{"id":"https://openalex.org/W2062822804","doi":"https://doi.org/10.1109/tgrs.2014.2347343","title":"Active Learning With Gaussian Process Classifier for Hyperspectral Image Classification","display_name":"Active Learning With Gaussian Process Classifier for Hyperspectral Image Classification","publication_year":2014,"publication_date":"2014-08-29","ids":{"openalex":"https://openalex.org/W2062822804","doi":"https://doi.org/10.1109/tgrs.2014.2347343","mag":"2062822804"},"language":"en","primary_location":{"id":"doi:10.1109/tgrs.2014.2347343","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2014.2347343","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/A5009814550","display_name":"Shujin Sun","orcid":null},"institutions":[{"id":"https://openalex.org/I170215575","display_name":"National University of Defense Technology","ror":"https://ror.org/05d2yfz11","country_code":"CN","type":"education","lineage":["https://openalex.org/I170215575"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Shujin Sun","raw_affiliation_strings":["Science and Technology on Automatic Target Recognition Laboratory, School of Electronic Science and Engineering, National University of Defense Technology, Changsha, China","Sci. & Technol. on Autom. Target Recognition Lab., Nat. Univ. of Defense Technol., Changsha, , China"],"affiliations":[{"raw_affiliation_string":"Science and Technology on Automatic Target Recognition Laboratory, School of Electronic Science and Engineering, National University of Defense Technology, Changsha, China","institution_ids":["https://openalex.org/I170215575"]},{"raw_affiliation_string":"Sci. & Technol. on Autom. Target Recognition Lab., Nat. Univ. of Defense Technol., Changsha, , China","institution_ids":["https://openalex.org/I170215575"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079556851","display_name":"Ping Zhong","orcid":"https://orcid.org/0000-0002-8686-3928"},"institutions":[{"id":"https://openalex.org/I170215575","display_name":"National University of Defense Technology","ror":"https://ror.org/05d2yfz11","country_code":"CN","type":"education","lineage":["https://openalex.org/I170215575"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ping Zhong","raw_affiliation_strings":["Science and Technology on Automatic Target Recognition Laboratory, School of Electronic Science and Engineering, National University of Defense Technology, Changsha, China","Sci. & Technol. on Autom. Target Recognition Lab., Nat. Univ. of Defense Technol., Changsha, , China"],"affiliations":[{"raw_affiliation_string":"Science and Technology on Automatic Target Recognition Laboratory, School of Electronic Science and Engineering, National University of Defense Technology, Changsha, China","institution_ids":["https://openalex.org/I170215575"]},{"raw_affiliation_string":"Sci. & Technol. on Autom. Target Recognition Lab., Nat. Univ. of Defense Technol., Changsha, , China","institution_ids":["https://openalex.org/I170215575"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101702108","display_name":"Huaitie Xiao","orcid":"https://orcid.org/0009-0005-4683-0952"},"institutions":[{"id":"https://openalex.org/I170215575","display_name":"National University of Defense Technology","ror":"https://ror.org/05d2yfz11","country_code":"CN","type":"education","lineage":["https://openalex.org/I170215575"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Huaitie Xiao","raw_affiliation_strings":["Science and Technology on Automatic Target Recognition Laboratory, School of Electronic Science and Engineering, National University of Defense Technology, Changsha, China","Sci. & Technol. on Autom. Target Recognition Lab., Nat. Univ. of Defense Technol., Changsha, , China"],"affiliations":[{"raw_affiliation_string":"Science and Technology on Automatic Target Recognition Laboratory, School of Electronic Science and Engineering, National University of Defense Technology, Changsha, China","institution_ids":["https://openalex.org/I170215575"]},{"raw_affiliation_string":"Sci. & Technol. on Autom. Target Recognition Lab., Nat. Univ. of Defense Technol., Changsha, , China","institution_ids":["https://openalex.org/I170215575"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5002760019","display_name":"Runsheng Wang","orcid":"https://orcid.org/0000-0002-7514-0767"},"institutions":[{"id":"https://openalex.org/I170215575","display_name":"National University of Defense Technology","ror":"https://ror.org/05d2yfz11","country_code":"CN","type":"education","lineage":["https://openalex.org/I170215575"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Runsheng Wang","raw_affiliation_strings":["Science and Technology on Automatic Target Recognition Laboratory, School of Electronic Science and Engineering, National University of Defense Technology, Changsha, China","Sci. & Technol. on Autom. Target Recognition Lab., Nat. Univ. of Defense Technol., Changsha, , China"],"affiliations":[{"raw_affiliation_string":"Science and Technology on Automatic Target Recognition Laboratory, School of Electronic Science and Engineering, National University of Defense Technology, Changsha, China","institution_ids":["https://openalex.org/I170215575"]},{"raw_affiliation_string":"Sci. & Technol. on Autom. Target Recognition Lab., Nat. Univ. of Defense Technol., Changsha, , China","institution_ids":["https://openalex.org/I170215575"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5009814550"],"corresponding_institution_ids":["https://openalex.org/I170215575"],"apc_list":null,"apc_paid":null,"fwci":9.8576,"has_fulltext":false,"cited_by_count":85,"citation_normalized_percentile":{"value":0.9817094,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":95,"max":99},"biblio":{"volume":"53","issue":"4","first_page":"1746","last_page":"1760"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9993000030517578,"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":0.9993000030517578,"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/T11667","display_name":"Advanced Chemical Sensor Technologies","score":0.9922999739646912,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"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":0.973800003528595,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.7659263610839844},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.706315279006958},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7012945413589478},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.5975943207740784},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5886210203170776},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5821106433868408},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5795776844024658},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.5006532669067383},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.4500390589237213},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.4167725443840027},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.3334960341453552},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.16208037734031677}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.7659263610839844},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.706315279006958},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7012945413589478},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.5975943207740784},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5886210203170776},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5821106433868408},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5795776844024658},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.5006532669067383},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.4500390589237213},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.4167725443840027},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.3334960341453552},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.16208037734031677},{"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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tgrs.2014.2347343","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2014.2347343","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/G2738041069","display_name":null,"funder_award_id":"61372159","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G2805227048","display_name":null,"funder_award_id":"61271439","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G4511917825","display_name":null,"funder_award_id":"201243","funder_id":"https://openalex.org/F4320321769","funder_display_name":"Foundation for the Author of National Excellent Doctoral Dissertation of the People's Republic of China"},{"id":"https://openalex.org/G5532028406","display_name":null,"funder_award_id":"NECT-13-0164","funder_id":"https://openalex.org/F4320334924","funder_display_name":"Program for New Century Excellent Talents in University"}],"funders":[{"id":"https://openalex.org/F4320309036","display_name":"Purdue University","ror":"https://ror.org/02dqehb95"},{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320321769","display_name":"Foundation for the Author of National Excellent Doctoral Dissertation of the People's Republic of China","ror":null},{"id":"https://openalex.org/F4320322874","display_name":"Universit\u00e0 degli Studi di Pavia","ror":"https://ror.org/00s6t1f81"},{"id":"https://openalex.org/F4320334924","display_name":"Program for New Century Excellent Talents in University","ror":"https://ror.org/01mv9t934"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":54,"referenced_works":["https://openalex.org/W1571870753","https://openalex.org/W1618905105","https://openalex.org/W1664950380","https://openalex.org/W1976038749","https://openalex.org/W1987607942","https://openalex.org/W1997069591","https://openalex.org/W2001298023","https://openalex.org/W2025263547","https://openalex.org/W2027248184","https://openalex.org/W2029296788","https://openalex.org/W2036150674","https://openalex.org/W2045095960","https://openalex.org/W2065772955","https://openalex.org/W2070129348","https://openalex.org/W2080211394","https://openalex.org/W2087263574","https://openalex.org/W2093596219","https://openalex.org/W2098057602","https://openalex.org/W2099768828","https://openalex.org/W2100835628","https://openalex.org/W2101365302","https://openalex.org/W2101926110","https://openalex.org/W2104269704","https://openalex.org/W2107131609","https://openalex.org/W2113464037","https://openalex.org/W2114691401","https://openalex.org/W2117063635","https://openalex.org/W2130670721","https://openalex.org/W2131697388","https://openalex.org/W2132525626","https://openalex.org/W2134357733","https://openalex.org/W2134601045","https://openalex.org/W2134663338","https://openalex.org/W2136251662","https://openalex.org/W2137956165","https://openalex.org/W2139573966","https://openalex.org/W2144438956","https://openalex.org/W2145539952","https://openalex.org/W2150045166","https://openalex.org/W2152438175","https://openalex.org/W2156929696","https://openalex.org/W2166923144","https://openalex.org/W2167881994","https://openalex.org/W2168481151","https://openalex.org/W2170912685","https://openalex.org/W2172000360","https://openalex.org/W2900032653","https://openalex.org/W2903158431","https://openalex.org/W4211049957","https://openalex.org/W4250800088","https://openalex.org/W6636501900","https://openalex.org/W6637241018","https://openalex.org/W6674989108","https://openalex.org/W6991122698"],"related_works":["https://openalex.org/W2072166414","https://openalex.org/W3209970181","https://openalex.org/W2070598848","https://openalex.org/W3034375524","https://openalex.org/W2027399350","https://openalex.org/W2060875994","https://openalex.org/W2044184146","https://openalex.org/W2005234362","https://openalex.org/W2162970382","https://openalex.org/W1997235926"],"abstract_inverted_index":{"Gaussian":[0],"process":[1],"(GP)":[2],"classifiers":[3,58,74,124,156],"represent":[4],"a":[5,89,180,200],"powerful":[6],"and":[7,27,32,83,132,184,192],"interesting":[8],"theoretical":[9],"framework":[10],"for":[11,29,41,75,107],"the":[12,19,33,46,50,62,65,70,80,85,102,119,129,137,150,154,158,167,188,207,210],"Bayesian":[13],"classification":[14,168],"of":[15,21,45,53,64,72,122,153,169,190,209],"hyperspectral":[16,30,76,172],"images.":[17],"However,":[18],"collection":[20],"labeled":[22],"samples":[23,35,106,135],"is":[24],"time":[25],"consuming":[26],"costly":[28],"data,":[31],"training":[34,66,86,105,152],"available":[36],"are":[37,164],"often":[38],"not":[39],"enough":[40],"an":[42,95,143],"adequate":[43],"learning":[44,97,197],"GP":[47,57,73,123,155],"classifier.":[48],"Moreover,":[49,140],"computational":[51],"cost":[52,82],"performing":[54],"inference":[55],"using":[56],"scales":[59],"cubically":[60],"with":[61,175,187,194],"size":[63],"set.":[67],"To":[68],"address":[69],"limitations":[71],"image":[77],"classification,":[78],"reducing":[79],"label":[81],"keeping":[84],"set":[87],"in":[88],"moderate":[90],"size,":[91],"this":[92],"paper":[93],"introduces":[94],"active":[96,196],"(AL)":[98],"strategy":[99],"to":[100,148],"collect":[101],"most":[103,130],"informative":[104],"manual":[108],"labeling.":[109],"First,":[110],"we":[111,141],"propose":[112],"three":[113],"new":[114],"AL":[115,159],"heuristics":[116],"based":[117],"on":[118,166],"probabilistic":[120],"output":[121],"aimed":[125],"at":[126],"actively":[127],"selecting":[128],"uncertain":[131],"confusing":[133],"candidate":[134],"from":[136],"unlabeled":[138],"data.":[139,173],"develop":[142],"incremental":[144,211],"model":[145,212],"updating":[146,213],"scheme":[147],"avoid":[149],"repeated":[151],"during":[157],"process.":[160],"The":[161],"proposed":[162],"approaches":[163,198],"tested":[165],"two":[170],"realworld":[171],"Comparison":[174],"random":[176],"sampling":[177],"method":[178],"reveals":[179],"better":[181],"accuracy":[182],"gain":[183],"faster":[185],"convergence":[186],"number":[189],"queries,":[191],"comparison":[193],"recent":[195],"shows":[199],"competitive":[201],"performance.":[202],"Experimental":[203],"results":[204],"also":[205],"verified":[206],"efficiency":[208],"scheme.":[214]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":9},{"year":2022,"cited_by_count":11},{"year":2021,"cited_by_count":6},{"year":2020,"cited_by_count":10},{"year":2019,"cited_by_count":6},{"year":2018,"cited_by_count":12},{"year":2017,"cited_by_count":8},{"year":2016,"cited_by_count":10},{"year":2015,"cited_by_count":4}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}