{"id":"https://openalex.org/W2889160774","doi":"https://doi.org/10.1109/ssp.2018.8450689","title":"Provably and Roubst Blind Source Seperation of Ill-Conditioned Hyperspectral Mixtures","display_name":"Provably and Roubst Blind Source Seperation of Ill-Conditioned Hyperspectral Mixtures","publication_year":2018,"publication_date":"2018-06-01","ids":{"openalex":"https://openalex.org/W2889160774","doi":"https://doi.org/10.1109/ssp.2018.8450689","mag":"2889160774"},"language":"en","primary_location":{"id":"doi:10.1109/ssp.2018.8450689","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ssp.2018.8450689","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE Statistical Signal Processing Workshop (SSP)","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/A5063704850","display_name":"Chia-Hsiang Lind","orcid":null},"institutions":[{"id":"https://openalex.org/I141596103","display_name":"University of Lisbon","ror":"https://ror.org/01c27hj86","country_code":"PT","type":"education","lineage":["https://openalex.org/I141596103"]}],"countries":["PT"],"is_corresponding":true,"raw_author_name":"Chia-Hsiang Lind","raw_affiliation_strings":["Universidade de Lisboa, Lisboa, Lisboa, PT"],"affiliations":[{"raw_affiliation_string":"Universidade de Lisboa, Lisboa, Lisboa, PT","institution_ids":["https://openalex.org/I141596103"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5017508063","display_name":"Jos\u00e9 M. Bioucas\u2010Dias","orcid":"https://orcid.org/0000-0002-0166-5149"},"institutions":[{"id":"https://openalex.org/I4210120471","display_name":"Instituto de Telecomunica\u00e7\u00f5es","ror":"https://ror.org/02ht4fk33","country_code":"PT","type":"nonprofit","lineage":["https://openalex.org/I4210120471"]},{"id":"https://openalex.org/I141596103","display_name":"University of Lisbon","ror":"https://ror.org/01c27hj86","country_code":"PT","type":"education","lineage":["https://openalex.org/I141596103"]}],"countries":["PT"],"is_corresponding":false,"raw_author_name":"Jose M. Bioucas Dias","raw_affiliation_strings":["Instituto de Telecomunicacoes, Universidade de Lisboa, Portugal"],"affiliations":[{"raw_affiliation_string":"Instituto de Telecomunicacoes, Universidade de Lisboa, Portugal","institution_ids":["https://openalex.org/I4210120471","https://openalex.org/I141596103"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5063704850"],"corresponding_institution_ids":["https://openalex.org/I141596103"],"apc_list":null,"apc_paid":null,"fwci":0.2057,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.57756716,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"53","issue":null,"first_page":"333","last_page":"337"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9994000196456909,"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":0.9994000196456909,"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":0.9972000122070312,"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/T11447","display_name":"Blind Source Separation Techniques","score":0.9908999800682068,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.8432981967926025},{"id":"https://openalex.org/keywords/blind-signal-separation","display_name":"Blind signal separation","score":0.7464374303817749},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5152271389961243},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3459945023059845},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.07438227534294128}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.8432981967926025},{"id":"https://openalex.org/C120317606","wikidata":"https://www.wikidata.org/wiki/Q17105967","display_name":"Blind signal separation","level":3,"score":0.7464374303817749},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5152271389961243},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3459945023059845},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.07438227534294128},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ssp.2018.8450689","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ssp.2018.8450689","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE Statistical Signal Processing Workshop (SSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W1515878031","https://openalex.org/W1755724306","https://openalex.org/W1976615758","https://openalex.org/W1976744214","https://openalex.org/W2048875085","https://openalex.org/W2050041778","https://openalex.org/W2063790512","https://openalex.org/W2070424424","https://openalex.org/W2100556411","https://openalex.org/W2101837437","https://openalex.org/W2105617746","https://openalex.org/W2123031198","https://openalex.org/W2153504150","https://openalex.org/W2157321686","https://openalex.org/W2163886442","https://openalex.org/W2185164352","https://openalex.org/W2245855345","https://openalex.org/W2556090297","https://openalex.org/W2593048869","https://openalex.org/W2742168128","https://openalex.org/W2761752711","https://openalex.org/W2765703469","https://openalex.org/W2768345624","https://openalex.org/W2950143340","https://openalex.org/W2963625764","https://openalex.org/W2964085075","https://openalex.org/W2964217433","https://openalex.org/W2964257934","https://openalex.org/W3099035708","https://openalex.org/W3101195009","https://openalex.org/W3106359998","https://openalex.org/W4231019019","https://openalex.org/W4250658187","https://openalex.org/W6675907514","https://openalex.org/W6730067477","https://openalex.org/W6744636821"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W2072166414","https://openalex.org/W3209970181","https://openalex.org/W2060875994","https://openalex.org/W3034375524","https://openalex.org/W4230131218","https://openalex.org/W2070598848","https://openalex.org/W2404757046","https://openalex.org/W2044184146"],"abstract_inverted_index":{"Given":[0],"a":[1,5,13,105,110,134,156],"hyperspectral":[2,9,63,153],"image":[3],"of":[4,24,39,74,81,120,189,194],"scene,":[6,30],"unsupervised":[7],"linear":[8],"unmixing":[10,192],"(HU)":[11],"is":[12,43,58,68,114,125,162,165,173],"blind":[14],"source":[15],"separation":[16],"problem":[17],"aimed":[18],"at":[19],"identifying":[20],"the":[21,25,29,37,50,54,72,79,89,118,140,151,180,190,195],"spectral":[22,55],"signatures":[23,56],"materials":[26],"present":[27],"in":[28,61,71,97],"termed":[31],"endmembers,":[32],"and,":[33],"for":[34],"each":[35,40],"pixel,":[36],"abundances":[38],"material.":[41],"HU":[42,76],"often":[44,69,95],"very":[45],"challenging,":[46],"namely":[47],"owing":[48],"to":[49,126,175],"high":[51],"correlation":[52],"between":[53],"that":[57,138],"commonly":[59],"observed":[60],"real":[62],"scenes.":[64],"However,":[65],"such":[66,148],"ill-conditioning":[67],"ignored":[70],"design":[73],"existing":[75,84],"methods.":[77],"To":[78],"best":[80],"our":[82],"knowledge,":[83],"preconditioning":[85,112],"techniques":[86],"rely":[87],"on":[88],"pure-pixel":[90],"assumption,":[91],"which":[92,164],"is,":[93],"however,":[94],"violated":[96],"practical":[98],"scenarios.":[99],"In":[100],"this":[101,171],"paper,":[102],"we":[103],"propose":[104],"new":[106],"theoretical":[107],"framework":[108],"wherein":[109],"provable":[111],"method":[113],"developed":[115],"without":[116],"requiring":[117],"presence":[119],"pure":[121],"pixels.":[122],"Our":[123,184],"approach":[124],"identify":[127],"John\u2019s":[128,141],"maximum-volume":[129],"data-inscribed":[130],"ellipsoid,":[131],"followed":[132],"by":[133],"specific":[135],"affine":[136],"transformation":[137,149,172],"maps":[139],"ellipsoid":[142],"into":[143,155],"an":[144],"Euclidean":[145],"ball.":[146],"Provably,":[147],"converts":[150],"original":[152],"vectors":[154],"preconditioned":[157],"dataset":[158],"whose":[159],"endmember":[160],"matrix":[161],"semi-unitary,":[163],"exploited":[166],"as":[167],"regularization":[168],"information.":[169],"Furthermore,":[170],"robust":[174],"noise":[176],"and":[177],"largely":[178],"simplifies":[179],"ensuing":[181],"unmixing/optimization":[182],"procedure.":[183],"numerical":[185],"experiments":[186],"provide":[187],"evidence":[188],"superior":[191],"performance":[193],"proposed":[196],"method.":[197]},"counts_by_year":[{"year":2019,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}