{"id":"https://openalex.org/W2010509186","doi":"https://doi.org/10.1109/whispers.2012.6874257","title":"New automated method for estimating the number of endmembers in hyperspectral images","display_name":"New automated method for estimating the number of endmembers in hyperspectral images","publication_year":2012,"publication_date":"2012-06-01","ids":{"openalex":"https://openalex.org/W2010509186","doi":"https://doi.org/10.1109/whispers.2012.6874257","mag":"2010509186"},"language":"en","primary_location":{"id":"doi:10.1109/whispers.2012.6874257","is_oa":false,"landing_page_url":"https://doi.org/10.1109/whispers.2012.6874257","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2012 4th Workshop on Hyperspectral Image and Signal Processing (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/A5035096066","display_name":"Charoula Andreou","orcid":null},"institutions":[{"id":"https://openalex.org/I200777214","display_name":"National and Kapodistrian University of Athens","ror":"https://ror.org/04gnjpq42","country_code":"GR","type":"education","lineage":["https://openalex.org/I200777214"]},{"id":"https://openalex.org/I174458059","display_name":"National Technical University of Athens","ror":"https://ror.org/03cx6bg69","country_code":"GR","type":"education","lineage":["https://openalex.org/I174458059"]}],"countries":["GR"],"is_corresponding":true,"raw_author_name":"C. Andreou","raw_affiliation_strings":["Laboratory of Remote Sensing, National Technical University of Athens, Athens, Greece","Lab. of Remote Sensing, Nat. Tech. Univ. of Athens, Athens, Greece"],"affiliations":[{"raw_affiliation_string":"Laboratory of Remote Sensing, National Technical University of Athens, Athens, Greece","institution_ids":["https://openalex.org/I174458059"]},{"raw_affiliation_string":"Lab. of Remote Sensing, Nat. Tech. Univ. of Athens, Athens, Greece","institution_ids":["https://openalex.org/I200777214"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5073925649","display_name":"Vassilia Karathanassi","orcid":"https://orcid.org/0000-0002-8834-4734"},"institutions":[{"id":"https://openalex.org/I174458059","display_name":"National Technical University of Athens","ror":"https://ror.org/03cx6bg69","country_code":"GR","type":"education","lineage":["https://openalex.org/I174458059"]},{"id":"https://openalex.org/I200777214","display_name":"National and Kapodistrian University of Athens","ror":"https://ror.org/04gnjpq42","country_code":"GR","type":"education","lineage":["https://openalex.org/I200777214"]}],"countries":["GR"],"is_corresponding":false,"raw_author_name":"V. Karathanassi","raw_affiliation_strings":["Laboratory of Remote Sensing, National Technical University of Athens, Athens, Greece","Lab. of Remote Sensing, Nat. Tech. Univ. of Athens, Athens, Greece"],"affiliations":[{"raw_affiliation_string":"Laboratory of Remote Sensing, National Technical University of Athens, Athens, Greece","institution_ids":["https://openalex.org/I174458059"]},{"raw_affiliation_string":"Lab. of Remote Sensing, Nat. Tech. Univ. of Athens, Athens, Greece","institution_ids":["https://openalex.org/I200777214"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5035096066"],"corresponding_institution_ids":["https://openalex.org/I200777214","https://openalex.org/I174458059"],"apc_list":null,"apc_paid":null,"fwci":0.49968169,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.68420127,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"4"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9998000264167786,"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":0.9998000264167786,"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":0.9940999746322632,"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.9914000034332275,"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/endmember","display_name":"Endmember","score":0.9644540548324585},{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.93398118019104},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6213699579238892},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6192985773086548},{"id":"https://openalex.org/keywords/subspace-topology","display_name":"Subspace topology","score":0.6111161112785339},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.589450478553772},{"id":"https://openalex.org/keywords/a-priori-and-a-posteriori","display_name":"A priori and a posteriori","score":0.5415225028991699},{"id":"https://openalex.org/keywords/signal-subspace","display_name":"Signal subspace","score":0.4481394588947296},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.34994006156921387},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.11157718300819397}],"concepts":[{"id":"https://openalex.org/C58237817","wikidata":"https://www.wikidata.org/wiki/Q5376204","display_name":"Endmember","level":3,"score":0.9644540548324585},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.93398118019104},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6213699579238892},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6192985773086548},{"id":"https://openalex.org/C32834561","wikidata":"https://www.wikidata.org/wiki/Q660730","display_name":"Subspace topology","level":2,"score":0.6111161112785339},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.589450478553772},{"id":"https://openalex.org/C75553542","wikidata":"https://www.wikidata.org/wiki/Q178161","display_name":"A priori and a posteriori","level":2,"score":0.5415225028991699},{"id":"https://openalex.org/C2777121530","wikidata":"https://www.wikidata.org/wiki/Q7512739","display_name":"Signal subspace","level":4,"score":0.4481394588947296},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.34994006156921387},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.11157718300819397},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/whispers.2012.6874257","is_oa":false,"landing_page_url":"https://doi.org/10.1109/whispers.2012.6874257","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2012 4th Workshop on Hyperspectral Image and Signal Processing (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":10,"referenced_works":["https://openalex.org/W1968923230","https://openalex.org/W1970673506","https://openalex.org/W2054658115","https://openalex.org/W2070424424","https://openalex.org/W2136625467","https://openalex.org/W2142635246","https://openalex.org/W2147712562","https://openalex.org/W2156958329","https://openalex.org/W2165755981","https://openalex.org/W4299439299"],"related_works":["https://openalex.org/W2037328426","https://openalex.org/W1990914742","https://openalex.org/W2891033441","https://openalex.org/W2006559622","https://openalex.org/W2315521504","https://openalex.org/W3106536224","https://openalex.org/W2890371384","https://openalex.org/W2051769241","https://openalex.org/W2773863718","https://openalex.org/W2563324120"],"abstract_inverted_index":{"Knowing":[0],"the":[1,15,25,28,42,50,63,73,84,90,105,110,117,132,135],"number":[2,43,64,136],"of":[3,27,34,44,65,75,89,116,134,137],"endmembers":[4,45,66,138],"in":[5,32,46,67],"a":[6,10,21,57,76,99],"hyperspectral":[7,68,106],"image":[8],"is":[9,38,70,81,120,139],"prerequisite":[11],"for":[12,24,61,131],"almost":[13],"all":[14],"endmember":[16],"extraction":[17],"algorithms":[18],"and":[19,97,124],"plays":[20],"key":[22],"role":[23],"accuracy":[26],"spectral":[29],"unmixing.":[30],"Moreover,":[31],"case":[33],"data":[35],"compression,":[36],"it":[37],"important":[39],"to":[40,48,108],"know":[41],"order":[47],"define":[49,109],"appropriate":[51],"signal":[52,112],"subspace.":[53,113],"In":[54],"this":[55],"paper,":[56],"new":[58],"automated":[59],"method":[60,80,119],"estimating":[62],"imagery":[69],"proposed,":[71],"without":[72],"need":[74],"priori":[77],"knowledge.":[78],"The":[79,114],"based":[82],"on":[83],"intra-band":[85],"standard":[86],"deviation":[87],"values":[88],"transformed":[91],"components":[92],"produced":[93],"by":[94],"eigen-based":[95],"decomposition,":[96],"uses":[98],"fixed":[100],"threshold":[101],"-the":[102],"same":[103],"regardless":[104],"dataset-":[107],"optimum":[111],"effectiveness":[115],"proposed":[118],"shown":[121],"using":[122],"synthetic":[123],"real":[125],"data.":[126],"Comparison":[127],"with":[128],"state-of-the-art":[129],"methods":[130],"estimation":[133],"also":[140],"performed.":[141]},"counts_by_year":[{"year":2018,"cited_by_count":1},{"year":2013,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
