{"id":"https://openalex.org/W4313506677","doi":"https://doi.org/10.3390/rs15020319","title":"Fast Full-Resolution Target-Adaptive CNN-Based Pansharpening Framework","display_name":"Fast Full-Resolution Target-Adaptive CNN-Based Pansharpening Framework","publication_year":2023,"publication_date":"2023-01-05","ids":{"openalex":"https://openalex.org/W4313506677","doi":"https://doi.org/10.3390/rs15020319"},"language":"en","primary_location":{"id":"doi:10.3390/rs15020319","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs15020319","pdf_url":"https://www.mdpi.com/2072-4292/15/2/319/pdf?version=1673404470","source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Remote Sensing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2072-4292/15/2/319/pdf?version=1673404470","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5091309552","display_name":"Matteo Ciotola","orcid":"https://orcid.org/0000-0001-6577-6879"},"institutions":[{"id":"https://openalex.org/I4210117450","display_name":"Federico II University Hospital","ror":"https://ror.org/02jr6tp70","country_code":"IT","type":"healthcare","lineage":["https://openalex.org/I4210117450","https://openalex.org/I71267560"]}],"countries":["IT"],"is_corresponding":true,"raw_author_name":"Matteo Ciotola","raw_affiliation_strings":["Department of Electrical Engineering and Information Technology (DIETI), University Federico II, 80125 Naples, Italy"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering and Information Technology (DIETI), University Federico II, 80125 Naples, Italy","institution_ids":["https://openalex.org/I4210117450"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5034757970","display_name":"Giuseppe Scarpa","orcid":"https://orcid.org/0000-0001-6458-9107"},"institutions":[{"id":"https://openalex.org/I183638586","display_name":"Parthenope University of Naples","ror":"https://ror.org/05pcv4v03","country_code":"IT","type":"education","lineage":["https://openalex.org/I183638586"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Giuseppe Scarpa","raw_affiliation_strings":["Department of Engineering, University Parthenope, Centro Direzionale ISOLA C4, 80133 Naples, Italy"],"affiliations":[{"raw_affiliation_string":"Department of Engineering, University Parthenope, Centro Direzionale ISOLA C4, 80133 Naples, Italy","institution_ids":["https://openalex.org/I183638586"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5091309552"],"corresponding_institution_ids":["https://openalex.org/I4210117450"],"apc_list":{"value":2500,"currency":"CHF","value_usd":2707},"apc_paid":{"value":2500,"currency":"CHF","value_usd":2707},"fwci":2.9939,"has_fulltext":true,"cited_by_count":19,"citation_normalized_percentile":{"value":0.92014999,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":"15","issue":"2","first_page":"319","last_page":"319"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11659","display_name":"Advanced Image Fusion Techniques","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11659","display_name":"Advanced Image Fusion Techniques","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9970999956130981,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.992900013923645,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8548892736434937},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.658685564994812},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6095064282417297},{"id":"https://openalex.org/keywords/generality","display_name":"Generality","score":0.598535418510437},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5897712707519531},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.49878764152526855},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.4812169373035431},{"id":"https://openalex.org/keywords/adaptation","display_name":"Adaptation (eye)","score":0.4686894714832306},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4366839826107025},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4240805208683014},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.34131917357444763}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8548892736434937},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.658685564994812},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6095064282417297},{"id":"https://openalex.org/C2780767217","wikidata":"https://www.wikidata.org/wiki/Q5532421","display_name":"Generality","level":2,"score":0.598535418510437},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5897712707519531},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.49878764152526855},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.4812169373035431},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.4686894714832306},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4366839826107025},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4240805208683014},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.34131917357444763},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C542102704","wikidata":"https://www.wikidata.org/wiki/Q183257","display_name":"Psychotherapist","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.3390/rs15020319","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs15020319","pdf_url":"https://www.mdpi.com/2072-4292/15/2/319/pdf?version=1673404470","source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Remote Sensing","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:1ebe85b4896e4fe6915437788db585aa","is_oa":true,"landing_page_url":"https://doaj.org/article/1ebe85b4896e4fe6915437788db585aa","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Remote Sensing, Vol 15, Iss 2, p 319 (2023)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/2072-4292/15/2/319/","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/rs15020319","pdf_url":null,"source":{"id":"https://openalex.org/S4306400947","display_name":"MDPI (MDPI AG)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210097602","host_organization_name":"Multidisciplinary Digital Publishing Institute (Switzerland)","host_organization_lineage":["https://openalex.org/I4210097602"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Remote Sensing; Volume 15; Issue 2; Pages: 319","raw_type":"Text"},{"id":"pmh:oai:ricerca.uniparthenope.it:11367/119617","is_oa":false,"landing_page_url":"https://hdl.handle.net/11367/119617","pdf_url":null,"source":{"id":"https://openalex.org/S4377196432","display_name":"CINECA IRIS Institutial research information system (Parthenope University of Naples)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I183638586","host_organization_name":"Parthenope University of Naples","host_organization_lineage":["https://openalex.org/I183638586"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"info:eu-repo/semantics/article"}],"best_oa_location":{"id":"doi:10.3390/rs15020319","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs15020319","pdf_url":"https://www.mdpi.com/2072-4292/15/2/319/pdf?version=1673404470","source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Remote Sensing","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.41999998688697815,"display_name":"Partnerships for the goals","id":"https://metadata.un.org/sdg/17"}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4313506677.pdf"},"referenced_works_count":57,"referenced_works":["https://openalex.org/W1247035941","https://openalex.org/W2000323021","https://openalex.org/W2010515061","https://openalex.org/W2022075948","https://openalex.org/W2034073840","https://openalex.org/W2054440797","https://openalex.org/W2056410627","https://openalex.org/W2058449382","https://openalex.org/W2064366277","https://openalex.org/W2100329651","https://openalex.org/W2106891293","https://openalex.org/W2113338111","https://openalex.org/W2120053475","https://openalex.org/W2123046940","https://openalex.org/W2150630348","https://openalex.org/W2163677711","https://openalex.org/W2165651437","https://openalex.org/W2171108951","https://openalex.org/W2171211028","https://openalex.org/W2172185514","https://openalex.org/W2339428543","https://openalex.org/W2462592242","https://openalex.org/W2560449954","https://openalex.org/W2612425037","https://openalex.org/W2616590213","https://openalex.org/W2619662254","https://openalex.org/W2754256031","https://openalex.org/W2765749804","https://openalex.org/W2766278341","https://openalex.org/W2777033955","https://openalex.org/W2778936354","https://openalex.org/W2792142731","https://openalex.org/W2792365373","https://openalex.org/W2901160789","https://openalex.org/W2921660688","https://openalex.org/W2935896423","https://openalex.org/W2963007295","https://openalex.org/W2963183385","https://openalex.org/W2965814782","https://openalex.org/W3001742927","https://openalex.org/W3041178351","https://openalex.org/W3048064159","https://openalex.org/W3096480538","https://openalex.org/W3096904276","https://openalex.org/W3097824737","https://openalex.org/W3098542449","https://openalex.org/W3172472472","https://openalex.org/W3194146876","https://openalex.org/W3207911243","https://openalex.org/W3212022090","https://openalex.org/W3213530674","https://openalex.org/W4226507018","https://openalex.org/W4288391574","https://openalex.org/W4312447544","https://openalex.org/W6628238970","https://openalex.org/W6684197827","https://openalex.org/W6803670618"],"related_works":["https://openalex.org/W4312417841","https://openalex.org/W4321369474","https://openalex.org/W2731899572","https://openalex.org/W3133861977","https://openalex.org/W4200173597","https://openalex.org/W3116150086","https://openalex.org/W2999805992","https://openalex.org/W4380075502","https://openalex.org/W4291897433","https://openalex.org/W4223943233"],"abstract_inverted_index":{"In":[0,133],"the":[1,32,67,74,118,130,151,182,203,211],"last":[2],"few":[3],"years,":[4],"there":[5],"has":[6,206],"been":[7,49,208],"a":[8,22,39,80,107,113,138,157],"renewed":[9],"interest":[10],"in":[11,16,18,35,85,153],"data":[12],"fusion":[13],"techniques,":[14],"and,":[15,84],"particular,":[17],"pansharpening":[19,47],"due":[20,128],"to":[21,27,100,129,192,215],"paradigm":[23],"shift":[24],"from":[25],"model-based":[26,196],"data-driven":[28],"approaches,":[29],"supported":[30],"by":[31,156],"recent":[33,98],"advances":[34],"deep":[36],"learning.":[37],"Although":[38],"plethora":[40],"of":[41,73,140,159,169,202],"convolutional":[42],"neural":[43],"networks":[44,76],"(CNN)":[45],"for":[46,56,150],"have":[48],"devised,":[50],"some":[51,91],"fundamental":[52],"issues":[53,104],"still":[54],"wait":[55],"answers.":[57],"Among":[58],"these,":[59],"cross-scale":[60],"and":[61,179,197],"cross-datasets":[62],"generalization":[63],"capabilities":[64],"are":[65,77,88],"probably":[66],"most":[68,72],"urgent":[69],"ones":[70],"since":[71],"current":[75],"trained":[78],"at":[79],"different":[81,175,216],"scale":[82],"(reduced-resolution),":[83],"general,":[86],"they":[87],"well-fitted":[89],"on":[90,95,106,161,173],"datasets":[92],"but":[93],"fail":[94],"others.":[96],"A":[97,166],"attempt":[99],"address":[101],"both":[102,195],"these":[103],"leverages":[105],"target-adaptive":[108],"inference":[109,154],"scheme":[110,147],"operating":[111],"with":[112,143],"suitable":[114],"full-resolution":[115],"loss.":[116,165],"On":[117],"downside,":[119],"such":[120],"an":[121,124,144],"approach":[122],"pays":[123],"additional":[125],"computational":[126,183],"overhead":[127],"adaptation":[131,213],"phase.":[132],"this":[134,141],"work,":[135],"we":[136],"propose":[137],"variant":[139],"method":[142],"effective":[145],"target-adaptation":[146],"that":[148],"allows":[149],"reduction":[152],"time":[155],"factor":[158],"ten,":[160],"average,":[162],"without":[163],"accuracy":[164,189],"wide":[167],"set":[168],"experiments":[170],"carried":[171],"out":[172],"three":[174],"datasets,":[176],"GeoEye-1,":[177],"WorldView-2":[178],"WorldView-3,":[180],"prove":[181],"gain":[184],"obtained":[185],"while":[186],"keeping":[187],"top":[188],"scores":[190],"compared":[191],"state-of-the-art":[193],"methods,":[194],"deep-learning":[198],"ones.":[199],"The":[200],"generality":[201],"proposed":[204],"solution":[205],"also":[207],"validated,":[209],"applying":[210],"new":[212],"framework":[214],"CNN":[217],"models.":[218]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":8},{"year":2023,"cited_by_count":5}],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2023-01-06T00:00:00"}