{"id":"https://openalex.org/W4285199022","doi":"https://doi.org/10.1109/lgrs.2022.3186493","title":"Active Deep Feature Extraction for Hyperspectral Image Classification Based on Adversarial Learning","display_name":"Active Deep Feature Extraction for Hyperspectral Image Classification Based on Adversarial Learning","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W4285199022","doi":"https://doi.org/10.1109/lgrs.2022.3186493"},"language":"en","primary_location":{"id":"doi:10.1109/lgrs.2022.3186493","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2022.3186493","pdf_url":null,"source":{"id":"https://openalex.org/S126920919","display_name":"IEEE Geoscience and Remote Sensing Letters","issn_l":"1545-598X","issn":["1545-598X","1558-0571"],"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 Geoscience and Remote Sensing Letters","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/A5100623786","display_name":"Xue Wang","orcid":"https://orcid.org/0000-0002-6999-1362"},"institutions":[{"id":"https://openalex.org/I211433327","display_name":"Ministry of Natural Resources","ror":"https://ror.org/02kxqx159","country_code":"CN","type":"government","lineage":["https://openalex.org/I211433327","https://openalex.org/I4210127390"]},{"id":"https://openalex.org/I66867065","display_name":"East China Normal University","ror":"https://ror.org/02n96ep67","country_code":"CN","type":"education","lineage":["https://openalex.org/I66867065"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xue Wang","raw_affiliation_strings":["Key Laboratory of Geographic Information Science (Ministry of Education), School of Geographic Sciences, and Key Laboratory of Spatial-Temporal Big Data Analysis and Application of Natural Resources in Megacities (Ministry of Natural Resources), East China Normal University, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0002-6999-1362","affiliations":[{"raw_affiliation_string":"Key Laboratory of Geographic Information Science (Ministry of Education), School of Geographic Sciences, and Key Laboratory of Spatial-Temporal Big Data Analysis and Application of Natural Resources in Megacities (Ministry of Natural Resources), East China Normal University, Shanghai, China","institution_ids":["https://openalex.org/I211433327","https://openalex.org/I66867065"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101482958","display_name":"Kun Tan","orcid":"https://orcid.org/0000-0001-6353-0146"},"institutions":[{"id":"https://openalex.org/I211433327","display_name":"Ministry of Natural Resources","ror":"https://ror.org/02kxqx159","country_code":"CN","type":"government","lineage":["https://openalex.org/I211433327","https://openalex.org/I4210127390"]},{"id":"https://openalex.org/I66867065","display_name":"East China Normal University","ror":"https://ror.org/02n96ep67","country_code":"CN","type":"education","lineage":["https://openalex.org/I66867065"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kun Tan","raw_affiliation_strings":["Key Laboratory of Geographic Information Science (Ministry of Education), School of Geographic Sciences, and Key Laboratory of Spatial-Temporal Big Data Analysis and Application of Natural Resources in Megacities (Ministry of Natural Resources), East China Normal University, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0001-6353-0146","affiliations":[{"raw_affiliation_string":"Key Laboratory of Geographic Information Science (Ministry of Education), School of Geographic Sciences, and Key Laboratory of Spatial-Temporal Big Data Analysis and Application of Natural Resources in Megacities (Ministry of Natural Resources), East China Normal University, Shanghai, China","institution_ids":["https://openalex.org/I211433327","https://openalex.org/I66867065"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014275049","display_name":"Cen Pan","orcid":null},"institutions":[{"id":"https://openalex.org/I4210157011","display_name":"Shanghai Institute of Geological Survey","ror":"https://ror.org/04pyk6020","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210157011"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Cen Pan","raw_affiliation_strings":["Shanghai Municipal Institute of Surveying and Mapping, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shanghai Municipal Institute of Surveying and Mapping, Shanghai, China","institution_ids":["https://openalex.org/I4210157011"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101941113","display_name":"Jianwei Ding","orcid":"https://orcid.org/0000-0003-1686-1940"},"institutions":[{"id":"https://openalex.org/I4210090615","display_name":"Hospital of Hebei Province","ror":"https://ror.org/0000yrh61","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I4210090615"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jianwei Ding","raw_affiliation_strings":["Second Surveying and Mapping Institute of Hebei, Shijiazhuang, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Second Surveying and Mapping Institute of Hebei, Shijiazhuang, China","institution_ids":["https://openalex.org/I4210090615"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016259816","display_name":"Zhaoxian Liu","orcid":"https://orcid.org/0000-0002-1770-8166"},"institutions":[{"id":"https://openalex.org/I4210090615","display_name":"Hospital of Hebei Province","ror":"https://ror.org/0000yrh61","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I4210090615"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhaoxian Liu","raw_affiliation_strings":["Second Surveying and Mapping Institute of Hebei, Shijiazhuang, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Second Surveying and Mapping Institute of Hebei, Shijiazhuang, China","institution_ids":["https://openalex.org/I4210090615"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5000273778","display_name":"Bo Han","orcid":"https://orcid.org/0000-0002-9226-0461"},"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":"Bo Han","raw_affiliation_strings":["China Academy of Space Technology, Institute of Remote Sensing Satellite, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"China Academy of Space Technology, Institute of Remote Sensing Satellite, Beijing, China","institution_ids":["https://openalex.org/I194716290"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.7094,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.70092947,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":"19","issue":null,"first_page":"1","last_page":"5"},"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9955000281333923,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9940000176429749,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.8435350656509399},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.764999508857727},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7478114366531372},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6826983094215393},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.6721373200416565},{"id":"https://openalex.org/keywords/redundancy","display_name":"Redundancy (engineering)","score":0.652588963508606},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.6504572629928589},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5568599700927734},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4434589743614197},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.41432249546051025},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.4128509759902954},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.33162921667099}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.8435350656509399},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.764999508857727},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7478114366531372},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6826983094215393},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.6721373200416565},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.652588963508606},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.6504572629928589},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5568599700927734},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4434589743614197},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.41432249546051025},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.4128509759902954},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.33162921667099},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lgrs.2022.3186493","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2022.3186493","pdf_url":null,"source":{"id":"https://openalex.org/S126920919","display_name":"IEEE Geoscience and Remote Sensing Letters","issn_l":"1545-598X","issn":["1545-598X","1558-0571"],"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 Geoscience and Remote Sensing Letters","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.49000000953674316,"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education"}],"awards":[{"id":"https://openalex.org/G2272609924","display_name":null,"funder_award_id":"KLSMNR-202202","funder_id":"https://openalex.org/F4320316090","funder_display_name":"Ministry of Natural Resources of the People's Republic of China"},{"id":"https://openalex.org/G2494174422","display_name":null,"funder_award_id":"42171335","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3024384241","display_name":null,"funder_award_id":"42001350","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3386138028","display_name":null,"funder_award_id":"2021M691016","funder_id":"https://openalex.org/F4320321543","funder_display_name":"China Postdoctoral Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320316090","display_name":"Ministry of Natural Resources of the People's Republic of China","ror":null},{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320321543","display_name":"China Postdoctoral Science Foundation","ror":"https://ror.org/0426zh255"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W2012878613","https://openalex.org/W2080021732","https://openalex.org/W2089666662","https://openalex.org/W2127271355","https://openalex.org/W2128518360","https://openalex.org/W2131697388","https://openalex.org/W2518897583","https://openalex.org/W2735257517","https://openalex.org/W2809113079","https://openalex.org/W2901819993","https://openalex.org/W2911445232","https://openalex.org/W2944653015","https://openalex.org/W3093896170","https://openalex.org/W3104418202","https://openalex.org/W4205421373","https://openalex.org/W4220788280","https://openalex.org/W6780248173"],"related_works":["https://openalex.org/W2159052453","https://openalex.org/W3013693939","https://openalex.org/W2566616303","https://openalex.org/W3131327266","https://openalex.org/W2502115930","https://openalex.org/W2072166414","https://openalex.org/W2752972570","https://openalex.org/W4297051394","https://openalex.org/W3209970181","https://openalex.org/W2070598848"],"abstract_inverted_index":{"The":[0,100],"issues":[1],"of":[2,15],"spectral":[3],"redundancy":[4],"and":[5,13,34,54,57,132],"limited":[6,77],"training":[7,71],"samples":[8,79],"hinder":[9],"the":[10,45,67,76,89,105,110,116,121],"widespread":[11],"application":[12],"development":[14],"hyperspectral":[16,98],"images.":[17],"In":[18],"this":[19,119],"letter,":[20],"a":[21,55,82],"novel":[22],"active":[23],"deep":[24,49],"feature":[25,50],"extraction":[26],"scheme":[27,112],"is":[28,41,113,126],"proposed":[29,90,111],"by":[30,128],"incorporating":[31,129],"both":[32,130],"representative":[33,133],"informative":[35,131],"measurement.":[36,134],"Firstly,":[37],"an":[38],"adversarial":[39],"autoencoder":[40],"modified":[42],"to":[43,65,80,115],"suit":[44],"classification":[46,84],"task":[47],"with":[48,88,96,104],"extraction.":[51],"Dictionary":[52],"learning":[53],"multi-variance":[56],"distributional":[58],"distance":[59],"(MVDD)":[60],"measure":[61],"are":[62],"then":[63],"introduced":[64],"choose":[66],"most":[68],"valuable":[69],"candidate":[70],"samples,":[72],"where":[73],"we":[74],"use":[75],"labeled":[78],"obtain":[81],"high":[83],"accuracy.":[85],"Comparative":[86],"experiments":[87],"querying":[91],"strategy":[92],"were":[93],"carried":[94],"out":[95],"two":[97,106],"datasets.":[99],"experimental":[101],"results":[102],"obtained":[103],"datasets":[107],"demonstrate":[108],"that":[109],"superior":[114],"others.":[117],"With":[118],"method,":[120],"unstable":[122],"increase":[123],"in":[124],"accuracy":[125],"eliminated":[127]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":1}],"updated_date":"2026-07-22T07:51:19.307946","created_date":"2025-10-10T00:00:00"}
