2021.3088313
DatabaseName	IEEE All-Society Periodicals Package (ASPP) 2005–Present IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE Electronic Library (IEL) CrossRef Water Resources Abstracts Technology Research Database Environmental Sciences and Pollution Management ASFA: Aquatic Sciences and Fisheries Abstracts Engineering Research Database Aerospace Database Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources Civil Engineering Abstracts Aquatic Science & Fisheries Abstracts (ASFA) Professional Advanced Technologies Database with Aerospace
DatabaseTitle	CrossRef Aerospace Database Civil Engineering Abstracts Aquatic Science & Fisheries Abstracts (ASFA) Professional Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources Technology Research Database ASFA: Aquatic Sciences and Fisheries Abstracts Engineering Research Database Advanced Technologies Database with Aerospace Water Resources Abstracts Environmental Sciences and Pollution Management
DatabaseTitleList	Aerospace Database
Database_xml	– sequence: 1 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher
DeliveryMethod	fulltext_linktorsrc
Discipline	Engineering Physics
EISSN	1558-0644
EndPage	16
ExternalDocumentID	10_1109_TGRS_2021_3088313 9462797
Genre	orig-research
GrantInformation_xml	– fundername: National Natural Science Foundation of China grantid: 61971237; 61501255 funderid: 10.13039/501100001809
GroupedDBID	-~X 0R~ 29I 4.4 5GY 5VS 6IK 97E AAJGR AARMG AASAJ AAWTH ABAZT ABQJQ ABVLG ACGFO ACGFS ACIWK ACNCT AENEX AETIX AFRAH AGQYO AGSQL AHBIQ AI. AIBXA AKJIK AKQYR ALLEH ALMA_UNASSIGNED_HOLDINGS ASUFR ATWAV BEFXN BFFAM BGNUA BKEBE BPEOZ CS3 DU5 EBS EJD F5P HZ~ H~9 IBMZZ ICLAB IFIPE IFJZH IPLJI JAVBF LAI M43 O9- OCL P2P RIA RIE RNS RXW TAE TN5 VH1 Y6R AAYXX CITATION 7UA 8FD C1K F1W FR3 H8D H96 KR7 L.G L7M
ID	FETCH-LOGICAL-c293t-16878d66e0c4c38ee932a95f7c6a794abe5412a0bc09e136fb1c471b2f639cb3
IEDL.DBID	RIE
ISICitedReferencesCount	19
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000732803400001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	0196-2892
IngestDate	Tue Aug 26 14:40:25 EDT 2025 Sat Nov 29 02:50:13 EST 2025 Tue Nov 18 21:25:31 EST 2025 Wed Aug 27 03:03:12 EDT 2025
IsPeerReviewed	true
IsScholarly	true
Language	English
License	https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html https://doi.org/10.15223/policy-029 https://doi.org/10.15223/policy-037
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c293t-16878d66e0c4c38ee932a95f7c6a794abe5412a0bc09e136fb1c471b2f639cb3
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ORCID	0000-0003-2975-2188
PQID	2619018516
PQPubID	85465
PageCount	16
ParticipantIDs	crossref_citationtrail_10_1109_TGRS_2021_3088313 crossref_primary_10_1109_TGRS_2021_3088313 ieee_primary_9462797 proquest_journals_2619018516
PublicationCentury	2000
PublicationDate	20220000 2022-00-00 20220101
PublicationDateYYYYMMDD	2022-01-01
PublicationDate_xml	– year: 2022 text: 20220000
PublicationDecade	2020
PublicationPlace	New York
PublicationPlace_xml	– name: New York
PublicationTitle	IEEE transactions on geoscience and remote sensing
PublicationTitleAbbrev	TGRS
PublicationYear	2022
Publisher	IEEE The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher_xml	– name: IEEE – name: The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
References	ref13 ref12 ref56 ref15 ref59 ref14 ref53 Papyan (ref52) 2017; 18 ref11 ref55 ref10 ref54 ref17 ref16 ref19 ref18 Simon (ref46) ref51 ref48 ref47 ref42 ref41 ref44 ref43 Yuhas (ref57) ref49 ref8 ref7 ref9 ref4 ref3 ref6 ref5 ref40 Wald (ref50) 1997; 63 ref35 Gregor (ref45) ref34 ref37 ref36 ref31 ref30 ref33 ref32 ref2 ref1 ref39 ref38 ref24 ref23 ref26 ref25 ref20 ref64 ref63 ref22 ref21 ref65 ref28 ref27 ref29 ref60 ref62 ref61 Wald (ref58)
References_xml	– ident: ref61 doi: 10.14358/pers.74.2.193 – ident: ref19 doi: 10.1109/LGRS.2017.2761021 – ident: ref38 doi: 10.1109/LGRS.2017.2736020 – ident: ref9 doi: 10.1109/TIP.2020.3007824 – ident: ref23 doi: 10.1109/TIP.2014.2333661 – ident: ref40 doi: 10.1109/JSTARS.2018.2794888 – ident: ref5 doi: 10.1109/36.763274 – ident: ref41 doi: 10.1109/ICCV.2017.193 – start-page: 399 volume-title: Proc. Int. Conf. Mach. Learn. (ICML) ident: ref45 article-title: Learning fast approximations of sparse coding – ident: ref13 doi: 10.1109/TGRS.2007.907604 – ident: ref11 doi: 10.1109/LGRS.2004.834804 – ident: ref51 doi: 10.1109/TSP.2017.2733447 – ident: ref2 doi: 10.1109/MGRS.2016.2561021 – ident: ref33 doi: 10.1109/TGRS.2017.2675961 – ident: ref43 doi: 10.1109/JSTARS.2020.3021074 – ident: ref53 doi: 10.1109/ICCV.2015.212 – ident: ref34 doi: 10.1109/LGRS.2019.2904526 – ident: ref62 doi: 10.1109/TPAMI.2020.3027563 – ident: ref37 doi: 10.1109/CVPR.2016.90 – ident: ref60 doi: 10.1109/TGRS.2009.2029094 – ident: ref7 doi: 10.1109/TGRS.2002.803623 – ident: ref10 doi: 10.1109/TIP.2018.2819501 – ident: ref15 doi: 10.1109/JSTARS.2017.2697445 – ident: ref56 doi: 10.1109/LGRS.2004.836784 – ident: ref24 doi: 10.1109/TSMCB.2012.2198810 – ident: ref47 doi: 10.1109/ICCV.2019.00259 – ident: ref16 doi: 10.1109/TGRS.2020.2974806 – ident: ref63 doi: 10.1109/ACCESS.2019.2959238 – ident: ref21 doi: 10.1109/TGRS.2019.2906073 – ident: ref48 doi: 10.1109/TIP.2019.2944270 – ident: ref12 doi: 10.1109/TGRS.2007.901007 – ident: ref55 doi: 10.1137/13094829X – ident: ref64 doi: 10.1109/TGRS.2019.2928715 – ident: ref42 doi: 10.1109/TGRS.2018.2817393 – volume: 63 start-page: 691 issue: 6 year: 1997 ident: ref50 article-title: Fusion of satellite images of different spatial resolutions: Assessing the quality of resulting images publication-title: Photogramm. Eng. Remote Sens. – ident: ref36 doi: 10.3390/rs8070594 – start-page: 99 volume-title: Proc. 3rd Conf. Fusion Earth Data Merging Point Meas. Raster Maps Remotely Sensed Images ident: ref58 article-title: Quality of high resolution synthesized images: Is there a simple criterion? – ident: ref3 doi: 10.1109/MGRS.2018.2890023 – ident: ref18 doi: 10.1109/JSTARS.2016.2546061 – start-page: 2274 volume-title: Proc. Adv. Neural Inf. Process. Syst. ident: ref46 article-title: Rethinking the CSC model for natural images – ident: ref54 doi: 10.1109/TIP.2015.2495260 – ident: ref35 doi: 10.1109/LGRS.2014.2376034 – ident: ref26 doi: 10.1016/j.inffus.2018.11.014 – ident: ref1 doi: 10.1109/TGRS.2014.2361734 – ident: ref28 doi: 10.1109/TGRS.2010.2067219 – ident: ref49 doi: 10.1109/TPAMI.2019.2904255 – ident: ref6 doi: 10.1109/TGRS.2005.856106 – volume: 18 start-page: 2887 issue: 1 year: 2017 ident: ref52 article-title: Convolutional neural networks analyzed via convolutional sparse coding publication-title: J. Mach. Learn. Res. – start-page: 147 volume-title: Proc. Summ. 3rd Annu. JPL Airborne Geosci. Workshop ident: ref57 article-title: Discrimination among semi-arid landscape endmembers using the spectral angle mapper (SAM) algorithm – ident: ref17 doi: 10.1109/TGRS.2016.2614367 – ident: ref39 doi: 10.1109/JSTARS.2019.2898574 – ident: ref8 doi: 10.14358/PERS.72.5.591 – ident: ref25 doi: 10.1137/130928625 – ident: ref4 doi: 10.1109/MGRS.2020.2976696 – ident: ref30 doi: 10.1109/TGRS.2015.2504261 – ident: ref29 doi: 10.1109/TGRS.2012.2230332 – ident: ref31 doi: 10.1109/JSTARS.2013.2283236 – ident: ref14 doi: 10.1109/TGRS.2014.2354471 – ident: ref59 doi: 10.1080/014311698215973 – ident: ref44 doi: 10.1109/TGRS.2020.3007884 – ident: ref65 doi: 10.1785/0220190028 – ident: ref27 doi: 10.1109/JSTARS.2019.2953140 – ident: ref32 doi: 10.1109/LGRS.2014.2331291 – ident: ref20 doi: 10.3390/rs11192315 – ident: ref22 doi: 10.3390/rs8100797
SSID	ssj0014517
Score	2.4705274
Snippet	Given a low-resolution multispectral (MS) image and a high-resolution panchromatic image, the task of pansharpening is to generate a high-resolution MS image....
SourceID	proquest crossref ieee
SourceType	Aggregation Database Enrichment Source Index Database Publisher
StartPage	1
SubjectTerms	Algorithms Artificial neural networks Coding Convolutional sparse coding (CSC) Deep learning deep neural network deep unfolding High resolution Image resolution Iterative algorithms Iterative methods Machine learning Neural networks Optimization Pansharpening Resolution Satellites Signal resolution Spatial resolution Training
Title	PSCSC-Net: A Deep Coupled Convolutional Sparse Coding Network for Pansharpening
URI	https://ieeexplore.ieee.org/document/9462797 https://www.proquest.com/docview/2619018516
Volume	60
WOSCitedRecordID	wos000732803400001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVIEE databaseName: IEEE Electronic Library (IEL) customDbUrl: eissn: 1558-0644 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0014517 issn: 0196-2892 databaseCode: RIE dateStart: 19800101 isFulltext: true titleUrlDefault: https://ieeexplore.ieee.org/ providerName: IEEE
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LS8NAEB60KOjBt1hf5OBJjGY3ySbrTerrVIvtwVvITiYolLbY1t_v7HZbFEXwlBB2Q5jZyXzzBjjLdMU4GkVYIpJ13aRhbrQJMRYCa5kaJOOGTWTtdv7yojtLcLGohSEil3xGl_bWxfKrIU6tq-xKJ0pmOluG5SxTs1qtRcQgSYUvjVYhGxHSRzBFpK96D89dtgSluIxZpmIRf9NBbqjKjz-xUy_3m__7sC3Y8DAyuJnxfRuWaLAD61-aC-7AqkvuxPEuPHW6rW4rbNPkOrgJbolGQWs4HfWp4uvgwx8-fl13xGYu8UOr0IL2LEM8YFgbdFijvdpQjXWj7EHv_q7Xegz9IIUQWZtPQqHyLK-UoggTjHMiBm2lTusMVcnyWBpKEyHLyGCkScSqNgJZaRlZM35BE-9DYzAc0AEEKA3bQFgntSwZaOUmMqIWjqdYqUo1IZpTtkDfZNzOuugXztiIdGGZUVhmFJ4ZTThfbBnNOmz8tXjXUn-x0BO-Ccdz9hVeBseFtQ0jhiNCHf6-6wjWpC1mcA6VY2hM3qd0Aiv4MXkbv5-64_UJTInMZw
linkProvider	IEEE
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3dT9swED-xsmnsgW0wRAfb8sDTtIDtJE7MGypjTGOhon3gLYovF20Saqt-8Pdzdk01xITEU6LIjqI7X-533wAHuWkYR6OMa0RyrpssLqyxMSZSYqsyi2T9sIm8LIvra9Nfg2-rWhgi8slndOhufSy_GePCucqOTKpVbvIXsJ6lqRLLaq1VzCDNZCiO1jGbESrEMKUwR8MfVwO2BZU8TFiqEpk80EJ-rMqjf7FXMGdvn_dp72AzAMnoZMn597BGoy148097wS145dM7cbYNl_1Bb9CLS5ofRyfRKdEk6o0Xkxtq-Dq6DcePXzeYsKFL_NCptKhc5ohHDGyjPuu0Py5Y4xwpH2B49n3YO4_DKIUYWZ_PY6mLvGi0JoEpJgURw7baZG2OumaJrC1lqVS1sCgMyUS3ViKrLataRjBokx3ojMYj2oUIlWUrCNu0VTVDrcIKK1vpuYqNbnQXxD1lKwxtxt20i5vKmxvCVI4ZlWNGFZjRha-rLZNlj42nFm876q8WBsJ3Yf-efVWQwlnlrEPBgETqj__f9QVenw9_X1QXP8tfe7ChXGmDd6_sQ2c-XdAneIm387-z6Wd_1O4AGk_Prg
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=PSCSC-Net%3A+A+Deep+Coupled+Convolutional+Sparse+Coding+Network+for+Pansharpening&rft.jtitle=IEEE+transactions+on+geoscience+and+remote+sensing&rft.au=Yin%2C+Haitao&rft.date=2022&rft.issn=0196-2892&rft.eissn=1558-0644&rft.volume=60&rft.spage=1&rft.epage=16&rft_id=info:doi/10.1109%2FTGRS.2021.3088313&rft.externalDBID=n%2Fa&rft.externalDocID=10_1109_TGRS_2021_3088313
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0196-2892&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0196-2892&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0196-2892&client=summon