{"id":"https://openalex.org/W3192932942","doi":"https://doi.org/10.1109/tgrs.2021.3094867","title":"Nonoverlapped Sampling for Hyperspectral Imagery: Performance Evaluation and a Cotraining-Based Classification Strategy","display_name":"Nonoverlapped Sampling for Hyperspectral Imagery: Performance Evaluation and a Cotraining-Based Classification Strategy","publication_year":2021,"publication_date":"2021-08-10","ids":{"openalex":"https://openalex.org/W3192932942","doi":"https://doi.org/10.1109/tgrs.2021.3094867","mag":"3192932942"},"language":"en","primary_location":{"id":"doi:10.1109/tgrs.2021.3094867","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2021.3094867","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/A5100681906","display_name":"Xianghai Cao","orcid":"https://orcid.org/0000-0003-0997-4664"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xianghai Cao","raw_affiliation_strings":["School of Artificial Intelligence, Xidian University, Xi\u2019an, China","School of Artificial Intelligence, Xidian University, Xi'an, China"],"raw_orcid":"https://orcid.org/0000-0003-0997-4664","affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Xidian University, Xi\u2019an, China","institution_ids":["https://openalex.org/I149594827"]},{"raw_affiliation_string":"School of Artificial Intelligence, Xidian University, Xi'an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5024261982","display_name":"Zuji Liu","orcid":null},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zuji Liu","raw_affiliation_strings":["School of Artificial Intelligence, Xidian University, Xi\u2019an, China","School of Artificial Intelligence, Xidian University, Xi'an, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Xidian University, Xi\u2019an, China","institution_ids":["https://openalex.org/I149594827"]},{"raw_affiliation_string":"School of Artificial Intelligence, Xidian University, Xi'an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101584026","display_name":"Xiangxiang Li","orcid":"https://orcid.org/0009-0003-8143-087X"},"institutions":[{"id":"https://openalex.org/I194716290","display_name":"China Academy of Space Technology","ror":"https://ror.org/025397a59","country_code":"CN","type":"government","lineage":["https://openalex.org/I194716290","https://openalex.org/I2802615301"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiangxiang Li","raw_affiliation_strings":["Space Star Technology Company Ltd., Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Space Star Technology Company Ltd., Beijing, China","institution_ids":["https://openalex.org/I194716290"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5108211676","display_name":"Qian Xiao","orcid":"https://orcid.org/0009-0009-2209-3168"},"institutions":[{"id":"https://openalex.org/I194716290","display_name":"China Academy of Space Technology","ror":"https://ror.org/025397a59","country_code":"CN","type":"government","lineage":["https://openalex.org/I194716290","https://openalex.org/I2802615301"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qian Xiao","raw_affiliation_strings":["Space Star Technology Company Ltd., Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Space Star Technology Company Ltd., Beijing, China","institution_ids":["https://openalex.org/I194716290"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5045546082","display_name":"Jie Feng","orcid":"https://orcid.org/0000-0002-8032-7542"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jie Feng","raw_affiliation_strings":["School of Artificial Intelligence, Xidian University, Xi\u2019an, China","School of Artificial Intelligence, Xidian University, Xi'an, China"],"raw_orcid":"https://orcid.org/0000-0002-8032-7542","affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Xidian University, Xi\u2019an, China","institution_ids":["https://openalex.org/I149594827"]},{"raw_affiliation_string":"School of Artificial Intelligence, Xidian University, Xi'an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5050630882","display_name":"Licheng Jiao","orcid":null},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Licheng Jiao","raw_affiliation_strings":["School of Artificial Intelligence, Xidian University, Xi\u2019an, China","School of Artificial Intelligence, Xidian University, Xi'an, China"],"raw_orcid":"https://orcid.org/0000-0003-3354-9617","affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Xidian University, Xi\u2019an, China","institution_ids":["https://openalex.org/I149594827"]},{"raw_affiliation_string":"School of Artificial Intelligence, Xidian University, Xi'an, China","institution_ids":["https://openalex.org/I149594827"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5100681906"],"corresponding_institution_ids":["https://openalex.org/I149594827"],"apc_list":null,"apc_paid":null,"fwci":0.8846,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.77428164,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":98},"biblio":{"volume":"60","issue":null,"first_page":"1","last_page":"14"},"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":0.965499997138977,"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/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.9624999761581421,"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":0.7730199694633484},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.6941095590591431},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6789396405220032},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6476774215698242},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5979713201522827},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.5740336179733276},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5302685499191284},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.5180299878120422},{"id":"https://openalex.org/keywords/test-set","display_name":"Test set","score":0.49860310554504395},{"id":"https://openalex.org/keywords/statistical-classification","display_name":"Statistical classification","score":0.4605919122695923},{"id":"https://openalex.org/keywords/correlation","display_name":"Correlation","score":0.4321480989456177},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3543407917022705},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.33553346991539},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.25724703073501587},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.24192899465560913},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.15908798575401306}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.7730199694633484},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.6941095590591431},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6789396405220032},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6476774215698242},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5979713201522827},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.5740336179733276},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5302685499191284},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.5180299878120422},{"id":"https://openalex.org/C169903167","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Test set","level":2,"score":0.49860310554504395},{"id":"https://openalex.org/C110083411","wikidata":"https://www.wikidata.org/wiki/Q1744628","display_name":"Statistical classification","level":2,"score":0.4605919122695923},{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.4321480989456177},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3543407917022705},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.33553346991539},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.25724703073501587},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.24192899465560913},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.15908798575401306},{"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/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"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.2021.3094867","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2021.3094867","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/G7886416700","display_name":null,"funder_award_id":"61877066","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":51,"referenced_works":["https://openalex.org/W1651266332","https://openalex.org/W1939429412","https://openalex.org/W1963500125","https://openalex.org/W1966580635","https://openalex.org/W1998030734","https://openalex.org/W2001298023","https://openalex.org/W2013251902","https://openalex.org/W2016860790","https://openalex.org/W2029316659","https://openalex.org/W2050497921","https://openalex.org/W2052160904","https://openalex.org/W2060300932","https://openalex.org/W2064604707","https://openalex.org/W2093462312","https://openalex.org/W2097915756","https://openalex.org/W2101711129","https://openalex.org/W2104269704","https://openalex.org/W2105386417","https://openalex.org/W2107131609","https://openalex.org/W2114819256","https://openalex.org/W2127199143","https://openalex.org/W2129652905","https://openalex.org/W2131864940","https://openalex.org/W2141881345","https://openalex.org/W2152057649","https://openalex.org/W2156163116","https://openalex.org/W2158400785","https://openalex.org/W2164330327","https://openalex.org/W2166923144","https://openalex.org/W2314785379","https://openalex.org/W2396056163","https://openalex.org/W2478493250","https://openalex.org/W2500751094","https://openalex.org/W2595902385","https://openalex.org/W2609880332","https://openalex.org/W2611452721","https://openalex.org/W2740467814","https://openalex.org/W2770644742","https://openalex.org/W2774446296","https://openalex.org/W2779530678","https://openalex.org/W2888715336","https://openalex.org/W2890982797","https://openalex.org/W2914331134","https://openalex.org/W3013953267","https://openalex.org/W3023496898","https://openalex.org/W3044461098","https://openalex.org/W3102274762","https://openalex.org/W3104795559","https://openalex.org/W4240485910","https://openalex.org/W6777050088","https://openalex.org/W6929278630"],"related_works":["https://openalex.org/W1999699871","https://openalex.org/W4225124612","https://openalex.org/W2043806667","https://openalex.org/W2021633306","https://openalex.org/W2006801911","https://openalex.org/W2033669961","https://openalex.org/W2971899271","https://openalex.org/W1972167985","https://openalex.org/W1975547468","https://openalex.org/W2350644419"],"abstract_inverted_index":{"For":[0,29],"hyperspectral":[1,43],"imagery":[2],"(HSI)":[3],"classification,":[4,221],"most":[5,36,98],"of":[6,20,48,84,113,161,200,215,233],"the":[7,14,18,35,55,59,66,70,85,100,103,114,127,134,148,159,179,197,213,231],"studies":[8],"focus":[9],"on":[10],"how":[11],"to":[12,177,229],"improve":[13],"classification":[15,24,95,105,163,189,198,227,246],"accuracy,":[16],"while":[17],"influence":[19,232],"sampling":[21,32,173,208,234],"strategy":[22,72,129,209,235],"for":[23,41,80,219],"performance":[25,81,160,199,240],"attracts":[26],"little":[27],"attention.":[28],"now,":[30],"random":[31],"(RS)":[33],"is":[34,130,175,210],"adopted":[37,176],"strategy.":[38],"That":[39],"is,":[40],"a":[42,45,171,204,225],"image,":[44],"certain":[46],"number":[47],"labeled":[49,61],"samples":[50,62,138,154,185],"are":[51,63,139,191],"randomly":[52],"selected":[53],"as":[54,65],"training":[56,88,119,135,151,182],"set,":[57],"and":[58,90,120,136,152,158,183,186,236],"remaining":[60],"taken":[64],"test":[67,91,121,137,153,184],"set.":[68,92],"However,":[69,123],"RS":[71,101,128],"will":[73,155,165],"produce":[74],"over":[75],"optimistic":[76],"results":[77,194],"when":[78,206],"used":[79],"evaluation":[82],"because":[83,112],"overlap":[86],"between":[87,118,150,181],"set":[89],"Though":[93],"spectral-spatial":[94,245],"methods":[96,106,164,190,202],"benefit":[97,109],"from":[99,110,142],"strategy,":[102],"pixel-wise":[104],"can":[107],"also":[108,223],"it":[111],"high":[115],"spectral":[116],"correlation":[117,149,180],"samples.":[122],"in":[124],"practical":[125],"applications,":[126],"not":[131],"feasible.":[132],"Because":[133],"often":[140],"collected":[141],"different":[143,187],"locations.":[144],"In":[145,168],"this":[146,169],"situation,":[147],"decrease":[156],"dramatically":[157],"HSI":[162,220],"be":[166],"affected.":[167],"article,":[170],"nonoverlapped":[172,207],"method":[174,228],"reduce":[178],"classic":[188,244],"evaluated.":[192],"Experimental":[193],"show":[195],"that":[196],"all":[201],"drops":[203],"lot":[205],"adopted.":[211],"After":[212],"analysis":[214],"some":[216],"important":[217],"factors":[218],"we":[222],"propose":[224],"cotraining-based":[226],"relief":[230],"obtains":[237],"much":[238],"better":[239],"compared":[241],"with":[242],"those":[243],"methods.":[247]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":5}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
