Multi-Scale Dual-Domain Guidance Network for Pan-sharpening
The goal of pan-sharpening is to produce a high-spatial-resolution multi-spectral (HRMS) image from a low-spatial-resolution multi-spectral (LRMS) counterpart by super-resolving the LRMS one under the guidance of a texture-rich panchromatic (PAN) image. Existing research has concentrated on using sp...
Gespeichert in:
Veröffentlicht in: | IEEE transactions on geoscience and remote sensing 2023-05, p.1-1 |
---|---|
Hauptverfasser: | , , , , , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext bestellen |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | 1 |
---|---|
container_issue | |
container_start_page | 1 |
container_title | IEEE transactions on geoscience and remote sensing |
container_volume | |
creator | He, Xuanhua Yan, Keyu Zhang, Jie Li, Rui Xie, Chengjun Zhou, Man Hong, Danfeng |
description | The goal of pan-sharpening is to produce a high-spatial-resolution multi-spectral (HRMS) image from a low-spatial-resolution multi-spectral (LRMS) counterpart by super-resolving the LRMS one under the guidance of a texture-rich panchromatic (PAN) image. Existing research has concentrated on using spatial information to generate HRMS images, but has neglected to investigate the frequency domain, which severely restricts the performance improvement. In this work, we propose a novel pan-sharpening approach, named Multi-Scale Dual-Domain Guidance Network (MSDDN) by fully exploring and exploiting the distinguished information in both the spatial and frequency domains. Specifically, the network is inborn with multi-scale U-shape manner and composed by two core parts: a spatial guidance sub-network for fusing local spatial information and a frequency guidance sub-network for fusing global frequency domain information and encouraging dual-domain complementary learning. In this way, the model can capture multi-scale dual-domain information to help it generate high-quality pan-sharpening results. Employing the proposed model on different datasets, the quantitative and qualitative results demonstrate that our method performs appreciatively against other state-of-the-art approaches and comprises a strong generalization ability for real-world scenes. The source code is available at https://github.com/alexhe101/MSDDN. |
doi_str_mv | 10.1109/TGRS.2023.3273334 |
format | Article |
fullrecord | <record><control><sourceid>ieee_RIE</sourceid><recordid>TN_cdi_ieee_primary_10119207</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>10119207</ieee_id><sourcerecordid>10119207</sourcerecordid><originalsourceid>FETCH-ieee_primary_101192073</originalsourceid><addsrcrecordid>eNqFyT0OgjAYANAOmog_BzBx6AWK_VoUG0dRXDRG2EmDRaulkBZivL2Lu9MbHkJzoCEAFcs8vWYho4yHnMWc82iAAgpiTdhGsBEae_-kFKIVxAHannrTaZKV0iic9NKQpKmltjjt9U3aUuGz6t6Ne-GqcfgiLfEP6Vpltb1P0bCSxqvZzwlaHPb57ki0Uqpona6l-xRAAQSjMf_TXwlfNeY</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>Multi-Scale Dual-Domain Guidance Network for Pan-sharpening</title><source>IEEE Electronic Library (IEL)</source><creator>He, Xuanhua ; Yan, Keyu ; Zhang, Jie ; Li, Rui ; Xie, Chengjun ; Zhou, Man ; Hong, Danfeng</creator><creatorcontrib>He, Xuanhua ; Yan, Keyu ; Zhang, Jie ; Li, Rui ; Xie, Chengjun ; Zhou, Man ; Hong, Danfeng</creatorcontrib><description>The goal of pan-sharpening is to produce a high-spatial-resolution multi-spectral (HRMS) image from a low-spatial-resolution multi-spectral (LRMS) counterpart by super-resolving the LRMS one under the guidance of a texture-rich panchromatic (PAN) image. Existing research has concentrated on using spatial information to generate HRMS images, but has neglected to investigate the frequency domain, which severely restricts the performance improvement. In this work, we propose a novel pan-sharpening approach, named Multi-Scale Dual-Domain Guidance Network (MSDDN) by fully exploring and exploiting the distinguished information in both the spatial and frequency domains. Specifically, the network is inborn with multi-scale U-shape manner and composed by two core parts: a spatial guidance sub-network for fusing local spatial information and a frequency guidance sub-network for fusing global frequency domain information and encouraging dual-domain complementary learning. In this way, the model can capture multi-scale dual-domain information to help it generate high-quality pan-sharpening results. Employing the proposed model on different datasets, the quantitative and qualitative results demonstrate that our method performs appreciatively against other state-of-the-art approaches and comprises a strong generalization ability for real-world scenes. The source code is available at https://github.com/alexhe101/MSDDN.</description><identifier>ISSN: 0196-2892</identifier><identifier>DOI: 10.1109/TGRS.2023.3273334</identifier><identifier>CODEN: IGRSD2</identifier><language>eng</language><publisher>IEEE</publisher><subject>Convolution ; Feature extraction ; Fourier transforms ; Frequency-domain analysis ; Multiresolution analysis ; Pan-sharpening ; Spatial resolution ; Spatial-frequency domain ; Superresolution</subject><ispartof>IEEE transactions on geoscience and remote sensing, 2023-05, p.1-1</ispartof><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><orcidid>0000-0001-7218-6703 ; 0009-0004-6637-853X ; 0000-0002-0629-2038 ; 0000-0002-3212-9584 ; 0000-0002-2885-1216 ; 0000-0003-2872-605X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/10119207$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>315,781,785,797,27929,27930,54763</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/10119207$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>He, Xuanhua</creatorcontrib><creatorcontrib>Yan, Keyu</creatorcontrib><creatorcontrib>Zhang, Jie</creatorcontrib><creatorcontrib>Li, Rui</creatorcontrib><creatorcontrib>Xie, Chengjun</creatorcontrib><creatorcontrib>Zhou, Man</creatorcontrib><creatorcontrib>Hong, Danfeng</creatorcontrib><title>Multi-Scale Dual-Domain Guidance Network for Pan-sharpening</title><title>IEEE transactions on geoscience and remote sensing</title><addtitle>TGRS</addtitle><description>The goal of pan-sharpening is to produce a high-spatial-resolution multi-spectral (HRMS) image from a low-spatial-resolution multi-spectral (LRMS) counterpart by super-resolving the LRMS one under the guidance of a texture-rich panchromatic (PAN) image. Existing research has concentrated on using spatial information to generate HRMS images, but has neglected to investigate the frequency domain, which severely restricts the performance improvement. In this work, we propose a novel pan-sharpening approach, named Multi-Scale Dual-Domain Guidance Network (MSDDN) by fully exploring and exploiting the distinguished information in both the spatial and frequency domains. Specifically, the network is inborn with multi-scale U-shape manner and composed by two core parts: a spatial guidance sub-network for fusing local spatial information and a frequency guidance sub-network for fusing global frequency domain information and encouraging dual-domain complementary learning. In this way, the model can capture multi-scale dual-domain information to help it generate high-quality pan-sharpening results. Employing the proposed model on different datasets, the quantitative and qualitative results demonstrate that our method performs appreciatively against other state-of-the-art approaches and comprises a strong generalization ability for real-world scenes. The source code is available at https://github.com/alexhe101/MSDDN.</description><subject>Convolution</subject><subject>Feature extraction</subject><subject>Fourier transforms</subject><subject>Frequency-domain analysis</subject><subject>Multiresolution analysis</subject><subject>Pan-sharpening</subject><subject>Spatial resolution</subject><subject>Spatial-frequency domain</subject><subject>Superresolution</subject><issn>0196-2892</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNqFyT0OgjAYANAOmog_BzBx6AWK_VoUG0dRXDRG2EmDRaulkBZivL2Lu9MbHkJzoCEAFcs8vWYho4yHnMWc82iAAgpiTdhGsBEae_-kFKIVxAHannrTaZKV0iic9NKQpKmltjjt9U3aUuGz6t6Ne-GqcfgiLfEP6Vpltb1P0bCSxqvZzwlaHPb57ki0Uqpona6l-xRAAQSjMf_TXwlfNeY</recordid><startdate>20230504</startdate><enddate>20230504</enddate><creator>He, Xuanhua</creator><creator>Yan, Keyu</creator><creator>Zhang, Jie</creator><creator>Li, Rui</creator><creator>Xie, Chengjun</creator><creator>Zhou, Man</creator><creator>Hong, Danfeng</creator><general>IEEE</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><orcidid>https://orcid.org/0000-0001-7218-6703</orcidid><orcidid>https://orcid.org/0009-0004-6637-853X</orcidid><orcidid>https://orcid.org/0000-0002-0629-2038</orcidid><orcidid>https://orcid.org/0000-0002-3212-9584</orcidid><orcidid>https://orcid.org/0000-0002-2885-1216</orcidid><orcidid>https://orcid.org/0000-0003-2872-605X</orcidid></search><sort><creationdate>20230504</creationdate><title>Multi-Scale Dual-Domain Guidance Network for Pan-sharpening</title><author>He, Xuanhua ; Yan, Keyu ; Zhang, Jie ; Li, Rui ; Xie, Chengjun ; Zhou, Man ; Hong, Danfeng</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-ieee_primary_101192073</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Convolution</topic><topic>Feature extraction</topic><topic>Fourier transforms</topic><topic>Frequency-domain analysis</topic><topic>Multiresolution analysis</topic><topic>Pan-sharpening</topic><topic>Spatial resolution</topic><topic>Spatial-frequency domain</topic><topic>Superresolution</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>He, Xuanhua</creatorcontrib><creatorcontrib>Yan, Keyu</creatorcontrib><creatorcontrib>Zhang, Jie</creatorcontrib><creatorcontrib>Li, Rui</creatorcontrib><creatorcontrib>Xie, Chengjun</creatorcontrib><creatorcontrib>Zhou, Man</creatorcontrib><creatorcontrib>Hong, Danfeng</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><jtitle>IEEE transactions on geoscience and remote sensing</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>He, Xuanhua</au><au>Yan, Keyu</au><au>Zhang, Jie</au><au>Li, Rui</au><au>Xie, Chengjun</au><au>Zhou, Man</au><au>Hong, Danfeng</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Multi-Scale Dual-Domain Guidance Network for Pan-sharpening</atitle><jtitle>IEEE transactions on geoscience and remote sensing</jtitle><stitle>TGRS</stitle><date>2023-05-04</date><risdate>2023</risdate><spage>1</spage><epage>1</epage><pages>1-1</pages><issn>0196-2892</issn><coden>IGRSD2</coden><abstract>The goal of pan-sharpening is to produce a high-spatial-resolution multi-spectral (HRMS) image from a low-spatial-resolution multi-spectral (LRMS) counterpart by super-resolving the LRMS one under the guidance of a texture-rich panchromatic (PAN) image. Existing research has concentrated on using spatial information to generate HRMS images, but has neglected to investigate the frequency domain, which severely restricts the performance improvement. In this work, we propose a novel pan-sharpening approach, named Multi-Scale Dual-Domain Guidance Network (MSDDN) by fully exploring and exploiting the distinguished information in both the spatial and frequency domains. Specifically, the network is inborn with multi-scale U-shape manner and composed by two core parts: a spatial guidance sub-network for fusing local spatial information and a frequency guidance sub-network for fusing global frequency domain information and encouraging dual-domain complementary learning. In this way, the model can capture multi-scale dual-domain information to help it generate high-quality pan-sharpening results. Employing the proposed model on different datasets, the quantitative and qualitative results demonstrate that our method performs appreciatively against other state-of-the-art approaches and comprises a strong generalization ability for real-world scenes. The source code is available at https://github.com/alexhe101/MSDDN.</abstract><pub>IEEE</pub><doi>10.1109/TGRS.2023.3273334</doi><orcidid>https://orcid.org/0000-0001-7218-6703</orcidid><orcidid>https://orcid.org/0009-0004-6637-853X</orcidid><orcidid>https://orcid.org/0000-0002-0629-2038</orcidid><orcidid>https://orcid.org/0000-0002-3212-9584</orcidid><orcidid>https://orcid.org/0000-0002-2885-1216</orcidid><orcidid>https://orcid.org/0000-0003-2872-605X</orcidid></addata></record> |
fulltext | fulltext_linktorsrc |
identifier | ISSN: 0196-2892 |
ispartof | IEEE transactions on geoscience and remote sensing, 2023-05, p.1-1 |
issn | 0196-2892 |
language | eng |
recordid | cdi_ieee_primary_10119207 |
source | IEEE Electronic Library (IEL) |
subjects | Convolution Feature extraction Fourier transforms Frequency-domain analysis Multiresolution analysis Pan-sharpening Spatial resolution Spatial-frequency domain Superresolution |
title | Multi-Scale Dual-Domain Guidance Network for Pan-sharpening |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-14T17%3A07%3A35IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-ieee_RIE&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Multi-Scale%20Dual-Domain%20Guidance%20Network%20for%20Pan-sharpening&rft.jtitle=IEEE%20transactions%20on%20geoscience%20and%20remote%20sensing&rft.au=He,%20Xuanhua&rft.date=2023-05-04&rft.spage=1&rft.epage=1&rft.pages=1-1&rft.issn=0196-2892&rft.coden=IGRSD2&rft_id=info:doi/10.1109/TGRS.2023.3273334&rft_dat=%3Cieee_RIE%3E10119207%3C/ieee_RIE%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=10119207&rfr_iscdi=true |