{"id":"https://openalex.org/W3005109735","doi":"https://doi.org/10.1109/tgrs.2020.2965995","title":"Autoencoder and Adversarial-Learning-Based Semisupervised Background Estimation for Hyperspectral Anomaly Detection","display_name":"Autoencoder and Adversarial-Learning-Based Semisupervised Background Estimation for Hyperspectral Anomaly Detection","publication_year":2020,"publication_date":"2020-02-04","ids":{"openalex":"https://openalex.org/W3005109735","doi":"https://doi.org/10.1109/tgrs.2020.2965995","mag":"3005109735"},"language":"en","primary_location":{"id":"doi:10.1109/tgrs.2020.2965995","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2020.2965995","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/A5052163069","display_name":"Weiying Xie","orcid":"https://orcid.org/0000-0001-8310-024X"},"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":"Weiying Xie","raw_affiliation_strings":["State Key Laboratory of Integrated Service Networks, Xidian University, Xi\u2019an, China","State Key Laboratory of Integrated Service Networks, Xidian University, Xi'an, China"],"raw_orcid":"https://orcid.org/0000-0001-8310-024X","affiliations":[{"raw_affiliation_string":"State Key Laboratory of Integrated Service Networks, Xidian University, Xi\u2019an, China","institution_ids":["https://openalex.org/I149594827"]},{"raw_affiliation_string":"State Key Laboratory of Integrated Service Networks, Xidian University, Xi'an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074335468","display_name":"Baozhu Liu","orcid":"https://orcid.org/0000-0001-7084-5365"},"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":"Baozhu Liu","raw_affiliation_strings":["State Key Laboratory of Integrated Service Networks, Xidian University, Xi\u2019an, China","State Key Laboratory of Integrated Service Networks, Xidian University, Xi'an, China"],"raw_orcid":"https://orcid.org/0000-0001-7084-5365","affiliations":[{"raw_affiliation_string":"State Key Laboratory of Integrated Service Networks, Xidian University, Xi\u2019an, China","institution_ids":["https://openalex.org/I149594827"]},{"raw_affiliation_string":"State Key Laboratory of Integrated Service Networks, Xidian University, Xi'an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067798266","display_name":"Yunsong Li","orcid":"https://orcid.org/0000-0002-0234-6270"},"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":"Yunsong Li","raw_affiliation_strings":["State Key Laboratory of Integrated Service Networks, Xidian University, Xi\u2019an, China","State Key Laboratory of Integrated Service Networks, Xidian University, Xi'an, China"],"raw_orcid":"https://orcid.org/0000-0002-0234-6270","affiliations":[{"raw_affiliation_string":"State Key Laboratory of Integrated Service Networks, Xidian University, Xi\u2019an, China","institution_ids":["https://openalex.org/I149594827"]},{"raw_affiliation_string":"State Key Laboratory of Integrated Service Networks, Xidian University, Xi'an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5007285444","display_name":"Jie Lei","orcid":"https://orcid.org/0000-0003-0851-6565"},"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 Lei","raw_affiliation_strings":["Science and Technology on Electrooptic Control Laboratory, Luoyang, China","State Key Laboratory of Integrated Service Networks, Xidian University, Xi\u2019an, China","State Key Laboratory of Integrated Service Networks, Xidian University, Xi'an, China"],"raw_orcid":"https://orcid.org/0000-0003-0851-6565","affiliations":[{"raw_affiliation_string":"Science and Technology on Electrooptic Control Laboratory, Luoyang, China","institution_ids":[]},{"raw_affiliation_string":"State Key Laboratory of Integrated Service Networks, Xidian University, Xi\u2019an, China","institution_ids":["https://openalex.org/I149594827"]},{"raw_affiliation_string":"State Key Laboratory of Integrated Service Networks, Xidian University, Xi'an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5033017179","display_name":"Qian Du","orcid":"https://orcid.org/0000-0001-8354-7500"},"institutions":[{"id":"https://openalex.org/I99041443","display_name":"Mississippi State University","ror":"https://ror.org/0432jq872","country_code":"US","type":"education","lineage":["https://openalex.org/I4210141039","https://openalex.org/I99041443"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Qian Du","raw_affiliation_strings":["Mississippi State University, Starkville, USA"],"raw_orcid":"https://orcid.org/0000-0001-8354-7500","affiliations":[{"raw_affiliation_string":"Mississippi State University, Starkville, USA","institution_ids":["https://openalex.org/I99041443"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":13.0076,"has_fulltext":false,"cited_by_count":101,"citation_normalized_percentile":{"value":0.98783334,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":100},"biblio":{"volume":"58","issue":"8","first_page":"5416","last_page":"5427"},"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/T11667","display_name":"Advanced Chemical Sensor Technologies","score":0.9664000272750854,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9417999982833862,"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/autoencoder","display_name":"Autoencoder","score":0.8406873941421509},{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.8091728687286377},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7727583050727844},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7142237424850464},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.6722003221511841},{"id":"https://openalex.org/keywords/discriminator","display_name":"Discriminator","score":0.6036361455917358},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5837065577507019},{"id":"https://openalex.org/keywords/constant-false-alarm-rate","display_name":"Constant false alarm rate","score":0.5102767944335938},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4543156325817108},{"id":"https://openalex.org/keywords/false-alarm","display_name":"False alarm","score":0.4528055191040039},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.42849215865135193},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.410582959651947},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.0755167305469513}],"concepts":[{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.8406873941421509},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.8091728687286377},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7727583050727844},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7142237424850464},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.6722003221511841},{"id":"https://openalex.org/C2779803651","wikidata":"https://www.wikidata.org/wiki/Q5282088","display_name":"Discriminator","level":3,"score":0.6036361455917358},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5837065577507019},{"id":"https://openalex.org/C77052588","wikidata":"https://www.wikidata.org/wiki/Q644307","display_name":"Constant false alarm rate","level":2,"score":0.5102767944335938},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4543156325817108},{"id":"https://openalex.org/C2776836416","wikidata":"https://www.wikidata.org/wiki/Q1364844","display_name":"False alarm","level":2,"score":0.4528055191040039},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.42849215865135193},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.410582959651947},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.0755167305469513},{"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/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tgrs.2020.2965995","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2020.2965995","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":[{"score":0.6899999976158142,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[{"id":"https://openalex.org/G1147426558","display_name":null,"funder_award_id":"JB180104","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"},{"id":"https://openalex.org/G1424917938","display_name":null,"funder_award_id":"61701360","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G1595715893","display_name":null,"funder_award_id":"B08038","funder_id":"https://openalex.org/F4320327912","funder_display_name":"Higher Education Discipline Innovation Project"},{"id":"https://openalex.org/G4131587450","display_name":"\u56fe\u50cf\u7b97\u672f\u7f16\u7801\u7279\u5f81\u53ca\u540c\u6b65\u6280\u672f\u7814\u7a76","funder_award_id":"61571345","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G4507359911","display_name":null,"funder_award_id":"61502367","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5657849150","display_name":null,"funder_award_id":"91538101","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5820774447","display_name":null,"funder_award_id":"2019T120878","funder_id":"https://openalex.org/F4320321543","funder_display_name":"China Postdoctoral Science Foundation"},{"id":"https://openalex.org/G7389467270","display_name":null,"funder_award_id":"2017M620440","funder_id":"https://openalex.org/F4320321543","funder_display_name":"China Postdoctoral Science Foundation"},{"id":"https://openalex.org/G7594473715","display_name":null,"funder_award_id":"61801359","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G841248577","display_name":null,"funder_award_id":"61501346","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"},{"id":"https://openalex.org/F4320321543","display_name":"China Postdoctoral Science Foundation","ror":"https://ror.org/0426zh255"},{"id":"https://openalex.org/F4320327912","display_name":"Higher Education Discipline Innovation Project","ror":null},{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":47,"referenced_works":["https://openalex.org/W795408010","https://openalex.org/W1836465849","https://openalex.org/W1970189346","https://openalex.org/W2004491663","https://openalex.org/W2010319424","https://openalex.org/W2024288510","https://openalex.org/W2047870694","https://openalex.org/W2099471712","https://openalex.org/W2118661197","https://openalex.org/W2124463804","https://openalex.org/W2163129097","https://openalex.org/W2288752886","https://openalex.org/W2295576075","https://openalex.org/W2533102868","https://openalex.org/W2568836762","https://openalex.org/W2592141703","https://openalex.org/W2743780012","https://openalex.org/W2768800090","https://openalex.org/W2785771886","https://openalex.org/W2791006446","https://openalex.org/W2791514264","https://openalex.org/W2807998381","https://openalex.org/W2810874828","https://openalex.org/W2884073548","https://openalex.org/W2901061792","https://openalex.org/W2901555355","https://openalex.org/W2904646604","https://openalex.org/W2910068345","https://openalex.org/W2911876518","https://openalex.org/W2912969801","https://openalex.org/W2948571212","https://openalex.org/W2949117887","https://openalex.org/W2953791858","https://openalex.org/W2964469941","https://openalex.org/W2969635036","https://openalex.org/W2972614519","https://openalex.org/W2982545969","https://openalex.org/W2988878652","https://openalex.org/W3032026348","https://openalex.org/W3196898581","https://openalex.org/W4293568373","https://openalex.org/W4320013936","https://openalex.org/W6638667902","https://openalex.org/W6752318946","https://openalex.org/W6758101687","https://openalex.org/W6758499544","https://openalex.org/W6780248173"],"related_works":["https://openalex.org/W2983142544","https://openalex.org/W2891059443","https://openalex.org/W4281663961","https://openalex.org/W3208888551","https://openalex.org/W4313561566","https://openalex.org/W3208386644","https://openalex.org/W1983393909","https://openalex.org/W4389832810","https://openalex.org/W4220682630","https://openalex.org/W3181622257"],"abstract_inverted_index":{"Reliable":[0],"detection":[1,26,50,179,198],"of":[2,42,66,126],"anomalies":[3],"without":[4],"any":[5],"prior":[6,67],"information":[7,33],"is":[8,86,109],"a":[9,74,129,132,191],"critical":[10],"yet":[11],"challenging":[12],"task":[13],"in":[14,34,55,94,194],"many":[15],"applications,":[16],"not":[17],"least":[18],"military":[19],"and":[20,77,131,147,181,200],"civilian":[21],"fields.":[22],"An":[23],"intelligent":[24],"anomaly":[25,49],"system":[27],"would":[28],"use":[29],"the":[30,40,90,99,113,118,142,153,157,171,174,195],"material-specific":[31],"spectral":[32,92,120,145],"hyperspectral":[35,48],"images":[36],"(HSIs),":[37],"thereby":[38],"avoiding":[39],"loss":[41,149],"visually":[43],"confusing":[44],"objects.":[45],"However,":[46],"conventional":[47],"methods":[51],"are":[52],"mainly":[53],"achieved":[54],"an":[56,104,127],"unsupervised":[57,105],"way":[58],"leading":[59],"to":[60,64,96,116,134,155,170],"limited":[61],"performance":[62],"due":[63],"lack":[65],"knowledge.":[68],"In":[69,102],"this":[70],"article,":[71],"we":[72],"propose":[73],"novel":[75],"autoencoder":[76],"adversarial-learning":[78],"based":[79],"semisupervised":[80],"background":[81,91,100,106,119,137,158],"estimation":[82],"model":[83,154],"(SBEM)":[84],"that":[85,168,188],"trained":[87],"only":[88],"on":[89,112,163],"samples":[93],"order":[95],"accurately":[97],"learn":[98,156],"distribution.":[101,138],"particular,":[103],"searching":[107],"method":[108],"firstly":[110],"conducted":[111],"original":[114],"HSIs":[115,166],"search":[117],"samples.":[121],"Our":[122],"proposed":[123,175],"SBEM":[124],"consists":[125],"encoder,":[128],"decoder,":[130],"discriminator":[133],"thoroughly":[135],"capture":[136],"Furthermore,":[139],"jointly":[140],"minimizing":[141],"reconstruction":[143],"loss,":[144,146],"adversarial":[148],"during":[150],"training":[151],"aids":[152],"distribution":[159],"as":[160],"required.":[161],"Experiments":[162],"four":[164],"real":[165],"demonstrate":[167],"compared":[169],"current":[172],"state-of-the-art,":[173],"framework":[176],"yields":[177],"higher":[178],"capability":[180],"lower":[182],"false":[183,201],"alarm":[184,202],"rate,":[185],"which":[186],"shows":[187],"it":[189],"has":[190],"significant":[192],"benefit":[193],"tradeoff":[196],"between":[197],"accuracy":[199],"rate.":[203]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":19},{"year":2024,"cited_by_count":11},{"year":2023,"cited_by_count":21},{"year":2022,"cited_by_count":26},{"year":2021,"cited_by_count":18},{"year":2020,"cited_by_count":3}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
