{"id":"https://openalex.org/W2141494774","doi":"https://doi.org/10.1109/tgrs.2011.2160950","title":"Pixel Unmixing in Hyperspectral Data by Means of Neural Networks","display_name":"Pixel Unmixing in Hyperspectral Data by Means of Neural Networks","publication_year":2011,"publication_date":"2011-08-03","ids":{"openalex":"https://openalex.org/W2141494774","doi":"https://doi.org/10.1109/tgrs.2011.2160950","mag":"2141494774"},"language":"en","primary_location":{"id":"doi:10.1109/tgrs.2011.2160950","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2011.2160950","pdf_url":null,"source":{"id":"https://openalex.org/S111326731","display_name":"IEEE Transactions on Geoscience and Remote Sensing","issn_l":"0196-2892","issn":["0196-2892","1558-0644"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Geoscience and Remote Sensing","raw_type":"journal-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/A5078994593","display_name":"Giorgio Licciardi","orcid":"https://orcid.org/0000-0003-4259-919X"},"institutions":[{"id":"https://openalex.org/I106785703","display_name":"Institut polytechnique de Grenoble","ror":"https://ror.org/05sbt2524","country_code":"FR","type":"education","lineage":["https://openalex.org/I106785703","https://openalex.org/I899635006"]},{"id":"https://openalex.org/I116067653","display_name":"University of Rome Tor Vergata","ror":"https://ror.org/02p77k626","country_code":"IT","type":"education","lineage":["https://openalex.org/I116067653"]},{"id":"https://openalex.org/I4210124956","display_name":"GIPSA-Lab","ror":"https://ror.org/02wrme198","country_code":"FR","type":"facility","lineage":["https://openalex.org/I106785703","https://openalex.org/I1294671590","https://openalex.org/I4210124956","https://openalex.org/I899635006","https://openalex.org/I899635006"]}],"countries":["FR","IT"],"is_corresponding":false,"raw_author_name":"Giorgio A. Licciardi","raw_affiliation_strings":["Computer Science, Systems and Production Department, Tor Vergata University, Rome, Italy","GIPSA-Laboratory, Grenoble Institute of Technology, Grenoble, France"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Science, Systems and Production Department, Tor Vergata University, Rome, Italy","institution_ids":["https://openalex.org/I116067653"]},{"raw_affiliation_string":"GIPSA-Laboratory, Grenoble Institute of Technology, Grenoble, France","institution_ids":["https://openalex.org/I106785703","https://openalex.org/I4210124956"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5024242637","display_name":"Fabio Del Frate","orcid":"https://orcid.org/0000-0002-1655-0643"},"institutions":[{"id":"https://openalex.org/I116067653","display_name":"University of Rome Tor Vergata","ror":"https://ror.org/02p77k626","country_code":"IT","type":"education","lineage":["https://openalex.org/I116067653"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Fabio Del Frate","raw_affiliation_strings":["Computer Science, Systems and Production Department, Tor Vergata University, Rome, Italy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Science, Systems and Production Department, Tor Vergata University, Rome, Italy","institution_ids":["https://openalex.org/I116067653"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":14.2109,"has_fulltext":false,"cited_by_count":184,"citation_normalized_percentile":{"value":0.98932489,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":100},"biblio":{"volume":"49","issue":"11","first_page":"4163","last_page":"4172"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","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/T10689","display_name":"Remote-Sensing Image Classification","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/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9962999820709229,"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/T12389","display_name":"Infrared Target Detection Methodologies","score":0.9926000237464905,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"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":0.9082857370376587},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7057941555976868},{"id":"https://openalex.org/keywords/principal-component-analysis","display_name":"Principal component analysis","score":0.6722056269645691},{"id":"https://openalex.org/keywords/imaging-spectrometer","display_name":"Imaging spectrometer","score":0.6441986560821533},{"id":"https://openalex.org/keywords/dimensionality-reduction","display_name":"Dimensionality reduction","score":0.6163837313652039},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6083711385726929},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.55599045753479},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.529076874256134},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.5253267288208008},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.514546811580658},{"id":"https://openalex.org/keywords/endmember","display_name":"Endmember","score":0.5048852562904358},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.48150834441185},{"id":"https://openalex.org/keywords/curse-of-dimensionality","display_name":"Curse of dimensionality","score":0.4728941023349762},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.44012144207954407},{"id":"https://openalex.org/keywords/scanner","display_name":"Scanner","score":0.42256584763526917},{"id":"https://openalex.org/keywords/spectrometer","display_name":"Spectrometer","score":0.4204978048801422},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.34394726157188416},{"id":"https://openalex.org/keywords/optics","display_name":"Optics","score":0.08819049596786499}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.9082857370376587},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7057941555976868},{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.6722056269645691},{"id":"https://openalex.org/C183852935","wikidata":"https://www.wikidata.org/wiki/Q6002848","display_name":"Imaging spectrometer","level":3,"score":0.6441986560821533},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.6163837313652039},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6083711385726929},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.55599045753479},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.529076874256134},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.5253267288208008},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.514546811580658},{"id":"https://openalex.org/C58237817","wikidata":"https://www.wikidata.org/wiki/Q5376204","display_name":"Endmember","level":3,"score":0.5048852562904358},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.48150834441185},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.4728941023349762},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.44012144207954407},{"id":"https://openalex.org/C2779751349","wikidata":"https://www.wikidata.org/wiki/Q1474480","display_name":"Scanner","level":2,"score":0.42256584763526917},{"id":"https://openalex.org/C33390570","wikidata":"https://www.wikidata.org/wiki/Q188463","display_name":"Spectrometer","level":2,"score":0.4204978048801422},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.34394726157188416},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.08819049596786499},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tgrs.2011.2160950","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2011.2160950","pdf_url":null,"source":{"id":"https://openalex.org/S111326731","display_name":"IEEE Transactions on Geoscience and Remote Sensing","issn_l":"0196-2892","issn":["0196-2892","1558-0644"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Geoscience and Remote Sensing","raw_type":"journal-article"},{"id":"pmh:oai:art.torvergata.it:2108/93488","is_oa":false,"landing_page_url":"http://hdl.handle.net/2108/93488","pdf_url":null,"source":{"id":"https://openalex.org/S4306400993","display_name":"Cineca Institutional Research Information System (Tor Vergata University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I116067653","host_organization_name":"University of Rome Tor Vergata","host_organization_lineage":["https://openalex.org/I116067653"],"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":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure","score":0.4399999976158142}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":37,"referenced_works":["https://openalex.org/W846821018","https://openalex.org/W1534053224","https://openalex.org/W1554663460","https://openalex.org/W1620913739","https://openalex.org/W1967329996","https://openalex.org/W1976734386","https://openalex.org/W2012749606","https://openalex.org/W2017257315","https://openalex.org/W2017280280","https://openalex.org/W2041298854","https://openalex.org/W2061280223","https://openalex.org/W2079454091","https://openalex.org/W2092782852","https://openalex.org/W2107120407","https://openalex.org/W2118206198","https://openalex.org/W2120627814","https://openalex.org/W2123909695","https://openalex.org/W2134594501","https://openalex.org/W2136625467","https://openalex.org/W2137983211","https://openalex.org/W2144841545","https://openalex.org/W2151659169","https://openalex.org/W2157621128","https://openalex.org/W2160840673","https://openalex.org/W2166659908","https://openalex.org/W2170548704","https://openalex.org/W2295820431","https://openalex.org/W2998399533","https://openalex.org/W3010636899","https://openalex.org/W3022206565","https://openalex.org/W3146803896","https://openalex.org/W4233760599","https://openalex.org/W4285719527","https://openalex.org/W4388297464","https://openalex.org/W6623562790","https://openalex.org/W6675750237","https://openalex.org/W6777357693"],"related_works":["https://openalex.org/W2037328426","https://openalex.org/W1990914742","https://openalex.org/W2891033441","https://openalex.org/W2006559622","https://openalex.org/W3106536224","https://openalex.org/W2890371384","https://openalex.org/W2051769241","https://openalex.org/W2563324120","https://openalex.org/W2040756827","https://openalex.org/W3045038000"],"abstract_inverted_index":{"Neural":[0],"networks":[1],"(NNs)":[2],"are":[3],"recognized":[4],"as":[5],"very":[6],"effective":[7],"techniques":[8],"when":[9,189],"facing":[10],"complex":[11],"retrieval":[12],"tasks":[13],"in":[14,27,32,167,171,177,182],"remote":[15],"sensing.":[16],"In":[17,35],"this":[18,202],"paper,":[19],"the":[20,29,39,50,54,57,61,65,68,73,82,94,99,117,128,135,172,180,183,199],"potential":[21],"of":[22,47,56,93,98,108,125,169,179,201],"NNs":[23],"has":[24,163],"been":[25,164],"applied":[26],"solving":[28],"unmixing":[30],"problem":[31],"hyperspectral":[33],"data.":[34,95,110],"its":[36],"complete":[37],"form,":[38],"processing":[40],"scheme":[41],"uses":[42],"an":[43],"NN":[44],"architecture":[45],"consisting":[46],"two":[48],"stages:":[49],"first":[51,112],"stage":[52,63],"reduces":[53],"dimension":[55],"input":[58,70],"vector,":[59],"while":[60],"second":[62,122],"performs":[64],"mapping":[66],"from":[67,127],"reduced":[69],"vector":[71],"to":[72],"abundance":[74],"percentages.":[75],"The":[76,96,111,121,148,186],"dimensionality":[77,173],"reduction":[78,174],"is":[79,102,114,151],"performed":[80],"by":[81,116,153,194],"so-called":[83],"autoassociative":[84],"NNs,":[85],"which":[86],"yield":[87],"a":[88],"nonlinear":[89],"principal":[90],"component":[91],"analysis":[92,162],"evaluation":[97],"whole":[100],"performance":[101,161],"carried":[103,165],"out":[104,166],"for":[105,137],"different":[106],"sets":[107],"experimental":[109],"one":[113],"provided":[115],"Airborne":[118,154],"Hyperspectral":[119],"Scanner.":[120],"set":[123,150],"consists":[124],"images":[126],"Compact":[129],"High-Resolution":[130],"Imaging":[131,156],"Spectrometer":[132,157],"on":[133],"board":[134],"Project":[136],"On-Board":[138],"Autonomy":[139],"satellite,":[140],"and":[141,145,176],"it":[142],"includes":[143],"multiangle":[144],"multitemporal":[146],"acquisitions.":[147],"third":[149],"represented":[152],"Visible/InfraRed":[155],"measurements.":[158],"A":[159],"quantitative":[160],"terms":[168,178],"effectiveness":[170],"phase":[175],"accuracy":[181],"final":[184],"estimation.":[185],"results":[187],"obtained,":[188],"compared":[190],"with":[191],"those":[192],"produced":[193],"appropriate":[195],"benchmark":[196],"techniques,":[197],"show":[198],"advantages":[200],"approach.":[203]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":9},{"year":2024,"cited_by_count":14},{"year":2023,"cited_by_count":18},{"year":2022,"cited_by_count":10},{"year":2021,"cited_by_count":19},{"year":2020,"cited_by_count":21},{"year":2019,"cited_by_count":25},{"year":2018,"cited_by_count":10},{"year":2017,"cited_by_count":14},{"year":2016,"cited_by_count":7},{"year":2015,"cited_by_count":8},{"year":2014,"cited_by_count":12},{"year":2013,"cited_by_count":3},{"year":2012,"cited_by_count":11}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
