{"id":"https://openalex.org/W4404239114","doi":"https://doi.org/10.1109/tgrs.2024.3496355","title":"MPDA: Multivariate Probability Distribution Autoencoder for Hyperspectral Anomaly Detection","display_name":"MPDA: Multivariate Probability Distribution Autoencoder for Hyperspectral Anomaly Detection","publication_year":2024,"publication_date":"2024-01-01","ids":{"openalex":"https://openalex.org/W4404239114","doi":"https://doi.org/10.1109/tgrs.2024.3496355"},"language":"en","primary_location":{"id":"doi:10.1109/tgrs.2024.3496355","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2024.3496355","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/A5040843796","display_name":"Zhenhua Mu","orcid":null},"institutions":[{"id":"https://openalex.org/I153374732","display_name":"Liaoning Normal University","ror":"https://ror.org/04c3cgg32","country_code":"CN","type":"education","lineage":["https://openalex.org/I153374732"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zhenhua Mu","raw_affiliation_strings":["School of Geography, Liaoning Normal University, Dalian, China"],"affiliations":[{"raw_affiliation_string":"School of Geography, Liaoning Normal University, Dalian, China","institution_ids":["https://openalex.org/I153374732"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078219436","display_name":"Yihan Wang","orcid":"https://orcid.org/0009-0002-8117-7889"},"institutions":[{"id":"https://openalex.org/I153374732","display_name":"Liaoning Normal University","ror":"https://ror.org/04c3cgg32","country_code":"CN","type":"education","lineage":["https://openalex.org/I153374732"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yihan Wang","raw_affiliation_strings":["School of Computer Science and Artificial Intelligence, Liaoning Normal University, Dalian, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Artificial Intelligence, Liaoning Normal University, Dalian, China","institution_ids":["https://openalex.org/I153374732"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100761817","display_name":"Yating Zhang","orcid":"https://orcid.org/0000-0001-8175-3044"},"institutions":[{"id":"https://openalex.org/I153374732","display_name":"Liaoning Normal University","ror":"https://ror.org/04c3cgg32","country_code":"CN","type":"education","lineage":["https://openalex.org/I153374732"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yating Zhang","raw_affiliation_strings":["School of Computer Science and Artificial Intelligence, Liaoning Normal University, Dalian, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Artificial Intelligence, Liaoning Normal University, Dalian, China","institution_ids":["https://openalex.org/I153374732"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102976928","display_name":"Chuanming Song","orcid":"https://orcid.org/0000-0003-1518-8029"},"institutions":[{"id":"https://openalex.org/I4210092944","display_name":"Dalian University","ror":"https://ror.org/00g2ypp58","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210092944"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chuanming Song","raw_affiliation_strings":["School of Information Engineering, Dalian University, Dalian, China"],"affiliations":[{"raw_affiliation_string":"School of Information Engineering, Dalian University, Dalian, China","institution_ids":["https://openalex.org/I4210092944"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5055787139","display_name":"Xianghai Wang","orcid":"https://orcid.org/0000-0002-7600-9939"},"institutions":[{"id":"https://openalex.org/I153374732","display_name":"Liaoning Normal University","ror":"https://ror.org/04c3cgg32","country_code":"CN","type":"education","lineage":["https://openalex.org/I153374732"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xianghai Wang","raw_affiliation_strings":["School of Geography and the School of Computer Science and Artificial Intelligence, Liaoning Normal University, Dalian, China"],"affiliations":[{"raw_affiliation_string":"School of Geography and the School of Computer Science and Artificial Intelligence, Liaoning Normal University, Dalian, China","institution_ids":["https://openalex.org/I153374732"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5040843796"],"corresponding_institution_ids":["https://openalex.org/I153374732"],"apc_list":null,"apc_paid":null,"fwci":1.3212,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.8393866,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":"62","issue":null,"first_page":"1","last_page":"13"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.8626000285148621,"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":0.8626000285148621,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.7688999772071838,"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":0.8495008945465088},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.7301239967346191},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.5692721009254456},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.537361204624176},{"id":"https://openalex.org/keywords/probability-distribution","display_name":"Probability distribution","score":0.5229407548904419},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.5210368633270264},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5073201060295105},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.47997331619262695},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.4485008120536804},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.39541205763816833},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.33222705125808716},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.2283901870250702},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.20276182889938354},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.12319183349609375},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.0907488763332367}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.8495008945465088},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.7301239967346191},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.5692721009254456},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.537361204624176},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.5229407548904419},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.5210368633270264},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5073201060295105},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.47997331619262695},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.4485008120536804},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.39541205763816833},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.33222705125808716},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2283901870250702},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.20276182889938354},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.12319183349609375},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.0907488763332367},{"id":"https://openalex.org/C26873012","wikidata":"https://www.wikidata.org/wiki/Q214781","display_name":"Condensed matter physics","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.2024.3496355","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2024.3496355","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/G5953977486","display_name":null,"funder_award_id":"42371338","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8847923281","display_name":null,"funder_award_id":"JYTZD2023101","funder_id":"https://openalex.org/F4320327799","funder_display_name":"Scientific Research Fund of Liaoning Provincial Education Department"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320327799","display_name":"Scientific Research Fund of Liaoning Provincial Education Department","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W2000721204","https://openalex.org/W2004491663","https://openalex.org/W2032747149","https://openalex.org/W2047870694","https://openalex.org/W2314640094","https://openalex.org/W2892077605","https://openalex.org/W2979897847","https://openalex.org/W3087883793","https://openalex.org/W3133271982","https://openalex.org/W3153686193","https://openalex.org/W3161195768","https://openalex.org/W3186256209","https://openalex.org/W3199351457","https://openalex.org/W3212209618","https://openalex.org/W4211023436","https://openalex.org/W4221059680","https://openalex.org/W4224261559","https://openalex.org/W4285130035","https://openalex.org/W4293863313","https://openalex.org/W4296210064","https://openalex.org/W4299591492","https://openalex.org/W4317433712","https://openalex.org/W4317624878","https://openalex.org/W4321380750","https://openalex.org/W4321488108","https://openalex.org/W4376607587","https://openalex.org/W4378194596","https://openalex.org/W4382936664","https://openalex.org/W4385533904","https://openalex.org/W4386303408","https://openalex.org/W4387623755","https://openalex.org/W4390494483","https://openalex.org/W4391661439","https://openalex.org/W4395096390"],"related_works":["https://openalex.org/W3186512740","https://openalex.org/W3194885736","https://openalex.org/W4363671829","https://openalex.org/W2806741695","https://openalex.org/W4290647774","https://openalex.org/W3189286258","https://openalex.org/W3207797160","https://openalex.org/W3210364259","https://openalex.org/W4300558037","https://openalex.org/W2667207928"],"abstract_inverted_index":{"In":[0,68],"recent":[1],"years,":[2],"the":[3,20,42,51,81,90,98,105,112,120,139,143,151,157,162,169,174,185,190,207,220],"significant":[4],"success":[5],"of":[6,22,53,93,107,154,164,219],"deep":[7],"learning":[8],"(DL)":[9],"in":[10,19,30,65,142,156,203],"computer":[11],"vision":[12],"has":[13],"contributed":[14],"to":[15,41,44,88,102,183],"its":[16],"continuous":[17],"development":[18],"field":[21],"hyperspectral":[23],"image":[24,118,141],"(HSI)":[25],"anomaly":[26,54],"detection":[27,214],"(AD).":[28],"However,":[29],"practical":[31],"applications,":[32],"HSI-AD":[33,78],"based":[34,79,132,178],"on":[35,80,133,179,198],"DL":[36,75],"faces":[37],"many":[38],"challenges":[39],"due":[40],"inability":[43],"effectively":[45],"acquire":[46],"training":[47],"samples":[48],"and":[49,114,160,213],"predict":[50],"types":[52],"targets.":[55,165],"This":[56],"makes":[57],"it":[58],"a":[59],"challenging":[60],"task,":[61],"especially":[62],"for":[63,77],"AD":[64,187,196],"complex":[66],"scenes.":[67],"this":[69],"article,":[70],"we":[71,96,125,145,167],"propose":[72,126],"an":[73,116,127],"unsupervised":[74,128],"framework":[76],"multivariate":[82,175],"probability":[83,99],"distribution":[84,91,181],"autoencoder":[85,130],"(MPDA).":[86],"First,":[87],"explore":[89],"characteristics":[92,182],"high-dimensional":[94],"data,":[95],"use":[97],"density":[100],"histogram":[101],"statistically":[103],"distribute":[104],"frequencies":[106],"each":[108],"interval":[109],"adaptively,":[110],"dividing":[111],"HSI":[113,200],"obtaining":[115],"AD-guided":[117],"through":[119],"designed":[121],"grid":[122],"structure.":[123],"Second,":[124],"multilayer":[129],"network":[131],"energy-weighted":[134],"skip":[135],"connections.":[136],"By":[137],"coupling":[138],"detection-guided":[140],"network,":[144],"achieve":[146],"reverse-guided":[147],"module":[148],"reconstruction,":[149],"weakening":[150],"feature":[152],"representation":[153],"anomalous":[155],"reconstructed":[158,170],"information":[159],"enhancing":[161],"separability":[163],"Finally,":[166],"model":[168],"error":[171],"images":[172],"using":[173],"skewed":[176],"t-distribution":[177],"data":[180],"obtain":[184],"final":[186],"map.":[188],"Through":[189],"comparative":[191],"experiments":[192],"with":[193],"other":[194],"innovative":[195],"algorithms":[197],"authentic":[199],"datasets":[201],"captured":[202],"five":[204],"different":[205],"scenarios,":[206],"proposed":[208],"algorithm":[209],"demonstrates":[210],"strong":[211],"generalization":[212],"capabilities.":[215],"The":[216],"source":[217],"code":[218],"MPDA":[221],"will":[222],"be":[223],"public":[224],"at":[225],"<uri":[226],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[227],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">https://github.com/muzhenhuam/MPDA/tree/master</uri>.":[228]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3}],"updated_date":"2026-03-15T09:29:46.208133","created_date":"2025-10-10T00:00:00"}
