Full-Scale Regression Modeling of Spatial Details for Single-/Multiplatform Hypersharpening
Whenever the sharpening band is not unique, the hypersharpening paradigm extends traditional pansharpening to any <inline-formula> <tex-math notation="LaTeX">m </tex-math></inline-formula>-to-<inline-formula> <tex-math notation="LaTeX">n </t...
Saved in:
| Published in: | IEEE transactions on geoscience and remote sensing Vol. 63; pp. 1 - 16 |
|---|---|
| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
New York
IEEE
2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects: | |
| ISSN: | 0196-2892, 1558-0644 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | Whenever the sharpening band is not unique, the hypersharpening paradigm extends traditional pansharpening to any <inline-formula> <tex-math notation="LaTeX">m </tex-math></inline-formula>-to-<inline-formula> <tex-math notation="LaTeX">n </tex-math></inline-formula> fusion task, by integrating spatial information from multiple sources. The <inline-formula> <tex-math notation="LaTeX">m </tex-math></inline-formula>-to-<inline-formula> <tex-math notation="LaTeX">n </tex-math></inline-formula> fusion task is recast into multiple 1-to-<inline-formula> <tex-math notation="LaTeX">n </tex-math></inline-formula> pansharpening problems by appropriately selecting or synthesizing a set of high-resolution (HR) bands to sharpen the set of low-resolution (LR) bands. The synthesis generates each of the sharpening bands as a linear combination of the available HR bands. The spectral coefficients of each synthetic band can be estimated using a multivariate linear regression (MLR) that matches the LR band to be sharpened. A different combination of the HR bands is assimilated into each LR band. Here, we propose a novel hypersharpening instance that directly combines high-pass spatial details, rather than low-pass image components. In general, fusion methods optimize their parameters at a reduced scale, assuming a scale-invariance property. Instead, we introduce an estimation strategy that allows the fusion parameters to be directly retrieved at the full spatial scale. Starting from an iterative process, we derive an asymptotic closed-form solution and establish its convergence conditions. Three case studies involving as many real datasets-Sentinel-2 (S2), Environmental Mapping and Analysis Program (EnMAP), and WorldView-3 (WV-3)-demonstrate performance improvements at reduced and full resolutions, obtained without any parametric optimization by the user, confirming the effectiveness and versatility of the proposed solution in single- and multiplatform fusion scenarios featuring diverse spatial resolutions, spectral bands, and resolution ratios. |
|---|---|
| AbstractList | Whenever the sharpening band is not unique, the hypersharpening paradigm extends traditional pansharpening to any <inline-formula> <tex-math notation="LaTeX">m </tex-math></inline-formula>-to-<inline-formula> <tex-math notation="LaTeX">n </tex-math></inline-formula> fusion task, by integrating spatial information from multiple sources. The <inline-formula> <tex-math notation="LaTeX">m </tex-math></inline-formula>-to-<inline-formula> <tex-math notation="LaTeX">n </tex-math></inline-formula> fusion task is recast into multiple 1-to-<inline-formula> <tex-math notation="LaTeX">n </tex-math></inline-formula> pansharpening problems by appropriately selecting or synthesizing a set of high-resolution (HR) bands to sharpen the set of low-resolution (LR) bands. The synthesis generates each of the sharpening bands as a linear combination of the available HR bands. The spectral coefficients of each synthetic band can be estimated using a multivariate linear regression (MLR) that matches the LR band to be sharpened. A different combination of the HR bands is assimilated into each LR band. Here, we propose a novel hypersharpening instance that directly combines high-pass spatial details, rather than low-pass image components. In general, fusion methods optimize their parameters at a reduced scale, assuming a scale-invariance property. Instead, we introduce an estimation strategy that allows the fusion parameters to be directly retrieved at the full spatial scale. Starting from an iterative process, we derive an asymptotic closed-form solution and establish its convergence conditions. Three case studies involving as many real datasets-Sentinel-2 (S2), Environmental Mapping and Analysis Program (EnMAP), and WorldView-3 (WV-3)-demonstrate performance improvements at reduced and full resolutions, obtained without any parametric optimization by the user, confirming the effectiveness and versatility of the proposed solution in single- and multiplatform fusion scenarios featuring diverse spatial resolutions, spectral bands, and resolution ratios. Whenever the sharpening band is not unique, the hypersharpening paradigm extends traditional pansharpening to any [Formula Omitted]-to-[Formula Omitted] fusion task, by integrating spatial information from multiple sources. The [Formula Omitted]-to-[Formula Omitted] fusion task is recast into multiple 1-to-[Formula Omitted] pansharpening problems by appropriately selecting or synthesizing a set of high-resolution (HR) bands to sharpen the set of low-resolution (LR) bands. The synthesis generates each of the sharpening bands as a linear combination of the available HR bands. The spectral coefficients of each synthetic band can be estimated using a multivariate linear regression (MLR) that matches the LR band to be sharpened. A different combination of the HR bands is assimilated into each LR band. Here, we propose a novel hypersharpening instance that directly combines high-pass spatial details, rather than low-pass image components. In general, fusion methods optimize their parameters at a reduced scale, assuming a scale-invariance property. Instead, we introduce an estimation strategy that allows the fusion parameters to be directly retrieved at the full spatial scale. Starting from an iterative process, we derive an asymptotic closed-form solution and establish its convergence conditions. Three case studies involving as many real datasets—Sentinel-2 (S2), Environmental Mapping and Analysis Program (EnMAP), and WorldView-3 (WV-3)—demonstrate performance improvements at reduced and full resolutions, obtained without any parametric optimization by the user, confirming the effectiveness and versatility of the proposed solution in single- and multiplatform fusion scenarios featuring diverse spatial resolutions, spectral bands, and resolution ratios. |
| Author | Alparone, Luciano Arienzo, Alberto Vivone, Gemine Garzelli, Andrea |
| Author_xml | – sequence: 1 givenname: Alberto orcidid: 0000-0002-1584-4631 surname: Arienzo fullname: Arienzo, Alberto email: alberto.arienzo@cnr.it organization: National Research Council-Institute of Methodologies for Environmental Analysis (CNR-IMAA), Tito Scalo, Italy – sequence: 2 givenname: Andrea orcidid: 0000-0003-2332-780X surname: Garzelli fullname: Garzelli, Andrea email: andrea.garzelli@unisi.it organization: Department of Information Engineering and Mathematics, University of Siena, Siena, Italy – sequence: 3 givenname: Luciano orcidid: 0000-0002-8984-938X surname: Alparone fullname: Alparone, Luciano email: luciano.alparone@unifi.it organization: Department of Information Engineering, University of Florence, Florence, Italy – sequence: 4 givenname: Gemine orcidid: 0000-0001-9542-0638 surname: Vivone fullname: Vivone, Gemine email: gemine.vivone@cnr.it organization: National Research Council-Institute of Methodologies for Environmental Analysis (CNR-IMAA), Tito Scalo, Italy |
| BookMark | eNpFkMFqwkAQhpdioWr7AIUeAj1Hdza72eyx2KoFpWDsqYcwrBMbWZN0Nx58-0YsdC4DM98_A9-IDeqmJsYegU8AuJluF5t8IrhQkyQF2dcNG4JSWcxTKQdsyMGksciMuGOjEA6cg1Sgh-xrfnIuzi06ija09xRC1dTRutmRq-p91JRR3mJXoYteqcPKhahsfJT3O0fxdH1yXdU67PrhMVqeW_LhG31LdQ_cs9sSXaCHvz5mn_O37WwZrz4W77OXVWyFzLqYUkIstdSUkACNyhhtyNpUKY5cGq1QGBCoMVPWCERtbcl3aYnW4C6RyZg9X--2vvk5UeiKQ3Pydf-ySERvA1IA6Cm4UtY3IXgqi9ZXR_TnAnhxcVhcHBYXh8Wfwz7zdM1URPTPA2hjpE5-ASKEcEY |
| CODEN | IGRSD2 |
| Cites_doi | 10.14358/PERS.72.5.591 10.1109/MGRS.2024.3509139 10.1109/MGRS.2022.3187652 10.1109/JSTARS.2015.2440092 10.1109/TNNLS.2024.3385473 10.1109/MGRS.2020.3019315 10.1109/JSTARS.2021.3075727 10.1117/12.976298 10.1016/j.inffus.2020.03.012 10.1109/TNNLS.2024.3409563 10.1109/TGRS.2009.2029094 10.1109/LGRS.2017.2677087 10.3390/rs8030172 10.1109/TPAMI.2023.3279050 10.1109/LGRS.2018.2884087 10.3390/jimaging11010001 10.3390/rs11192315 10.1109/TGRS.2021.3101848 10.1109/TGRS.2020.3000267 10.1109/TGRS.2017.2697943 10.3390/rs13214399 10.1201/b18189 10.3390/rs16244694 10.1109/LGRS.2017.2777916 10.14358/pers.74.2.193 10.1109/TCYB.2019.2951572 10.1109/TCYB.2023.3238200 10.1109/TGRS.2004.837324 10.1109/TIP.2016.2556944 10.1137/1.9781611970937 10.1109/LGRS.2009.2022650 10.1109/TIP.2018.2819501 10.1109/TGRS.2024.3405848 10.1109/JSTARS.2024.3406762 10.3390/rs16193576 10.1109/IGARSS.2018.8518869 10.1017/CBO9781316017876 10.3390/rs16050875 10.1109/TIP.2018.2836307 10.1109/TGRS.2021.3066425 10.1109/LGRS.2022.3194257 10.1109/TGRS.2019.2906073 10.1109/TGRS.2023.3332176 10.1109/TIP.2015.2458572 10.1109/JSTARS.2021.3086877 10.1109/IGARSS.2005.1525659 10.1109/TIP.2020.3004261 10.3390/rs3091817 10.1109/TNNLS.2023.3297319 10.1109/TGRS.2011.2161320 10.1117/12.2067770 10.1109/MGRS.2022.3170092 10.1109/WHISPERS.2015.8075412 10.1016/j.rse.2020.112121 10.1016/j.inffus.2022.08.032 |
| ContentType | Journal Article |
| Copyright | Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2025 |
| Copyright_xml | – notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2025 |
| DBID | 97E RIA RIE AAYXX CITATION 7UA 8FD C1K F1W FR3 H8D H96 KR7 L.G L7M |
| DOI | 10.1109/TGRS.2025.3614444 |
| 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_2025_3614444 11179947 |
| Genre | orig-research |
| GrantInformation_xml | – fundername: Italian Ministry of University and Research funded by the European Union - NextGenerationEU grantid: CN_00000033 – fundername: National Recovery and Resilience Plan (NRRP), Mission 4 Component 2 Investment 1.4 - Call for tender grantid: 3138 – fundername: rectified by Decree grantid: 3175 – fundername: Concession Decree grantid: 1034 – fundername: Italian Ministry of University and Research, CUP grantid: B83C22002930006 – fundername: title “National Biodiversity Future Center -NBFC." |
| GroupedDBID | -~X 0R~ 29I 4.4 5GY 5VS 6IK 97E AAJGR 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-c248t-e6eaaf747e3e217a59979ecc6550a04975a2912a7a85c92aa7ccf0d6fac9ad343 |
| IEDL.DBID | RIE |
| ISICitedReferencesCount | 0 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001594874000004&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 | Sat Nov 29 14:29:50 EST 2025 Sat Nov 29 07:04:49 EST 2025 Sat Oct 25 03:08:12 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-c248t-e6eaaf747e3e217a59979ecc6550a04975a2912a7a85c92aa7ccf0d6fac9ad343 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ORCID | 0000-0002-1584-4631 0000-0001-9542-0638 0000-0003-2332-780X 0000-0002-8984-938X |
| PQID | 3261416111 |
| PQPubID | 85465 |
| PageCount | 16 |
| ParticipantIDs | ieee_primary_11179947 proquest_journals_3261416111 crossref_primary_10_1109_TGRS_2025_3614444 |
| PublicationCentury | 2000 |
| PublicationDate | 20250000 2025-00-00 20250101 |
| PublicationDateYYYYMMDD | 2025-01-01 |
| PublicationDate_xml | – year: 2025 text: 20250000 |
| PublicationDecade | 2020 |
| PublicationPlace | New York |
| PublicationPlace_xml | – name: New York |
| PublicationTitle | IEEE transactions on geoscience and remote sensing |
| PublicationTitleAbbrev | TGRS |
| PublicationYear | 2025 |
| 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 | ref57 ref12 Wald (ref54) 2002 ref56 ref15 ref59 Yokoya (ref3) 2016; 8 Shaw (ref6) 2003; 14 ref14 ref58 ref53 ref52 ref11 ref55 ref10 Alparone (ref44) 2024; 16 ref17 ref16 ref19 ref18 Alparone (ref21) 2024; 16 Wald (ref51) 1997; 63 ref46 Heldens (ref2) 2011; 3 ref48 ref47 ref42 ref41 ref43 ref49 ref8 Alparone (ref5) 2024; 16 ref7 ref40 ref35 Alparone (ref50) 2024; 11 ref34 ref37 ref36 ref31 Alparone (ref20) 2024; 17 ref30 ref33 ref32 Vivone (ref13) 2021; 14 Yuhas (ref38) ref1 ref39 Zhang (ref4) 2021; 252 Arienzo (ref45) 2021; 13 ref24 ref23 ref26 ref25 Vivone (ref9) 2019; 11 ref22 ref28 ref27 ref29 |
| References_xml | – ident: ref41 doi: 10.14358/PERS.72.5.591 – ident: ref8 doi: 10.1109/MGRS.2024.3509139 – ident: ref14 doi: 10.1109/MGRS.2022.3187652 – ident: ref18 doi: 10.1109/JSTARS.2015.2440092 – volume: 14 start-page: 3 issue: 1 year: 2003 ident: ref6 article-title: Spectral imaging for remote sensing publication-title: LINCOLN Lab. J. – ident: ref26 doi: 10.1109/TNNLS.2024.3385473 – volume-title: Data Fusion: Definitions Architectures—Fusion Images Different Spatial Resolutions year: 2002 ident: ref54 – ident: ref12 doi: 10.1109/MGRS.2020.3019315 – ident: ref30 doi: 10.1109/JSTARS.2021.3075727 – ident: ref39 doi: 10.1117/12.976298 – ident: ref19 doi: 10.1016/j.inffus.2020.03.012 – ident: ref32 doi: 10.1109/TNNLS.2024.3409563 – ident: ref57 doi: 10.1109/TGRS.2009.2029094 – ident: ref16 doi: 10.1109/LGRS.2017.2677087 – volume: 8 start-page: 172 issue: 3 year: 2016 ident: ref3 article-title: Potential of resolution-enhanced hyperspectral data for mineral mapping using simulated EnMAP and Sentinel-2 images publication-title: Remote Sens. doi: 10.3390/rs8030172 – ident: ref34 doi: 10.1109/TPAMI.2023.3279050 – ident: ref22 doi: 10.1109/LGRS.2018.2884087 – volume: 11 start-page: 1 issue: 1 year: 2024 ident: ref50 article-title: Benchmarking of multispectral pansharpening: Reproducibility, assessment, and meta-analysis publication-title: J. Imag. doi: 10.3390/jimaging11010001 – volume: 11 start-page: 2315 issue: 19 year: 2019 ident: ref9 article-title: Fast reproducible pansharpening based on instrument and acquisition modeling: AWLP revisited publication-title: Remote Sens. doi: 10.3390/rs11192315 – ident: ref28 doi: 10.1109/TGRS.2021.3101848 – ident: ref17 doi: 10.1109/TGRS.2020.3000267 – ident: ref37 doi: 10.1109/TGRS.2017.2697943 – volume: 13 start-page: 4399 issue: 21 year: 2021 ident: ref45 article-title: Reproducibility of pansharpening methods and quality indexes versus data formats publication-title: Remote Sens. doi: 10.3390/rs13214399 – ident: ref7 doi: 10.1201/b18189 – volume: 16 start-page: 4694 issue: 24 year: 2024 ident: ref21 article-title: Downscaling Land Surface Temperature via assimilation of LandSat 8/9 OLI and TIRS data and hypersharpening publication-title: Remote Sens. doi: 10.3390/rs16244694 – ident: ref52 doi: 10.1109/LGRS.2017.2777916 – ident: ref55 doi: 10.14358/pers.74.2.193 – ident: ref25 doi: 10.1109/TCYB.2019.2951572 – volume: 63 start-page: 691 issue: 6 year: 1997 ident: ref51 article-title: Fusion of satellite images of different spatial resolutions: Assessing the quality of resulting images publication-title: Photogramm. Eng. Remote Sens. – ident: ref27 doi: 10.1109/TCYB.2023.3238200 – ident: ref48 doi: 10.1109/TGRS.2004.837324 – ident: ref47 doi: 10.1109/TIP.2016.2556944 – ident: ref42 doi: 10.1137/1.9781611970937 – ident: ref53 doi: 10.1109/LGRS.2009.2022650 – ident: ref43 doi: 10.1109/TIP.2018.2819501 – ident: ref11 doi: 10.1109/TGRS.2024.3405848 – volume: 17 start-page: 10956 year: 2024 ident: ref20 article-title: Spatial resolution enhancement of satellite hyperspectral data via nested hypersharpening with Sentinel-2 multispectral data publication-title: IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens. doi: 10.1109/JSTARS.2024.3406762 – volume: 16 start-page: 3576 issue: 19 year: 2024 ident: ref44 article-title: Automatic fine co-registration of datasets from extremely high resolution satellite multispectral scanners by means of injection of residues of multivariate regression publication-title: Remote Sens. doi: 10.3390/rs16193576 – ident: ref58 doi: 10.1109/IGARSS.2018.8518869 – ident: ref1 doi: 10.1017/CBO9781316017876 – volume: 16 start-page: 875 issue: 5 year: 2024 ident: ref5 article-title: Spatial resolution enhancement of vegetation indexes via fusion of hyperspectral and multispectral satellite data publication-title: Remote Sens. doi: 10.3390/rs16050875 – ident: ref24 doi: 10.1109/TIP.2018.2836307 – ident: ref10 doi: 10.1109/TGRS.2021.3066425 – ident: ref31 doi: 10.1109/LGRS.2022.3194257 – ident: ref46 doi: 10.1109/TGRS.2019.2906073 – ident: ref29 doi: 10.1109/TGRS.2023.3332176 – ident: ref49 doi: 10.1109/TIP.2015.2458572 – volume: 14 start-page: 6102 year: 2021 ident: ref13 article-title: A benchmarking protocol for pansharpening: Dataset, preprocessing, and quality assessment publication-title: IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens. doi: 10.1109/JSTARS.2021.3086877 – ident: ref40 doi: 10.1109/IGARSS.2005.1525659 – ident: ref35 doi: 10.1109/TIP.2020.3004261 – volume: 3 start-page: 1817 issue: 9 year: 2011 ident: ref2 article-title: Can the future EnMAP mission contribute to urban applications? A literature survey publication-title: Remote Sens. doi: 10.3390/rs3091817 – ident: ref33 doi: 10.1109/TNNLS.2023.3297319 – ident: ref23 doi: 10.1109/TGRS.2011.2161320 – ident: ref56 doi: 10.1117/12.2067770 – ident: ref59 doi: 10.1109/MGRS.2022.3170092 – ident: ref15 doi: 10.1109/WHISPERS.2015.8075412 – start-page: 147 volume-title: Proc. Summaries 3rd Annu. JPL Airborne Geosci. Workshop ident: ref38 article-title: Discrimination among semi-arid landscape endmembers using the Spectral Angle Mapper (SAM) algorithm – volume: 252 year: 2021 ident: ref4 article-title: Advances in hyperspectral remote sensing of vegetation traits and functions publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2020.112121 – ident: ref36 doi: 10.1016/j.inffus.2022.08.032 |
| SSID | ssj0014517 |
| Score | 2.4653473 |
| Snippet | Whenever the sharpening band is not unique, the hypersharpening paradigm extends traditional pansharpening to any <inline-formula> <tex-math notation="LaTeX">m... Whenever the sharpening band is not unique, the hypersharpening paradigm extends traditional pansharpening to any [Formula Omitted]-to-[Formula Omitted] fusion... |
| SourceID | proquest crossref ieee |
| SourceType | Aggregation Database Index Database Publisher |
| StartPage | 1 |
| SubjectTerms | Band spectra Closed form solutions Degradation Environmental Mapping and Analysis Program (EnMAP) hypersharpening Matrix decomposition multiband image fusion Multisensor fusion multivariate linear regression (MLR) Optical imaging optical remote sensing Optical sensors Optimization Pansharpening Parameters Reviews Sentinel-2 (S2) Spatial data Spatial resolution Spectral bands Tensors Training unimodal fusion WorldView-3 (WV-3) |
| Title | Full-Scale Regression Modeling of Spatial Details for Single-/Multiplatform Hypersharpening |
| URI | https://ieeexplore.ieee.org/document/11179947 https://www.proquest.com/docview/3261416111 |
| Volume | 63 |
| WOSCitedRecordID | wos001594874000004&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/eLvHCXMwlV1bS8MwFD7oUNAH7-J0Sh58EuKWNGmaR_G2F4c4hYEPJc0SFWSTdfP3e5J2XhAffCu0aUu-tOf7ziUH4LiQFq1SwalDukGFLxQ1TAwpz1JkR5Ilw9THZhOq18sGA31bF6vHWhjnXEw-c6fhMMbyh2M7C66yNov7lwm1CItKqapY6zNkICSra6NTfJLmdQiTdXT7_vquj1KQy9Mk6B8hfhih2FXl16842per9X--2Qas1USSnFXIb8KCG23B6rftBbdgOaZ32nIbHoPSpH3Ew5E791Tlvo5IaIQWytHJ2JPQmxjXIrmIOaUlQTJL-nju1dH2TZV1aKaB4ZIuStdJ-RwCN8GpsgMPV5f3511at1WglotsSl3qjPEoI1ziUJAYqbXSiGSKYsWgYFDScM24USaTVnNjlLW-g5gZq80wEckuNEbjkdsDIrn3iRQOaVghOsZnmddpwdDqoYxKjG7CyXye87dq94w8qo6OzgMoeQAlr0Fpwk6Y2K8L6zltQmsOTV5_YGWOrJMFbcbY_h_DDmAl3L1yl7SgMZ3M3CEs2ffpSzk5imvnAx_8wcg |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1bS8MwFD7oVNQH7-J0ah58EuKWNGmbR_E2UYe4CYIPJc0SFWSTdfr7PUnrDfHBt0IbUvKlPd-XcwPYy6VBq5RzapFuUOHyhGom-pSnMbIjyaJ-7EKziaTTSe_u1HWVrB5yYay1IfjMHvjL4MvvD82rPyprslC_TCSTMCWF4KxM1_p0GgjJquzoGOdSvHJispZq9s5uuigGuTyIvAIS4ocZCn1Vfv2Mg4U5Xfznuy3BQkUlyWGJ_TJM2MEKzH8rMLgCMyHA0xSrcO-1Ju0iIpbc2Icy-nVAfCs0n5BOho747sS4G8lxiCotCNJZ0sV7z5Y2r8q4Qz32HJe0UbyOikfvuvHHKmtwe3rSO2rTqrECNVykY2pjq7VDIWEji5JES6UShVjGKFc0SoZEaq4Y14lOpVFc68QY10LUtFG6H4loHWqD4cBuAJHcuUgKi0QsFy3t0tSpOGdo91BIRVrVYf9jnbOXsn5GFnRHS2UelMyDklWg1GHNL-zXg9Wa1qHxAU1WfWJFhryTeXXG2OYfw3Zhtt27uswuzzsXWzDnZyoPTxpQG49e7TZMm7fxUzHaCfvoHWAUxQ8 |
| 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=Full-Scale+Regression+Modeling+of+Spatial+Details+for+Single-%2FMultiplatform+Hypersharpening&rft.jtitle=IEEE+transactions+on+geoscience+and+remote+sensing&rft.au=Arienzo%2C+Alberto&rft.au=Garzelli%2C+Andrea&rft.au=Alparone%2C+Luciano&rft.au=Vivone%2C+Gemine&rft.date=2025&rft.issn=0196-2892&rft.eissn=1558-0644&rft.volume=63&rft.spage=1&rft.epage=16&rft_id=info:doi/10.1109%2FTGRS.2025.3614444&rft.externalDBID=n%2Fa&rft.externalDocID=10_1109_TGRS_2025_3614444 |
| 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 |