{"id":"https://openalex.org/W2038235828","doi":"https://doi.org/10.1109/tgrs.2014.2307573","title":"Spectral Unmixing via Compressive Sensing","display_name":"Spectral Unmixing via Compressive Sensing","publication_year":2014,"publication_date":"2014-03-29","ids":{"openalex":"https://openalex.org/W2038235828","doi":"https://doi.org/10.1109/tgrs.2014.2307573","mag":"2038235828"},"language":"en","primary_location":{"id":"doi:10.1109/tgrs.2014.2307573","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2014.2307573","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/A5014312349","display_name":"Junmin Liu","orcid":"https://orcid.org/0000-0002-1462-7248"},"institutions":[{"id":"https://openalex.org/I87445476","display_name":"Xi'an Jiaotong University","ror":"https://ror.org/017zhmm22","country_code":"CN","type":"education","lineage":["https://openalex.org/I87445476"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Junmin Liu","raw_affiliation_strings":["The School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, China","[Sch. of Math. & Stat., Xi'an Jiaotong Univ., Xi'an, China]"],"affiliations":[{"raw_affiliation_string":"The School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, China","institution_ids":["https://openalex.org/I87445476"]},{"raw_affiliation_string":"[Sch. of Math. & Stat., Xi'an Jiaotong Univ., Xi'an, China]","institution_ids":["https://openalex.org/I87445476"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5111561023","display_name":"Jiangshe Zhang","orcid":null},"institutions":[{"id":"https://openalex.org/I87445476","display_name":"Xi'an Jiaotong University","ror":"https://ror.org/017zhmm22","country_code":"CN","type":"education","lineage":["https://openalex.org/I87445476"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiangshe Zhang","raw_affiliation_strings":["Department of Statistics, School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, China","Dept. of Stat., Xi'an Jiaotong Univ., Xi'an, China"],"affiliations":[{"raw_affiliation_string":"Department of Statistics, School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, China","institution_ids":["https://openalex.org/I87445476"]},{"raw_affiliation_string":"Dept. of Stat., Xi'an Jiaotong Univ., Xi'an, China","institution_ids":["https://openalex.org/I87445476"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5014312349"],"corresponding_institution_ids":["https://openalex.org/I87445476"],"apc_list":null,"apc_paid":null,"fwci":2.523,"has_fulltext":false,"cited_by_count":27,"citation_normalized_percentile":{"value":0.88042483,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":"52","issue":"11","first_page":"7099","last_page":"7110"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"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/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"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/T10689","display_name":"Remote-Sensing Image Classification","score":0.9987000226974487,"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":0.9961000084877014,"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":0.9034948348999023},{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.8544239401817322},{"id":"https://openalex.org/keywords/compressed-sensing","display_name":"Compressed sensing","score":0.7520134449005127},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.5499682426452637},{"id":"https://openalex.org/keywords/matrix","display_name":"Matrix (chemical analysis)","score":0.49904751777648926},{"id":"https://openalex.org/keywords/mixture-model","display_name":"Mixture model","score":0.4914616346359253},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.49051398038864136},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.48650187253952026},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4791552424430847},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.4785761833190918},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.47359994053840637},{"id":"https://openalex.org/keywords/minification","display_name":"Minification","score":0.4548211693763733},{"id":"https://openalex.org/keywords/random-matrix","display_name":"Random matrix","score":0.44247251749038696},{"id":"https://openalex.org/keywords/sparse-matrix","display_name":"Sparse matrix","score":0.4347556233406067},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4009486436843872},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3813095688819885},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.36809730529785156},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.17728835344314575},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.1512155830860138},{"id":"https://openalex.org/keywords/chemistry","display_name":"Chemistry","score":0.09403321146965027},{"id":"https://openalex.org/keywords/eigenvalues-and-eigenvectors","display_name":"Eigenvalues and eigenvectors","score":0.0894700288772583}],"concepts":[{"id":"https://openalex.org/C58237817","wikidata":"https://www.wikidata.org/wiki/Q5376204","display_name":"Endmember","level":3,"score":0.9034948348999023},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.8544239401817322},{"id":"https://openalex.org/C124851039","wikidata":"https://www.wikidata.org/wiki/Q2665459","display_name":"Compressed sensing","level":2,"score":0.7520134449005127},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.5499682426452637},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.49904751777648926},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.4914616346359253},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.49051398038864136},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.48650187253952026},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4791552424430847},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.4785761833190918},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.47359994053840637},{"id":"https://openalex.org/C147764199","wikidata":"https://www.wikidata.org/wiki/Q6865248","display_name":"Minification","level":2,"score":0.4548211693763733},{"id":"https://openalex.org/C64812099","wikidata":"https://www.wikidata.org/wiki/Q176604","display_name":"Random matrix","level":3,"score":0.44247251749038696},{"id":"https://openalex.org/C56372850","wikidata":"https://www.wikidata.org/wiki/Q1050404","display_name":"Sparse matrix","level":3,"score":0.4347556233406067},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4009486436843872},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3813095688819885},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.36809730529785156},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.17728835344314575},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.1512155830860138},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.09403321146965027},{"id":"https://openalex.org/C158693339","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Eigenvalues and eigenvectors","level":2,"score":0.0894700288772583},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","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/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","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},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tgrs.2014.2307573","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2014.2307573","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"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G4279657145","display_name":null,"funder_award_id":"91230101","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G644493519","display_name":null,"funder_award_id":"11201367","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320327583","display_name":"Beijing Center for Mathematics and Information Interdisciplinary Sciences","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":90,"referenced_works":["https://openalex.org/W846821018","https://openalex.org/W1582058549","https://openalex.org/W1592941960","https://openalex.org/W1775879243","https://openalex.org/W1904464160","https://openalex.org/W1964570608","https://openalex.org/W1966798775","https://openalex.org/W1972790666","https://openalex.org/W1975900269","https://openalex.org/W1976615758","https://openalex.org/W1998700518","https://openalex.org/W2010669260","https://openalex.org/W2015418199","https://openalex.org/W2015548667","https://openalex.org/W2016546886","https://openalex.org/W2027717478","https://openalex.org/W2028781966","https://openalex.org/W2032944446","https://openalex.org/W2033529478","https://openalex.org/W2040325979","https://openalex.org/W2050834445","https://openalex.org/W2058532290","https://openalex.org/W2060384859","https://openalex.org/W2063978378","https://openalex.org/W2078204800","https://openalex.org/W2081555128","https://openalex.org/W2084252873","https://openalex.org/W2084724634","https://openalex.org/W2084780416","https://openalex.org/W2090171991","https://openalex.org/W2093059173","https://openalex.org/W2098295833","https://openalex.org/W2099641086","https://openalex.org/W2103955025","https://openalex.org/W2104266187","https://openalex.org/W2105464873","https://openalex.org/W2105877514","https://openalex.org/W2109006918","https://openalex.org/W2109211655","https://openalex.org/W2117731089","https://openalex.org/W2118798900","https://openalex.org/W2119667497","https://openalex.org/W2121058967","https://openalex.org/W2122825543","https://openalex.org/W2125298866","https://openalex.org/W2125678373","https://openalex.org/W2127062304","https://openalex.org/W2129131372","https://openalex.org/W2129638195","https://openalex.org/W2129812935","https://openalex.org/W2130835014","https://openalex.org/W2134033146","https://openalex.org/W2135046866","https://openalex.org/W2135161976","https://openalex.org/W2136235822","https://openalex.org/W2140219630","https://openalex.org/W2142786738","https://openalex.org/W2143500192","https://openalex.org/W2145096794","https://openalex.org/W2145597631","https://openalex.org/W2145889472","https://openalex.org/W2145962650","https://openalex.org/W2146148434","https://openalex.org/W2152751592","https://openalex.org/W2153663612","https://openalex.org/W2154332973","https://openalex.org/W2156401956","https://openalex.org/W2156458885","https://openalex.org/W2157321686","https://openalex.org/W2160547390","https://openalex.org/W2160955696","https://openalex.org/W2163721270","https://openalex.org/W2163886442","https://openalex.org/W2163916252","https://openalex.org/W2164452299","https://openalex.org/W2166207339","https://openalex.org/W2167807229","https://openalex.org/W2169466597","https://openalex.org/W2170929819","https://openalex.org/W2295820431","https://openalex.org/W2296616510","https://openalex.org/W2512514633","https://openalex.org/W2970930377","https://openalex.org/W3022380717","https://openalex.org/W4233760599","https://openalex.org/W4250955649","https://openalex.org/W6623562790","https://openalex.org/W6634825034","https://openalex.org/W6660713223","https://openalex.org/W6682582758"],"related_works":["https://openalex.org/W2136635809","https://openalex.org/W2018409903","https://openalex.org/W2122965290","https://openalex.org/W2051369786","https://openalex.org/W2006559622","https://openalex.org/W4214827030","https://openalex.org/W2963514467","https://openalex.org/W3106244377","https://openalex.org/W2737338842","https://openalex.org/W3005946484"],"abstract_inverted_index":{"The":[0],"recently":[1],"developed":[2],"theory":[3],"of":[4,50,53,91,115,176,180],"compressive":[5],"sensing":[6],"(CS)":[7],"exhibits":[8],"enormous":[9],"potentials":[10],"in":[11,26,112,173],"signal":[12],"recovery.":[13],"In":[14],"this":[15],"paper,":[16],"we":[17,73],"investigate":[18],"its":[19],"application":[20],"on":[21,34,146,165],"spectral":[22,55,108],"unmixing,":[23],"which":[24],"appears":[25],"hyperspectral":[27,151],"data":[28,152],"analysis":[29],"and":[30,86,137,149],"is":[31,46,69,99,110],"usually":[32],"based":[33],"a":[35,43,47,51,70,78,84,102,161],"linear":[36,48],"mixture":[37],"model":[38,98,136,140],"(LMM)":[39],"that":[40,42,68,154],"assumes":[41],"mixed":[44],"pixel":[45],"combination":[49],"set":[52,89],"pure":[54],"signatures":[56],"(called":[57],"endmembers)":[58],"weighted":[59],"by":[60,82,101,132,159],"their":[61],"corresponding":[62],"abundances.":[63,193],"Unlike":[64],"the":[65,92,96,113,118,129,134,155,166,174,178,181,190],"classical":[66],"LMM":[67],"compact":[71],"representation,":[72],"first":[74],"extend":[75],"it":[76],"to":[77,127],"sparse":[79,192],"representation":[80],"(SR)":[81],"using":[83],"redundant":[85],"known":[87],"endmember":[88],"instead":[90],"complete":[93],"one.":[94],"Then,":[95],"SR":[97,135,167],"multiplied":[100],"random":[103,162],"Gaussian":[104,163],"measurement":[105],"matrix,":[106],"so":[107],"unmixing":[109],"casted":[111],"framework":[114],"CS.":[116],"Finally,":[117],"\u2113":[119,182],"<sub":[120,183],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[121,184],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">1</sub>":[122,185],"-minimization":[123,186],"algorithms":[124,187],"are":[125],"used":[126],"recover":[128],"nonnegative":[130,191],"abundances":[131],"solving":[133],"our":[138],"proposed":[139],"named":[141],"CS+SR,":[142],"respectively.":[143],"Experimental":[144],"results":[145],"both":[147],"simulated":[148],"real":[150],"demonstrate":[153],"CS+SR":[156],"model,":[157,168],"formed":[158],"multiplying":[160],"matrix":[164],"can":[169],"improve,":[170],"at":[171],"least":[172],"sense":[175],"probability,":[177],"ability":[179],"for":[188],"recovering":[189]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":6},{"year":2019,"cited_by_count":3},{"year":2018,"cited_by_count":2},{"year":2017,"cited_by_count":3},{"year":2016,"cited_by_count":2},{"year":2015,"cited_by_count":2},{"year":2014,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
