{"id":"https://openalex.org/W3163373600","doi":"https://doi.org/10.1109/icpr48806.2021.9413162","title":"Semi-supervised Deep Learning Techniques for Spectrum Reconstruction","display_name":"Semi-supervised Deep Learning Techniques for Spectrum Reconstruction","publication_year":2021,"publication_date":"2021-01-10","ids":{"openalex":"https://openalex.org/W3163373600","doi":"https://doi.org/10.1109/icpr48806.2021.9413162","mag":"3163373600"},"language":"en","primary_location":{"id":"doi:10.1109/icpr48806.2021.9413162","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr48806.2021.9413162","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 25th International Conference on Pattern Recognition (ICPR)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5020344106","display_name":"Adriano Simonetto","orcid":null},"institutions":[{"id":"https://openalex.org/I138689650","display_name":"University of Padua","ror":"https://ror.org/00240q980","country_code":"IT","type":"education","lineage":["https://openalex.org/I138689650"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Adriano Simonetto","raw_affiliation_strings":["University of Padova"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Padova","institution_ids":["https://openalex.org/I138689650"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070777118","display_name":"Pietro Zanuttigh","orcid":"https://orcid.org/0000-0002-9502-2389"},"institutions":[{"id":"https://openalex.org/I138689650","display_name":"University of Padua","ror":"https://ror.org/00240q980","country_code":"IT","type":"education","lineage":["https://openalex.org/I138689650"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Pietro Zanuttigh","raw_affiliation_strings":["University of Padova"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Padova","institution_ids":["https://openalex.org/I138689650"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056189356","display_name":"Vincent Parret","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Vincent Parret","raw_affiliation_strings":["Sony Europe B.V., Zweigniederlassung Deutschland, Stuttgart Technology Center"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Sony Europe B.V., Zweigniederlassung Deutschland, Stuttgart Technology Center","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033349094","display_name":"Piergiorgio Sartor","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Piergiorgio Sartor","raw_affiliation_strings":["Sony Europe B.V., Zweigniederlassung Deutschland, Stuttgart Technology Center"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Sony Europe B.V., Zweigniederlassung Deutschland, Stuttgart Technology Center","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101091098","display_name":"Alexander Gatto","orcid":"https://orcid.org/0000-0002-0460-3795"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Alexander Gatto","raw_affiliation_strings":["Sony Europe B.V., Zweigniederlassung Deutschland, Stuttgart Technology Center"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Sony Europe B.V., Zweigniederlassung Deutschland, Stuttgart Technology Center","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"7767","last_page":"7774"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9998999834060669,"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/T10689","display_name":"Remote-Sensing Image Classification","score":0.9998999834060669,"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":0.9983999729156494,"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/T11659","display_name":"Advanced Image Fusion Techniques","score":0.998199999332428,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.9587836265563965},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7658975124359131},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7561479806900024},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.7295479774475098},{"id":"https://openalex.org/keywords/rgb-color-model","display_name":"RGB color model","score":0.7260653376579285},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.6260699033737183},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5789738893508911},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.5433706641197205},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5190068483352661},{"id":"https://openalex.org/keywords/scheme","display_name":"Scheme (mathematics)","score":0.4838882386684418},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.4247242212295532},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4212897717952728},{"id":"https://openalex.org/keywords/supervised-learning","display_name":"Supervised learning","score":0.4169241786003113},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.38083386421203613},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.23229482769966125},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1476631462574005}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.9587836265563965},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7658975124359131},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7561479806900024},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.7295479774475098},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.7260653376579285},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.6260699033737183},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5789738893508911},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.5433706641197205},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5190068483352661},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.4838882386684418},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.4247242212295532},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4212897717952728},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.4169241786003113},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.38083386421203613},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.23229482769966125},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1476631462574005},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/icpr48806.2021.9413162","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr48806.2021.9413162","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 25th International Conference on Pattern Recognition (ICPR)","raw_type":"proceedings-article"},{"id":"pmh:oai:www.research.unipd.it:11577/3390553","is_oa":false,"landing_page_url":"http://hdl.handle.net/11577/3390553","pdf_url":null,"source":{"id":"https://openalex.org/S4377196283","display_name":"Research Padua  Archive (University of Padua)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I138689650","host_organization_name":"University of Padua","host_organization_lineage":["https://openalex.org/I138689650"],"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/conferenceObject"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","score":0.7699999809265137,"id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":43,"referenced_works":["https://openalex.org/W8423413","https://openalex.org/W1901129140","https://openalex.org/W1987721959","https://openalex.org/W2005089986","https://openalex.org/W2012946078","https://openalex.org/W2073459066","https://openalex.org/W2092116045","https://openalex.org/W2099471712","https://openalex.org/W2100109944","https://openalex.org/W2138621882","https://openalex.org/W2163886442","https://openalex.org/W2194775991","https://openalex.org/W2221899823","https://openalex.org/W2242218935","https://openalex.org/W2314684259","https://openalex.org/W2401075589","https://openalex.org/W2520430674","https://openalex.org/W2599211382","https://openalex.org/W2604824122","https://openalex.org/W2762818368","https://openalex.org/W2766101120","https://openalex.org/W2766419681","https://openalex.org/W2797755516","https://openalex.org/W2807593488","https://openalex.org/W2892288283","https://openalex.org/W2893739000","https://openalex.org/W2895667927","https://openalex.org/W2899771611","https://openalex.org/W2963945023","https://openalex.org/W2964013315","https://openalex.org/W2964321964","https://openalex.org/W2982374186","https://openalex.org/W4242943569","https://openalex.org/W4299902414","https://openalex.org/W4320013936","https://openalex.org/W6656651650","https://openalex.org/W6668990524","https://openalex.org/W6727116172","https://openalex.org/W6736270120","https://openalex.org/W6745213946","https://openalex.org/W6746466744","https://openalex.org/W6750607774","https://openalex.org/W6756040250"],"related_works":["https://openalex.org/W2072166414","https://openalex.org/W3209970181","https://openalex.org/W2070598848","https://openalex.org/W3034375524","https://openalex.org/W2027399350","https://openalex.org/W2060875994","https://openalex.org/W2044184146","https://openalex.org/W2019190440","https://openalex.org/W3034864990","https://openalex.org/W3171384686"],"abstract_inverted_index":{"State-of-the-art":[0],"approaches":[1],"for":[2,53,157],"the":[3,22,61,81,85,128,158],"estimation":[4,56],"of":[5,24,48,100],"hyperspectral":[6,37,54,62,122],"images":[7,151],"(HSI)":[8],"from":[9],"RGB":[10,86],"data":[11,26,55],"are":[12,29],"mostly":[13],"based":[14,79],"on":[15,80],"deep":[16,50],"learning":[17,51,73,109,136],"techniques":[18,140],"but":[19],"due":[20],"to":[21,31,58,84,112,133,142],"lack":[23],"training":[25],"their":[27],"performances":[28,145],"limited":[30,98],"uncommon":[32],"scenarios":[33],"where":[34],"a":[35,46,72,76,97,106,117,134,154],"large":[36],"database":[38],"is":[39,67],"available.":[40],"In":[41],"this":[42],"work":[43,59,113],"we":[44,70,104,125],"present":[45],"family":[47],"novel":[49,107],"schemes":[52],"able":[57,111],"when":[60],"information":[63],"at":[64],"our":[65],"disposal":[66],"limited.":[68],"Firstly,":[69],"introduce":[71],"scheme":[74,110],"exploiting":[75],"physical":[77],"model":[78],"backward":[82],"mapping":[83],"space":[87],"and":[88],"total":[89],"variation":[90],"regularization":[91],"that":[92,127],"can":[93,130],"be":[94,131],"trained":[95],"with":[96,115,121],"amount":[99],"HSI":[101,150],"images.":[102],"Then,":[103],"propose":[105],"semi-supervised":[108],"even":[114],"just":[116,153],"few":[118,155],"pixels":[119,156],"labeled":[120],"information.":[123],"Finally,":[124],"show":[126],"approach":[129],"extended":[132],"transfer":[135],"scenario.":[137],"The":[138],"proposed":[139],"allow":[141],"reach":[143],"impressive":[144],"while":[146],"requiring":[147],"only":[148],"some":[149],"or":[152],"training.":[159]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}