{"id":"https://openalex.org/W4391923843","doi":"https://doi.org/10.1109/whispers61460.2023.10431020","title":"Advancing Prisma Pansharpening: A Deep Learning Approach with Synthetic Data Pretraining and Transfer Learning","display_name":"Advancing Prisma Pansharpening: A Deep Learning Approach with Synthetic Data Pretraining and Transfer Learning","publication_year":2023,"publication_date":"2023-10-31","ids":{"openalex":"https://openalex.org/W4391923843","doi":"https://doi.org/10.1109/whispers61460.2023.10431020"},"language":"en","primary_location":{"id":"doi:10.1109/whispers61460.2023.10431020","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/whispers61460.2023.10431020","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 13th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5081747078","display_name":"Riccardo Musto","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Riccardo Musto","raw_affiliation_strings":["Leonardo Labs (Leonardo S.p.A.),Rome,Italy,00156"],"affiliations":[{"raw_affiliation_string":"Leonardo Labs (Leonardo S.p.A.),Rome,Italy,00156","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100774114","display_name":"A. Tricomi","orcid":"https://orcid.org/0000-0002-5071-5501"},"institutions":[{"id":"https://openalex.org/I4210119941","display_name":"e GEOS (Italy)","ror":"https://ror.org/02jf95y23","country_code":"IT","type":"company","lineage":["https://openalex.org/I4210119941"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Alessia Tricomi","raw_affiliation_strings":["E-GEOS S.p.A.,Rome,Italy,00156"],"affiliations":[{"raw_affiliation_string":"E-GEOS S.p.A.,Rome,Italy,00156","institution_ids":["https://openalex.org/I4210119941"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5104255337","display_name":"Roberta Bruno","orcid":null},"institutions":[{"id":"https://openalex.org/I4210119941","display_name":"e GEOS (Italy)","ror":"https://ror.org/02jf95y23","country_code":"IT","type":"company","lineage":["https://openalex.org/I4210119941"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Roberta Bruno","raw_affiliation_strings":["E-GEOS S.p.A.,Rome,Italy,00156"],"affiliations":[{"raw_affiliation_string":"E-GEOS S.p.A.,Rome,Italy,00156","institution_ids":["https://openalex.org/I4210119941"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5060647511","display_name":"Giorgio Pasquali","orcid":"https://orcid.org/0000-0002-4029-7430"},"institutions":[{"id":"https://openalex.org/I4210119941","display_name":"e GEOS (Italy)","ror":"https://ror.org/02jf95y23","country_code":"IT","type":"company","lineage":["https://openalex.org/I4210119941"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Giorgio Pasquali","raw_affiliation_strings":["E-GEOS S.p.A.,Rome,Italy,00156"],"affiliations":[{"raw_affiliation_string":"E-GEOS S.p.A.,Rome,Italy,00156","institution_ids":["https://openalex.org/I4210119941"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5081747078"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.325,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.65056469,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"biblio":{"volume":"8","issue":null,"first_page":"1","last_page":"7"},"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.9984999895095825,"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"}},{"id":"https://openalex.org/T11019","display_name":"Image Enhancement Techniques","score":0.9959999918937683,"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/transfer-of-learning","display_name":"Transfer of learning","score":0.7877999544143677},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6929200291633606},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6787700057029724},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5828161835670471},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4980180263519287}],"concepts":[{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.7877999544143677},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6929200291633606},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6787700057029724},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5828161835670471},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4980180263519287}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/whispers61460.2023.10431020","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/whispers61460.2023.10431020","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 13th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W2097117768","https://openalex.org/W2124743705","https://openalex.org/W2142843085","https://openalex.org/W2159269332","https://openalex.org/W2171211028","https://openalex.org/W2171845746","https://openalex.org/W2194775991","https://openalex.org/W2700882095","https://openalex.org/W2777033955","https://openalex.org/W2963442801","https://openalex.org/W3019893222","https://openalex.org/W3097824737","https://openalex.org/W3175432749","https://openalex.org/W3181737610","https://openalex.org/W3213530674","https://openalex.org/W4226507018","https://openalex.org/W4229685799","https://openalex.org/W4250482878","https://openalex.org/W4289515279","https://openalex.org/W4312340363","https://openalex.org/W6685078114","https://openalex.org/W6718563727"],"related_works":["https://openalex.org/W2731899572","https://openalex.org/W2961085424","https://openalex.org/W3215138031","https://openalex.org/W4306674287","https://openalex.org/W4321369474","https://openalex.org/W3009238340","https://openalex.org/W4360585206","https://openalex.org/W4206357785","https://openalex.org/W4281381188","https://openalex.org/W3192840557"],"abstract_inverted_index":{"Hyperspectral":[0],"pansharpening":[1,49,134],"has":[2],"gained":[3],"significant":[4],"importance":[5],"in":[6,84,131,144],"recent":[7],"years":[8],"as":[9],"hyperspectral":[10,23,39,141],"systems":[11],"have":[12],"become":[13],"more":[14],"widely":[15],"available.":[16],"In":[17,41],"order":[18],"to":[19,50,80,90,108,115],"generate":[20],"an":[21],"enhanced":[22,140],"cube":[24],"with":[25,37],"improved":[26],"spatial":[27,97],"and":[28,59,96,103,112],"spectral":[29,95],"resolution,":[30],"this":[31,42],"technique":[32],"fuses":[33],"a":[34,38,55,85],"panchromatic":[35],"image":[36],"cube.":[40],"paper,":[43],"we":[44,71],"investigate":[45],"the":[46,65,82,110,116,120,126,137],"application":[47],"of":[48,67,94,119,128],"PRISMA":[51,104,121],"data":[52,105,142],"by":[53],"proposing":[54],"novel":[56],"training":[57],"approach":[58,130],"deep":[60],"learning":[61,102],"model.":[62],"To":[63],"overcome":[64],"lack":[66],"ground":[68],"truth":[69],"data,":[70,75],"first":[72],"leverage":[73],"synthetic":[74],"generated":[76],"using":[77],"AVIRIS-NG":[78],"imagery,":[79],"pretrain":[81],"model":[83,111],"supervised":[86],"manner,":[87],"enabling":[88],"it":[89,114],"learn":[91],"robust":[92],"representations":[93],"features.":[98],"Subsequently,":[99],"unsupervised":[100],"transfer":[101],"are":[106],"used":[107],"fine-tune":[109],"adapt":[113],"specific":[117],"characteristics":[118],"sensor.":[122],"The":[123],"results":[124],"demonstrate":[125],"effectiveness":[127],"our":[129],"achieving":[132],"promising":[133],"performances,":[135],"paving":[136],"way":[138],"for":[139],"analysis":[143],"various":[145],"remote":[146],"sensing":[147],"applications.":[148]},"counts_by_year":[{"year":2024,"cited_by_count":2}],"updated_date":"2025-12-21T01:58:51.020947","created_date":"2025-10-10T00:00:00"}