{"id":"https://openalex.org/W2167408677","doi":"https://doi.org/10.1109/igarss.2012.6350412","title":"An approach to fully unsupervised hyperspectral unmixing","display_name":"An approach to fully unsupervised hyperspectral unmixing","publication_year":2012,"publication_date":"2012-07-01","ids":{"openalex":"https://openalex.org/W2167408677","doi":"https://doi.org/10.1109/igarss.2012.6350412","mag":"2167408677"},"language":"en","primary_location":{"id":"doi:10.1109/igarss.2012.6350412","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2012.6350412","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2012 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"http://publica.fraunhofer.de/documents/N-217257.html","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5031865300","display_name":"Wolfgang Gro\u00df","orcid":"https://orcid.org/0000-0002-8430-0734"},"institutions":[{"id":"https://openalex.org/I4210111500","display_name":"Fraunhofer Institute of Optronics, System Technologies and Image Exploitation","ror":"https://ror.org/01zx97922","country_code":"DE","type":"facility","lineage":["https://openalex.org/I4210111500","https://openalex.org/I4923324"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Wolfgang Gross","raw_affiliation_strings":["Fraunhofer Institute of Optronics, System Technologies and Image Exploitation (IOSB), Gutleuthausstr. 1, 76275 Ettlingen, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fraunhofer Institute of Optronics, System Technologies and Image Exploitation (IOSB), Gutleuthausstr. 1, 76275 Ettlingen, Germany","institution_ids":["https://openalex.org/I4210111500"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5091134997","display_name":"Hendrik Schilling","orcid":"https://orcid.org/0000-0001-9046-8931"},"institutions":[{"id":"https://openalex.org/I4210111500","display_name":"Fraunhofer Institute of Optronics, System Technologies and Image Exploitation","ror":"https://ror.org/01zx97922","country_code":"DE","type":"facility","lineage":["https://openalex.org/I4210111500","https://openalex.org/I4923324"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Hendrik Schilling","raw_affiliation_strings":["Fraunhofer Institute of Optronics, System Technologies and Image Exploitation (IOSB), Gutleuthausstr. 1, 76275 Ettlingen, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fraunhofer Institute of Optronics, System Technologies and Image Exploitation (IOSB), Gutleuthausstr. 1, 76275 Ettlingen, Germany","institution_ids":["https://openalex.org/I4210111500"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5075370224","display_name":"Wolfgang Middelmann","orcid":"https://orcid.org/0000-0002-3507-7396"},"institutions":[{"id":"https://openalex.org/I4210111500","display_name":"Fraunhofer Institute of Optronics, System Technologies and Image Exploitation","ror":"https://ror.org/01zx97922","country_code":"DE","type":"facility","lineage":["https://openalex.org/I4210111500","https://openalex.org/I4923324"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Wolfgang Middelmann","raw_affiliation_strings":["Fraunhofer Institute of Optronics, System Technologies and Image Exploitation (IOSB), Gutleuthausstr. 1, 76275 Ettlingen, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fraunhofer Institute of Optronics, System Technologies and Image Exploitation (IOSB), Gutleuthausstr. 1, 76275 Ettlingen, Germany","institution_ids":["https://openalex.org/I4210111500"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.5209,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.73308895,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"4714","last_page":"4717"},"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/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.9919000267982483,"subfield":{"id":"https://openalex.org/subfields/1602","display_name":"Analytical Chemistry"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12389","display_name":"Infrared Target Detection Methodologies","score":0.9821000099182129,"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":0.8936375379562378},{"id":"https://openalex.org/keywords/initialization","display_name":"Initialization","score":0.7687073945999146},{"id":"https://openalex.org/keywords/endmember","display_name":"Endmember","score":0.7319841980934143},{"id":"https://openalex.org/keywords/non-negative-matrix-factorization","display_name":"Non-negative matrix factorization","score":0.6715732216835022},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6248525381088257},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5799678564071655},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5210798978805542},{"id":"https://openalex.org/keywords/multiplicative-function","display_name":"Multiplicative function","score":0.4286121726036072},{"id":"https://openalex.org/keywords/matrix-decomposition","display_name":"Matrix decomposition","score":0.39841926097869873},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.24859315156936646}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.8936375379562378},{"id":"https://openalex.org/C114466953","wikidata":"https://www.wikidata.org/wiki/Q6034165","display_name":"Initialization","level":2,"score":0.7687073945999146},{"id":"https://openalex.org/C58237817","wikidata":"https://www.wikidata.org/wiki/Q5376204","display_name":"Endmember","level":3,"score":0.7319841980934143},{"id":"https://openalex.org/C152671427","wikidata":"https://www.wikidata.org/wiki/Q10843505","display_name":"Non-negative matrix factorization","level":4,"score":0.6715732216835022},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6248525381088257},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5799678564071655},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5210798978805542},{"id":"https://openalex.org/C42747912","wikidata":"https://www.wikidata.org/wiki/Q1048447","display_name":"Multiplicative function","level":2,"score":0.4286121726036072},{"id":"https://openalex.org/C42355184","wikidata":"https://www.wikidata.org/wiki/Q1361088","display_name":"Matrix decomposition","level":3,"score":0.39841926097869873},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.24859315156936646},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C158693339","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Eigenvalues and eigenvectors","level":2,"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/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/igarss.2012.6350412","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2012.6350412","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2012 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-article"},{"id":"pmh:oai:fraunhofer.de:N-217257","is_oa":true,"landing_page_url":"http://publica.fraunhofer.de/documents/N-217257.html","pdf_url":null,"source":{"id":"https://openalex.org/S4306400318","display_name":"Fraunhofer-Publica (Fraunhofer-Gesellschaft)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4923324","host_organization_name":"Fraunhofer-Gesellschaft","host_organization_lineage":["https://openalex.org/I4923324"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Fraunhofer IOSB","raw_type":"Conference Paper"},{"id":"pmh:oai:publica.fraunhofer.de:publica/376694","is_oa":false,"landing_page_url":"https://publica.fraunhofer.de/handle/publica/376694","pdf_url":null,"source":{"id":"https://openalex.org/S4306400318","display_name":"Fraunhofer-Publica (Fraunhofer-Gesellschaft)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4923324","host_organization_name":"Fraunhofer-Gesellschaft","host_organization_lineage":["https://openalex.org/I4923324"],"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":"conference paper"}],"best_oa_location":{"id":"pmh:oai:fraunhofer.de:N-217257","is_oa":true,"landing_page_url":"http://publica.fraunhofer.de/documents/N-217257.html","pdf_url":null,"source":{"id":"https://openalex.org/S4306400318","display_name":"Fraunhofer-Publica (Fraunhofer-Gesellschaft)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4923324","host_organization_name":"Fraunhofer-Gesellschaft","host_organization_lineage":["https://openalex.org/I4923324"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Fraunhofer IOSB","raw_type":"Conference Paper"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":7,"referenced_works":["https://openalex.org/W1973797280","https://openalex.org/W2002519017","https://openalex.org/W2099301440","https://openalex.org/W2120627814","https://openalex.org/W2135029798","https://openalex.org/W2165755981","https://openalex.org/W6907664265"],"related_works":["https://openalex.org/W1980988957","https://openalex.org/W2089298795","https://openalex.org/W2101428145","https://openalex.org/W2773002387","https://openalex.org/W2766484909","https://openalex.org/W2802800261","https://openalex.org/W2163867257","https://openalex.org/W1992367803","https://openalex.org/W2059578032","https://openalex.org/W2898368675"],"abstract_inverted_index":{"In":[0,90],"the":[1,42,53,64,77,87,118,125,137,147,153,184],"last":[2],"few":[3],"years,":[4],"unmixing":[5,46,143,161],"of":[6,11,22,27,34,36,108,136,183],"hyperspectral":[7,180],"data":[8,51,181],"has":[9],"become":[10],"major":[12],"importance.":[13],"The":[14,141,174],"high":[15],"spectral":[16,104],"resolution":[17],"results":[18],"in":[19],"a":[20,83,163,168],"loss":[21],"spatial":[23],"resolution.":[24],"Thus,":[25],"spectra":[26],"edges":[28],"and":[29,70,166,170,189],"small":[30],"objects":[31],"are":[32,95],"composed":[33],"mixtures":[35],"their":[37,71],"neighboring":[38],"materials.":[39],"Due":[40],"to":[41,61,97,133,159],"fact":[43],"that":[44],"supervised":[45],"is":[47,59,81,111,131,144,157],"impossible":[48],"for":[49,101,122],"extensive":[50],"sets,":[52],"unsupervised":[54,103],"Nonnegative":[55],"Matrix":[56],"Factorization":[57],"(NMF)":[58],"used":[60],"automatically":[62],"determine":[63],"pure":[65],"materials,":[66],"so":[67],"called":[68],"endmembers,":[69],"abundances":[72],"per":[73],"sample":[74],"[1].":[75],"As":[76],"underlying":[78],"optimization":[79],"problem":[80],"nonlinear,":[82],"good":[84],"initialization":[85,113],"improves":[86],"outcome":[88],"[2].":[89],"this":[91,109],"paper,":[92],"several":[93],"methods":[94],"combined":[96],"create":[98],"an":[99,112],"algorithm":[100,155,175],"fully":[102],"unmixing.":[105],"Major":[106],"part":[107],"paper":[110],"method,":[114],"which":[115],"iteratively":[116],"calculates":[117],"best":[119],"possible":[120,158],"candidates":[121],"endmembers":[123],"among":[124],"measured":[126],"data.":[127],"A":[128],"termination":[129],"condition":[130],"applied":[132],"prevent":[134],"violations":[135],"linear":[138],"mixture":[139],"model.":[140],"actual":[142],"performed":[145],"by":[146],"multiplicative":[148],"update":[149],"from":[150],"[3].":[151],"Using":[152],"proposed":[154],"it":[156],"perform":[160],"without":[162],"priori":[164],"studies":[165],"accomplish":[167],"sparse":[169],"easily":[171],"interpretable":[172],"solution.":[173],"was":[176],"tested":[177],"on":[178],"different":[179],"sets":[182],"sensor":[185],"types":[186],"AISA":[187,190],"Hawk":[188],"Eagle.":[191]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2017,"cited_by_count":1},{"year":2015,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}