{"id":"https://openalex.org/W3207335568","doi":"https://doi.org/10.1109/igarss47720.2021.9554644","title":"A Union Framework with Sparse Topic Relaxion and Group Clustering for Hyperspectral Unmixing","display_name":"A Union Framework with Sparse Topic Relaxion and Group Clustering for Hyperspectral Unmixing","publication_year":2021,"publication_date":"2021-07-11","ids":{"openalex":"https://openalex.org/W3207335568","doi":"https://doi.org/10.1109/igarss47720.2021.9554644","mag":"3207335568"},"language":"en","primary_location":{"id":"doi:10.1109/igarss47720.2021.9554644","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss47720.2021.9554644","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS","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/A5100348978","display_name":"Linlin Wang","orcid":"https://orcid.org/0000-0001-9619-2577"},"institutions":[{"id":"https://openalex.org/I3124059619","display_name":"China University of Geosciences","ror":"https://ror.org/04gcegc37","country_code":"CN","type":"education","lineage":["https://openalex.org/I3124059619"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Linlin Wang","raw_affiliation_strings":["School of Geography and Information Engineering, China University of Geosciences, Wuhan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Geography and Information Engineering, China University of Geosciences, Wuhan, China","institution_ids":["https://openalex.org/I3124059619"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079862010","display_name":"Qiqi Zhu","orcid":"https://orcid.org/0000-0002-5339-0829"},"institutions":[{"id":"https://openalex.org/I3124059619","display_name":"China University of Geosciences","ror":"https://ror.org/04gcegc37","country_code":"CN","type":"education","lineage":["https://openalex.org/I3124059619"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qiqi Zhu","raw_affiliation_strings":["School of Geography and Information Engineering, China University of Geosciences, Wuhan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Geography and Information Engineering, China University of Geosciences, Wuhan, China","institution_ids":["https://openalex.org/I3124059619"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5114832354","display_name":"Wen Zeng","orcid":"https://orcid.org/0000-0002-1550-7926"},"institutions":[{"id":"https://openalex.org/I3124059619","display_name":"China University of Geosciences","ror":"https://ror.org/04gcegc37","country_code":"CN","type":"education","lineage":["https://openalex.org/I3124059619"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wen Zeng","raw_affiliation_strings":["School of Geography and Information Engineering, China University of Geosciences, Wuhan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Geography and Information Engineering, China University of Geosciences, Wuhan, China","institution_ids":["https://openalex.org/I3124059619"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5063647157","display_name":"Qingfeng Guan","orcid":"https://orcid.org/0000-0002-7392-3709"},"institutions":[{"id":"https://openalex.org/I3124059619","display_name":"China University of Geosciences","ror":"https://ror.org/04gcegc37","country_code":"CN","type":"education","lineage":["https://openalex.org/I3124059619"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qingfeng Guan","raw_affiliation_strings":["School of Geography and Information Engineering, China University of Geosciences, Wuhan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Geography and Information Engineering, China University of Geosciences, Wuhan, China","institution_ids":["https://openalex.org/I3124059619"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I3124059619"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"3837","last_page":"3840"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":1.0,"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":1.0,"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":0.9876999855041504,"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/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9735999703407288,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.9444587230682373},{"id":"https://openalex.org/keywords/endmember","display_name":"Endmember","score":0.8443694114685059},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6217988729476929},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.6039646863937378},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.5417329668998718},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5134459733963013},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.471016526222229},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.4628405272960663},{"id":"https://openalex.org/keywords/topic-model","display_name":"Topic model","score":0.44410890340805054},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.43803805112838745},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4270394444465637},{"id":"https://openalex.org/keywords/statistical-model","display_name":"Statistical model","score":0.4251469373703003},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.4187614619731903},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3389550447463989}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.9444587230682373},{"id":"https://openalex.org/C58237817","wikidata":"https://www.wikidata.org/wiki/Q5376204","display_name":"Endmember","level":3,"score":0.8443694114685059},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6217988729476929},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.6039646863937378},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.5417329668998718},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5134459733963013},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.471016526222229},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.4628405272960663},{"id":"https://openalex.org/C171686336","wikidata":"https://www.wikidata.org/wiki/Q3532085","display_name":"Topic model","level":2,"score":0.44410890340805054},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.43803805112838745},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4270394444465637},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.4251469373703003},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.4187614619731903},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3389550447463989},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"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/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss47720.2021.9554644","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss47720.2021.9554644","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Decent work and economic growth","score":0.5099999904632568,"id":"https://metadata.un.org/sdg/8"}],"awards":[{"id":"https://openalex.org/G6674607238","display_name":null,"funder_award_id":"41901306","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"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W1964570608","https://openalex.org/W1995450389","https://openalex.org/W2009576740","https://openalex.org/W2087263574","https://openalex.org/W2092363223","https://openalex.org/W2103442517","https://openalex.org/W2118718620","https://openalex.org/W2157321686","https://openalex.org/W2163886442","https://openalex.org/W2760901445","https://openalex.org/W2766836613","https://openalex.org/W2806966882","https://openalex.org/W3015126059","https://openalex.org/W3028040906","https://openalex.org/W3106244377","https://openalex.org/W6675660984","https://openalex.org/W6677759377"],"related_works":["https://openalex.org/W2037328426","https://openalex.org/W2891033441","https://openalex.org/W1990914742","https://openalex.org/W3106536224","https://openalex.org/W2890371384","https://openalex.org/W2006559622","https://openalex.org/W2040756827","https://openalex.org/W2138418872","https://openalex.org/W2051769241","https://openalex.org/W2773863718"],"abstract_inverted_index":{"Hyperspectral":[0],"unmixing":[1,119,125],"has":[2,10,37],"been":[3,38],"one":[4],"of":[5,17,25,34,54,71,108,127,137,168],"the":[6,23,26,52,69,72,93,99,106,110,114,118,134,138,142,164,169],"hot":[7],"spots":[8],"and":[9,61,85,152,166],"great":[11],"potential":[12,53],"in":[13,57,160],"a":[14,78],"wide":[15],"range":[16],"hyperspectral":[18,35,150],"applications.":[19],"In":[20,91],"recent":[21],"years,":[22],"employment":[24],"probabilistic":[27],"topic":[28,55,83,101],"model":[29,102],"to":[30,49,132],"acquire":[31],"latent":[32],"topics":[33],"image":[36,59],"an":[39],"effective":[40],"way":[41],"for":[42],"spectral":[43],"unmixing.":[44],"However,":[45],"these":[46,76],"methods":[47,158],"fail":[48],"fully":[50],"exploit":[51],"models":[56,126],"uncovering":[58],"semantics":[60],"need":[62],"extra":[63],"sparsity":[64],"constraints,":[65],"which":[66],"greatly":[67],"increases":[68],"complexity":[70],"model.":[73],"To":[74],"solve":[75],"problems,":[77],"union":[79],"framework":[80],"with":[81,154],"sparse":[82,95,100],"relaxion":[84],"group":[86],"clustering":[87],"(USTRGC)":[88],"is":[89],"proposed.":[90],"USTRGC,":[92],"implied":[94],"prior":[96],"constraints":[97],"by":[98,113],"are":[103,130],"introduced.":[104],"Through":[105],"relaxation":[107],"model,":[109,139],"influence":[111],"caused":[112],"endmember":[115],"variability":[116],"on":[117,147],"accuracy":[120],"can":[121],"be":[122],"alleviated.":[123],"Furthermore,":[124],"different":[128],"category":[129],"united":[131],"alleviate":[133],"ill-posed":[135],"nature":[136],"thereby":[140],"improving":[141],"fractional":[143],"abundance.":[144],"Comprehensive":[145],"evaluations":[146],"two":[148],"real":[149],"images,":[151],"comparisons":[153],"some":[155],"popular":[156],"HU":[157],"available":[159],"existing":[161],"research":[162],"verifies":[163],"effectiveness":[165],"superiority":[167],"proposed":[170],"methods.":[171]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}