{"id":"https://openalex.org/W2900692397","doi":"https://doi.org/10.1109/igarss.2018.8518428","title":"Relative Attribute Based Unmixing","display_name":"Relative Attribute Based Unmixing","publication_year":2018,"publication_date":"2018-07-01","ids":{"openalex":"https://openalex.org/W2900692397","doi":"https://doi.org/10.1109/igarss.2018.8518428","mag":"2900692397"},"language":"en","primary_location":{"id":"doi:10.1109/igarss.2018.8518428","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2018.8518428","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium","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/A5053146359","display_name":"Genping Zhao","orcid":"https://orcid.org/0000-0002-3360-1756"},"institutions":[{"id":"https://openalex.org/I139024713","display_name":"Guangdong University of Technology","ror":"https://ror.org/04azbjn80","country_code":"CN","type":"education","lineage":["https://openalex.org/I139024713"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Genping Zhao","raw_affiliation_strings":["School of Computers, Guangdong University and Technology, Guangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computers, Guangdong University and Technology, Guangzhou, China","institution_ids":["https://openalex.org/I139024713"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032362514","display_name":"Lianglun Cheng","orcid":"https://orcid.org/0000-0002-8213-041X"},"institutions":[{"id":"https://openalex.org/I139024713","display_name":"Guangdong University of Technology","ror":"https://ror.org/04azbjn80","country_code":"CN","type":"education","lineage":["https://openalex.org/I139024713"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lianglun Cheng","raw_affiliation_strings":["School of Computers, Guangdong University and Technology, Guangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computers, Guangdong University and Technology, Guangzhou, China","institution_ids":["https://openalex.org/I139024713"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080686591","display_name":"Heng Wu","orcid":"https://orcid.org/0000-0003-0832-2218"},"institutions":[{"id":"https://openalex.org/I139024713","display_name":"Guangdong University of Technology","ror":"https://ror.org/04azbjn80","country_code":"CN","type":"education","lineage":["https://openalex.org/I139024713"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Heng Wu","raw_affiliation_strings":["School of Automation, Guangdong University and Technology, Guangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Automation, Guangdong University and Technology, Guangzhou, China","institution_ids":["https://openalex.org/I139024713"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100712362","display_name":"Hui Li","orcid":"https://orcid.org/0000-0002-3800-5435"},"institutions":[{"id":"https://openalex.org/I139024713","display_name":"Guangdong University of Technology","ror":"https://ror.org/04azbjn80","country_code":"CN","type":"education","lineage":["https://openalex.org/I139024713"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hui Li","raw_affiliation_strings":["School of Computers, Guangdong University and Technology, Guangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computers, Guangdong University and Technology, Guangzhou, China","institution_ids":["https://openalex.org/I139024713"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100353697","display_name":"Xiaolin Li","orcid":"https://orcid.org/0000-0001-5899-5651"},"institutions":[{"id":"https://openalex.org/I18452120","display_name":"Yantai University","ror":"https://ror.org/01rp41m56","country_code":"CN","type":"education","lineage":["https://openalex.org/I18452120"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaolin Li","raw_affiliation_strings":["School of Opto-Electronic Information Science and Technology, Yantai university, Yantai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Opto-Electronic Information Science and Technology, Yantai university, Yantai, China","institution_ids":["https://openalex.org/I18452120"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"2705","last_page":"2708"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9994999766349792,"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":0.9994999766349792,"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/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9908000230789185,"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":0.9538000226020813,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.732737123966217},{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.6706362962722778},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6010628938674927},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5875710844993591},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5736697912216187},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5624987483024597},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.5579720735549927},{"id":"https://openalex.org/keywords/relative-species-abundance","display_name":"Relative species abundance","score":0.5530734658241272},{"id":"https://openalex.org/keywords/relative-value","display_name":"Relative value","score":0.5299991965293884},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.5184728503227234},{"id":"https://openalex.org/keywords/rank","display_name":"Rank (graph theory)","score":0.5063196420669556},{"id":"https://openalex.org/keywords/kullback\u2013leibler-divergence","display_name":"Kullback\u2013Leibler divergence","score":0.47363418340682983},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.43742990493774414},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.366696298122406},{"id":"https://openalex.org/keywords/abundance","display_name":"Abundance (ecology)","score":0.2494315505027771},{"id":"https://openalex.org/keywords/combinatorics","display_name":"Combinatorics","score":0.08014315366744995}],"concepts":[{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.732737123966217},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.6706362962722778},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6010628938674927},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5875710844993591},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5736697912216187},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5624987483024597},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.5579720735549927},{"id":"https://openalex.org/C122325731","wikidata":"https://www.wikidata.org/wiki/Q3738459","display_name":"Relative species abundance","level":3,"score":0.5530734658241272},{"id":"https://openalex.org/C201236551","wikidata":"https://www.wikidata.org/wiki/Q7310809","display_name":"Relative value","level":2,"score":0.5299991965293884},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.5184728503227234},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.5063196420669556},{"id":"https://openalex.org/C171752962","wikidata":"https://www.wikidata.org/wiki/Q255166","display_name":"Kullback\u2013Leibler divergence","level":2,"score":0.47363418340682983},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.43742990493774414},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.366696298122406},{"id":"https://openalex.org/C77077793","wikidata":"https://www.wikidata.org/wiki/Q336019","display_name":"Abundance (ecology)","level":2,"score":0.2494315505027771},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.08014315366744995},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C505870484","wikidata":"https://www.wikidata.org/wiki/Q180538","display_name":"Fishery","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/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss.2018.8518428","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2018.8518428","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.4699999988079071,"display_name":"Quality Education"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W1755724306","https://openalex.org/W2009576740","https://openalex.org/W2039039627","https://openalex.org/W2075875861","https://openalex.org/W2113138229","https://openalex.org/W2294130536","https://openalex.org/W2296762421","https://openalex.org/W2318512420","https://openalex.org/W2470203776","https://openalex.org/W2517982728","https://openalex.org/W2527329788","https://openalex.org/W2528975388","https://openalex.org/W3101195009"],"related_works":["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/W2044184146","https://openalex.org/W4313014865","https://openalex.org/W2019190440","https://openalex.org/W2766422654"],"abstract_inverted_index":{"The":[0,130],"abundance":[1,114,127],"of":[2,6,23,57,67,95,115,121,186],"a":[3,75,81],"mixed":[4,96,116,131,179,190],"pixel":[5,117,191],"certain":[7,72,122],"class":[8,73,123],"can":[9],"be":[10,28],"understood":[11],"as":[12,36,118],"to":[13,19,62,71,111,136],"get":[14],"the":[15,20,44,55,64,113,126,147,183],"relative":[16,109,119],"score":[17,76],"referring":[18],"pure":[21],"representative":[22],"this":[24],"class,":[25],"while":[26],"not":[27],"classified":[29],"with":[30,43,74,128,143,166],"two":[31],"absolute":[32],"and":[33,93,124,176],"discrete":[34],"value":[35,185],"\u201dlor":[37],"0\u201d.":[38],"This":[39],"is":[40,60,78,103,159],"in":[41,50,141],"accordance":[42],"Relative":[45,98],"Attribute":[46,99],"Learning":[47],"(RAL)":[48],"problem":[49,83],"computer":[51],"vision.":[52],"In":[53,155],"RAL,":[54],"concept":[56],"\u201crelative":[58],"attribute\u201d":[59],"used":[61,135],"describe":[63,112],"belonging":[65],"level":[66],"an":[68],"obj":[69],"ect":[70],"which":[77],"achieved":[79,169],"from":[80,170],"learn-to-rank":[82],"using":[84,108],"rankSVM":[85,142],"framework.":[86],"To":[87],"utilize":[88],"information":[89],"between":[90],"data":[91,132],"samples":[92],"even":[94],"pixels,":[97],"based":[100],"Unmixing":[101],"(RAU)":[102],"proposed":[104,187],"first":[105],"time":[106],"by":[107,146,162],"attribute":[110],"purity":[120],"learn":[125],"rankSVM.":[129],"sample":[133],"are":[134],"construct":[137],"training":[138],"comparisons":[139,164],"set":[140,165],"archetypes":[144],"generated":[145],"reported":[148],"Kernel":[149],"Archetypal":[150],"Analysis":[151],"(KAA)":[152],"unmixing":[153],"method.":[154],"addition,":[156],"spectral":[157],"variability":[158],"also":[160],"addressed":[161],"constructing":[163],"synonyms":[167],"spectrum":[168],"KAA.":[171],"Experiments":[172],"on":[173],"both":[174],"synthetic":[175],"real":[177],"hyperspectral":[178],"image":[180],"have":[181],"demonstrated":[182],"potential":[184],"method":[188],"for":[189],"analysis.":[192]},"counts_by_year":[{"year":2021,"cited_by_count":2}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
