{"id":"https://openalex.org/W2884158490","doi":"https://doi.org/10.1109/tgrs.2018.2849225","title":"The Effect of Ground Truth on Performance Evaluation of Hyperspectral Image Classification","display_name":"The Effect of Ground Truth on Performance Evaluation of Hyperspectral Image Classification","publication_year":2018,"publication_date":"2018-07-19","ids":{"openalex":"https://openalex.org/W2884158490","doi":"https://doi.org/10.1109/tgrs.2018.2849225","mag":"2884158490"},"language":"en","primary_location":{"id":"doi:10.1109/tgrs.2018.2849225","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2018.2849225","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/A5067097659","display_name":"Shutao Li","orcid":"https://orcid.org/0000-0002-0585-9848"},"institutions":[{"id":"https://openalex.org/I16609230","display_name":"Hunan University","ror":"https://ror.org/05htk5m33","country_code":"CN","type":"education","lineage":["https://openalex.org/I16609230"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shutao Li","raw_affiliation_strings":["College of Electrical and Information Engineering, Hunan University, Changsha, China"],"raw_orcid":"https://orcid.org/0000-0002-0585-9848","affiliations":[{"raw_affiliation_string":"College of Electrical and Information Engineering, Hunan University, Changsha, China","institution_ids":["https://openalex.org/I16609230"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5024679777","display_name":"Qiaobo Hao","orcid":"https://orcid.org/0000-0002-0177-7263"},"institutions":[{"id":"https://openalex.org/I16609230","display_name":"Hunan University","ror":"https://ror.org/05htk5m33","country_code":"CN","type":"education","lineage":["https://openalex.org/I16609230"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qiaobo Hao","raw_affiliation_strings":["College of Electrical and Information Engineering, Hunan University, Changsha, China"],"raw_orcid":"https://orcid.org/0000-0002-0177-7263","affiliations":[{"raw_affiliation_string":"College of Electrical and Information Engineering, Hunan University, Changsha, China","institution_ids":["https://openalex.org/I16609230"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012299081","display_name":"Guanghao Gao","orcid":null},"institutions":[{"id":"https://openalex.org/I16609230","display_name":"Hunan University","ror":"https://ror.org/05htk5m33","country_code":"CN","type":"education","lineage":["https://openalex.org/I16609230"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guanghao Gao","raw_affiliation_strings":["College of Electrical and Information Engineering, Hunan University, Changsha, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Electrical and Information Engineering, Hunan University, Changsha, China","institution_ids":["https://openalex.org/I16609230"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5057514965","display_name":"Xudong Kang","orcid":"https://orcid.org/0000-0002-3807-2531"},"institutions":[{"id":"https://openalex.org/I16609230","display_name":"Hunan University","ror":"https://ror.org/05htk5m33","country_code":"CN","type":"education","lineage":["https://openalex.org/I16609230"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xudong Kang","raw_affiliation_strings":["College of Electrical and Information Engineering, Hunan University, Changsha, China"],"raw_orcid":"https://orcid.org/0000-0002-3807-2531","affiliations":[{"raw_affiliation_string":"College of Electrical and Information Engineering, Hunan University, Changsha, China","institution_ids":["https://openalex.org/I16609230"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I16609230"],"apc_list":null,"apc_paid":null,"fwci":3.6777,"has_fulltext":false,"cited_by_count":31,"citation_normalized_percentile":{"value":0.93748541,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"56","issue":"12","first_page":"7195","last_page":"7206"},"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":0.9890999794006348,"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/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.843683123588562},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.8073952198028564},{"id":"https://openalex.org/keywords/correlation-coefficient","display_name":"Correlation coefficient","score":0.5994670391082764},{"id":"https://openalex.org/keywords/pearson-product-moment-correlation-coefficient","display_name":"Pearson product-moment correlation coefficient","score":0.571167528629303},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.5577281713485718},{"id":"https://openalex.org/keywords/rank-correlation","display_name":"Rank correlation","score":0.552804708480835},{"id":"https://openalex.org/keywords/cohens-kappa","display_name":"Cohen's kappa","score":0.5366415977478027},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5295428037643433},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5174118280410767},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.47246623039245605},{"id":"https://openalex.org/keywords/rank","display_name":"Rank (graph theory)","score":0.4577864408493042},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4397137463092804},{"id":"https://openalex.org/keywords/spearmans-rank-correlation-coefficient","display_name":"Spearman's rank correlation coefficient","score":0.4334626793861389},{"id":"https://openalex.org/keywords/correlation","display_name":"Correlation","score":0.43086493015289307},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.4254639744758606},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.3991985023021698},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.38326412439346313},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3424926996231079}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.843683123588562},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.8073952198028564},{"id":"https://openalex.org/C2780092901","wikidata":"https://www.wikidata.org/wiki/Q3433612","display_name":"Correlation coefficient","level":2,"score":0.5994670391082764},{"id":"https://openalex.org/C55078378","wikidata":"https://www.wikidata.org/wiki/Q1136628","display_name":"Pearson product-moment correlation coefficient","level":2,"score":0.571167528629303},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5577281713485718},{"id":"https://openalex.org/C101601086","wikidata":"https://www.wikidata.org/wiki/Q3753228","display_name":"Rank correlation","level":2,"score":0.552804708480835},{"id":"https://openalex.org/C163864269","wikidata":"https://www.wikidata.org/wiki/Q1107106","display_name":"Cohen's kappa","level":2,"score":0.5366415977478027},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5295428037643433},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5174118280410767},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.47246623039245605},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.4577864408493042},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4397137463092804},{"id":"https://openalex.org/C159744936","wikidata":"https://www.wikidata.org/wiki/Q1126730","display_name":"Spearman's rank correlation coefficient","level":2,"score":0.4334626793861389},{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.43086493015289307},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.4254639744758606},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3991985023021698},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.38326412439346313},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3424926996231079},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tgrs.2018.2849225","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2018.2849225","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/G8694044906","display_name":null,"funder_award_id":"61601179","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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":39,"referenced_works":["https://openalex.org/W1535602073","https://openalex.org/W1939429412","https://openalex.org/W1971155098","https://openalex.org/W1985973695","https://openalex.org/W2002392274","https://openalex.org/W2016860790","https://openalex.org/W2018482939","https://openalex.org/W2041100636","https://openalex.org/W2097915756","https://openalex.org/W2107799335","https://openalex.org/W2136251662","https://openalex.org/W2144151128","https://openalex.org/W2152057649","https://openalex.org/W2153635508","https://openalex.org/W2162480849","https://openalex.org/W2164769329","https://openalex.org/W2166923144","https://openalex.org/W2240914251","https://openalex.org/W2321547661","https://openalex.org/W2396056163","https://openalex.org/W2436131245","https://openalex.org/W2500751094","https://openalex.org/W2519307493","https://openalex.org/W2603422184","https://openalex.org/W2740976805","https://openalex.org/W2754507318","https://openalex.org/W2761917471","https://openalex.org/W2767581044","https://openalex.org/W2767805377","https://openalex.org/W2768309288","https://openalex.org/W2779054585","https://openalex.org/W2791928749","https://openalex.org/W2795547044","https://openalex.org/W2887702762","https://openalex.org/W3100245404","https://openalex.org/W3101640299","https://openalex.org/W3102274762","https://openalex.org/W4240485910","https://openalex.org/W4300952079"],"related_works":["https://openalex.org/W3152154711","https://openalex.org/W2999441357","https://openalex.org/W1911095394","https://openalex.org/W2773390159","https://openalex.org/W2749680699","https://openalex.org/W2357941858","https://openalex.org/W2358850878","https://openalex.org/W2555516226","https://openalex.org/W2963320501","https://openalex.org/W2389190814"],"abstract_inverted_index":{"In":[0,58],"the":[1,25,28,32,42,46,61,67,83,89,102,117,155,172],"field":[2],"of":[3,15,63,71,104,158,174],"hyperspectral":[4,72],"image":[5,73],"classification,":[6],"a":[7,50],"widely":[8],"used":[9],"way":[10],"for":[11,136,180],"objective":[12,68,181],"performance":[13,69,166,182],"evaluation":[14,70,114,183],"different":[16,105],"classification":[17,43,52,74,106],"methods":[18,107],"is":[19,75,79,86],"calculating":[20],"three":[21],"accuracy":[22,36,91,160,178],"indexes,":[23],"i.e.,":[24,49,116],"overall":[26],"accuracy,":[27,30],"average":[29],"and":[31,128,170],"Kappa":[33],"coefficient.":[34],"These":[35],"indexes":[37,92,179],"are":[38],"obtained":[39,148],"by":[40,55],"comparing":[41],"results":[44],"with":[45,184],"ground":[47,64,84,111,151,186],"truth,":[48],"reference":[51],"map":[53],"labeled":[54],"human":[56],"experts.":[57],"this":[59],"paper,":[60],"effect":[62],"truths":[65,152],"on":[66,140],"studied.":[76],"The":[77],"purpose":[78],"to":[80,100,108],"investigate,":[81],"if":[82],"truth":[85],"insufficient,":[87],"whether":[88],"above":[90],"can":[93,146],"be":[94,147],"completely":[95],"responsible.":[96],"Furthermore,":[97],"in":[98],"order":[99],"measure":[101],"robustness":[103],"those":[109],"insufficient":[110,150,185],"truths,":[112],"four":[113],"metrics,":[115],"Pearson":[118],"linear":[119],"correlation":[120,126,131],"coefficient,":[121,127],"root-mean-square":[122],"error,":[123],"Spearman's":[124],"rank":[125,130],"Kendall's":[129],"coefficient":[132],"have":[133],"been":[134],"adopted":[135],"further":[137],"analysis.":[138],"Based":[139],"these":[141],"experiments,":[142],"an":[143],"interesting":[144],"conclusion":[145],"that":[149,164],"may":[153,168],"limit":[154],"assessment":[156],"capability":[157],"existing":[159],"indexes.":[161],"This":[162],"underlines":[163],"overoptimistic":[165],"evaluations":[167],"exist":[169],"stresses":[171],"demand":[173],"designing":[175],"more":[176],"appropriate":[177],"truths.":[187]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":4},{"year":2019,"cited_by_count":9},{"year":2018,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
