{"id":"https://openalex.org/W2765328230","doi":"https://doi.org/10.1109/whispers.2014.8077560","title":"A variational Bayes algorithm for joint-sparse abundance estimation","display_name":"A variational Bayes algorithm for joint-sparse abundance estimation","publication_year":2014,"publication_date":"2014-06-01","ids":{"openalex":"https://openalex.org/W2765328230","doi":"https://doi.org/10.1109/whispers.2014.8077560","mag":"2765328230"},"language":"en","primary_location":{"id":"doi:10.1109/whispers.2014.8077560","is_oa":false,"landing_page_url":"https://doi.org/10.1109/whispers.2014.8077560","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5075524987","display_name":"Paris V. Giampouras","orcid":"https://orcid.org/0000-0003-2039-0758"},"institutions":[{"id":"https://openalex.org/I4210118731","display_name":"National Observatory of Athens","ror":"https://ror.org/03dtebk39","country_code":"GR","type":"facility","lineage":["https://openalex.org/I4210118731"]}],"countries":["GR"],"is_corresponding":false,"raw_author_name":"Paris V. Giampouras","raw_affiliation_strings":["IAASARS, National Observatory of Athens, Penteli, Greece"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IAASARS, National Observatory of Athens, Penteli, Greece","institution_ids":["https://openalex.org/I4210118731"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076527758","display_name":"Konstantinos E. Themelis","orcid":"https://orcid.org/0000-0002-0090-4312"},"institutions":[{"id":"https://openalex.org/I4210118731","display_name":"National Observatory of Athens","ror":"https://ror.org/03dtebk39","country_code":"GR","type":"facility","lineage":["https://openalex.org/I4210118731"]}],"countries":["GR"],"is_corresponding":false,"raw_author_name":"Konstantinos E. Themelis","raw_affiliation_strings":["IAASARS, National Observatory of Athens, Penteli, Greece"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IAASARS, National Observatory of Athens, Penteli, Greece","institution_ids":["https://openalex.org/I4210118731"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081131282","display_name":"Athanasios A. Rontogiannis","orcid":"https://orcid.org/0000-0001-6161-4055"},"institutions":[{"id":"https://openalex.org/I4210118731","display_name":"National Observatory of Athens","ror":"https://ror.org/03dtebk39","country_code":"GR","type":"facility","lineage":["https://openalex.org/I4210118731"]}],"countries":["GR"],"is_corresponding":false,"raw_author_name":"Athanasios A. Rontogiannis","raw_affiliation_strings":["IAASARS, National Observatory of Athens, Penteli, Greece"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IAASARS, National Observatory of Athens, Penteli, Greece","institution_ids":["https://openalex.org/I4210118731"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5014467125","display_name":"Konstantinos Koutroumbas","orcid":"https://orcid.org/0000-0002-8480-1539"},"institutions":[{"id":"https://openalex.org/I4210118731","display_name":"National Observatory of Athens","ror":"https://ror.org/03dtebk39","country_code":"GR","type":"facility","lineage":["https://openalex.org/I4210118731"]}],"countries":["GR"],"is_corresponding":false,"raw_author_name":"Konstantinos D. Koutroumbas","raw_affiliation_strings":["IAASARS, National Observatory of Athens, Penteli, Greece"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IAASARS, National Observatory of Athens, Penteli, Greece","institution_ids":["https://openalex.org/I4210118731"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4210118731"],"apc_list":null,"apc_paid":null,"fwci":1.0271,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.77417174,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"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":0.9998999834060669,"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/T13890","display_name":"Remote Sensing and Land Use","score":0.9927999973297119,"subfield":{"id":"https://openalex.org/subfields/1902","display_name":"Atmospheric Science"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10111","display_name":"Remote Sensing in Agriculture","score":0.9891999959945679,"subfield":{"id":"https://openalex.org/subfields/2303","display_name":"Ecology"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental 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":0.7282148599624634},{"id":"https://openalex.org/keywords/bayes-theorem","display_name":"Bayes' theorem","score":0.7169601917266846},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.612361490726471},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.6056913137435913},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6025360226631165},{"id":"https://openalex.org/keywords/joint","display_name":"Joint (building)","score":0.6009220480918884},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.5839157700538635},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5742889046669006},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5586663484573364},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5359469652175903},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5053780674934387},{"id":"https://openalex.org/keywords/bayesian-hierarchical-modeling","display_name":"Bayesian hierarchical modeling","score":0.43742451071739197},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3292611241340637},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.06545761227607727}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.7282148599624634},{"id":"https://openalex.org/C207201462","wikidata":"https://www.wikidata.org/wiki/Q182505","display_name":"Bayes' theorem","level":3,"score":0.7169601917266846},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.612361490726471},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.6056913137435913},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6025360226631165},{"id":"https://openalex.org/C18555067","wikidata":"https://www.wikidata.org/wiki/Q8375051","display_name":"Joint (building)","level":2,"score":0.6009220480918884},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.5839157700538635},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5742889046669006},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5586663484573364},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5359469652175903},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5053780674934387},{"id":"https://openalex.org/C191413810","wikidata":"https://www.wikidata.org/wiki/Q17100952","display_name":"Bayesian hierarchical modeling","level":4,"score":0.43742451071739197},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3292611241340637},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.06545761227607727},{"id":"https://openalex.org/C170154142","wikidata":"https://www.wikidata.org/wiki/Q150737","display_name":"Architectural engineering","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/whispers.2014.8077560","is_oa":false,"landing_page_url":"https://doi.org/10.1109/whispers.2014.8077560","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.712.1041","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.712.1041","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://members.noa.gr/tronto/whispers2014.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":6,"referenced_works":["https://openalex.org/W2015548667","https://openalex.org/W2063790512","https://openalex.org/W2157321686","https://openalex.org/W2163721270","https://openalex.org/W2166864699","https://openalex.org/W2766914577"],"related_works":["https://openalex.org/W4385388142","https://openalex.org/W4388293756","https://openalex.org/W1570450443","https://openalex.org/W3034774545","https://openalex.org/W4328114192","https://openalex.org/W93579797","https://openalex.org/W2353147637","https://openalex.org/W2035819464","https://openalex.org/W2789738696","https://openalex.org/W4302308957"],"abstract_inverted_index":{"This":[0],"paper":[1],"presents":[2],"a":[3,24,30,43],"variational":[4,46],"Bayesian":[5,26,53],"scheme":[6],"for":[7],"semi-supervised":[8],"unmixing":[9],"on":[10,33,66],"hyperspectral":[11],"images":[12],"that":[13,28],"exploits":[14],"the":[15,34,58],"inherent":[16],"spatial":[17],"correlation":[18],"between":[19],"neighboring":[20],"pixels.":[21],"More":[22],"specifically,":[23],"hierarchical":[25],"model":[27,61],"promotes":[29],"joint-sparse":[31,60],"profile":[32],"abundance":[35],"vectors":[36],"of":[37,57],"adjacent":[38],"pixels":[39],"is":[40,49],"developed":[41],"and":[42,69],"computationally":[44],"efficient":[45],"Bayes":[47],"algorithm":[48],"incorporated":[50],"to":[51],"perform":[52],"inference.":[54],"The":[55],"benefits":[56],"proposed":[59],"are":[62],"demonstrated":[63],"via":[64],"simulations":[65],"both":[67],"synthetic":[68],"real":[70],"data.":[71]},"counts_by_year":[{"year":2017,"cited_by_count":1},{"year":2015,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
