{"id":"https://openalex.org/W3022984473","doi":"https://doi.org/10.1109/tsp.2020.2991311","title":"Training Data Assisted Anomaly Detection of Multi-Pixel Targets In Hyperspectral Imagery","display_name":"Training Data Assisted Anomaly Detection of Multi-Pixel Targets In Hyperspectral Imagery","publication_year":2020,"publication_date":"2020-01-01","ids":{"openalex":"https://openalex.org/W3022984473","doi":"https://doi.org/10.1109/tsp.2020.2991311","mag":"3022984473"},"language":"en","primary_location":{"id":"doi:10.1109/tsp.2020.2991311","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tsp.2020.2991311","pdf_url":null,"source":{"id":"https://openalex.org/S168680287","display_name":"IEEE Transactions on Signal Processing","issn_l":"1053-587X","issn":["1053-587X","1941-0476"],"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 Signal Processing","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/A5100361920","display_name":"Jun Liu","orcid":"https://orcid.org/0000-0002-7193-0622"},"institutions":[{"id":"https://openalex.org/I126520041","display_name":"University of Science and Technology of China","ror":"https://ror.org/04c4dkn09","country_code":"CN","type":"education","lineage":["https://openalex.org/I126520041","https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jun Liu","raw_affiliation_strings":["Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei, China"],"affiliations":[{"raw_affiliation_string":"Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei, China","institution_ids":["https://openalex.org/I126520041"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101537013","display_name":"Yutong Feng","orcid":null},"institutions":[{"id":"https://openalex.org/I126520041","display_name":"University of Science and Technology of China","ror":"https://ror.org/04c4dkn09","country_code":"CN","type":"education","lineage":["https://openalex.org/I126520041","https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yutong Feng","raw_affiliation_strings":["Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei, China"],"affiliations":[{"raw_affiliation_string":"Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei, China","institution_ids":["https://openalex.org/I126520041"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059682113","display_name":"Weijian Liu","orcid":"https://orcid.org/0000-0002-0330-8073"},"institutions":[{"id":"https://openalex.org/I4210134970","display_name":"Wuhan Branch of the National Science Library","ror":"https://ror.org/046n0zq43","country_code":"CN","type":"archive","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210134970","https://openalex.org/I4210148850"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weijian Liu","raw_affiliation_strings":["Wuhan Electronic Information Institute, Wuhan, China"],"affiliations":[{"raw_affiliation_string":"Wuhan Electronic Information Institute, Wuhan, China","institution_ids":["https://openalex.org/I4210134970"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044520390","display_name":"Danilo Orlando","orcid":"https://orcid.org/0000-0001-8630-8505"},"institutions":[{"id":"https://openalex.org/I4210124295","display_name":"University Niccol\u00f2 Cusano","ror":"https://ror.org/032c3ae16","country_code":"IT","type":"education","lineage":["https://openalex.org/I4210124295"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Danilo Orlando","raw_affiliation_strings":["Universit\u00e0 degli Studi \u201cNiccol\u00f2 Cusano\u201d, Rome, Italy"],"affiliations":[{"raw_affiliation_string":"Universit\u00e0 degli Studi \u201cNiccol\u00f2 Cusano\u201d, Rome, Italy","institution_ids":["https://openalex.org/I4210124295"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100365817","display_name":"Hongbin Li","orcid":"https://orcid.org/0000-0003-1453-847X"},"institutions":[{"id":"https://openalex.org/I108468826","display_name":"Stevens Institute of Technology","ror":"https://ror.org/02z43xh36","country_code":"US","type":"education","lineage":["https://openalex.org/I108468826"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hongbin Li","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, USA","institution_ids":["https://openalex.org/I108468826"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5100361920"],"corresponding_institution_ids":["https://openalex.org/I126520041"],"apc_list":null,"apc_paid":null,"fwci":3.0539,"has_fulltext":false,"cited_by_count":23,"citation_normalized_percentile":{"value":0.92725111,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":98},"biblio":{"volume":"68","issue":null,"first_page":"3022","last_page":"3032"},"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/T11871","display_name":"Advanced Statistical Methods and Models","score":0.968500018119812,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12389","display_name":"Infrared Target Detection Methodologies","score":0.9679999947547913,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.7957000732421875},{"id":"https://openalex.org/keywords/constant-false-alarm-rate","display_name":"Constant false alarm rate","score":0.7654417157173157},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.7096893787384033},{"id":"https://openalex.org/keywords/false-alarm","display_name":"False alarm","score":0.6680612564086914},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.6479894518852234},{"id":"https://openalex.org/keywords/covariance-matrix","display_name":"Covariance matrix","score":0.624264657497406},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6176329255104065},{"id":"https://openalex.org/keywords/likelihood-ratio-test","display_name":"Likelihood-ratio test","score":0.5747831463813782},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5174891948699951},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5092562437057495},{"id":"https://openalex.org/keywords/test-data","display_name":"Test data","score":0.48435792326927185},{"id":"https://openalex.org/keywords/wald-test","display_name":"Wald test","score":0.478874146938324},{"id":"https://openalex.org/keywords/covariance","display_name":"Covariance","score":0.46842849254608154},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.4344910681247711},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.4291868507862091},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.42447441816329956},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3718681335449219},{"id":"https://openalex.org/keywords/statistical-hypothesis-testing","display_name":"Statistical hypothesis testing","score":0.3360763192176819},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.28812992572784424},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.2757622003555298}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.7957000732421875},{"id":"https://openalex.org/C77052588","wikidata":"https://www.wikidata.org/wiki/Q644307","display_name":"Constant false alarm rate","level":2,"score":0.7654417157173157},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.7096893787384033},{"id":"https://openalex.org/C2776836416","wikidata":"https://www.wikidata.org/wiki/Q1364844","display_name":"False alarm","level":2,"score":0.6680612564086914},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.6479894518852234},{"id":"https://openalex.org/C185142706","wikidata":"https://www.wikidata.org/wiki/Q1134404","display_name":"Covariance matrix","level":2,"score":0.624264657497406},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6176329255104065},{"id":"https://openalex.org/C9483764","wikidata":"https://www.wikidata.org/wiki/Q585740","display_name":"Likelihood-ratio test","level":2,"score":0.5747831463813782},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5174891948699951},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5092562437057495},{"id":"https://openalex.org/C16910744","wikidata":"https://www.wikidata.org/wiki/Q7705759","display_name":"Test data","level":2,"score":0.48435792326927185},{"id":"https://openalex.org/C203274628","wikidata":"https://www.wikidata.org/wiki/Q748664","display_name":"Wald test","level":3,"score":0.478874146938324},{"id":"https://openalex.org/C178650346","wikidata":"https://www.wikidata.org/wiki/Q201984","display_name":"Covariance","level":2,"score":0.46842849254608154},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.4344910681247711},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.4291868507862091},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.42447441816329956},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3718681335449219},{"id":"https://openalex.org/C87007009","wikidata":"https://www.wikidata.org/wiki/Q210832","display_name":"Statistical hypothesis testing","level":2,"score":0.3360763192176819},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.28812992572784424},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2757622003555298},{"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/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","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},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tsp.2020.2991311","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tsp.2020.2991311","pdf_url":null,"source":{"id":"https://openalex.org/S168680287","display_name":"IEEE Transactions on Signal Processing","issn_l":"1053-587X","issn":["1053-587X","1941-0476"],"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 Signal Processing","raw_type":"journal-article"},{"id":"pmh:oai:arpi.unipi.it:11568/1270491","is_oa":false,"landing_page_url":"https://hdl.handle.net/11568/1270491","pdf_url":null,"source":{"id":"https://openalex.org/S4377196265","display_name":"CINECA IRIS Institutial research information system (University of Pisa)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I108290504","host_organization_name":"University of Pisa","host_organization_lineage":["https://openalex.org/I108290504"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"info:eu-repo/semantics/article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G3580731644","display_name":null,"funder_award_id":"61871469","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3869712404","display_name":null,"funder_award_id":"QYZDY-SSW-JSC035","funder_id":"https://openalex.org/F4320321133","funder_display_name":"Chinese Academy of Sciences"},{"id":"https://openalex.org/G8177953101","display_name":null,"funder_award_id":"CX2100060053","funder_id":"https://openalex.org/F4320335892","funder_display_name":"Youth Innovation Promotion Association"},{"id":"https://openalex.org/G8676638698","display_name":null,"funder_award_id":"WK2100000006","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320321133","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35"},{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null},{"id":"https://openalex.org/F4320335892","display_name":"Youth Innovation Promotion Association","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W391578156","https://openalex.org/W1495949875","https://openalex.org/W1965392255","https://openalex.org/W1970099214","https://openalex.org/W1972578813","https://openalex.org/W1974539967","https://openalex.org/W1983340144","https://openalex.org/W1986921156","https://openalex.org/W1990953362","https://openalex.org/W2017014096","https://openalex.org/W2024138258","https://openalex.org/W2047870694","https://openalex.org/W2067782748","https://openalex.org/W2069205140","https://openalex.org/W2091489596","https://openalex.org/W2096972831","https://openalex.org/W2106999806","https://openalex.org/W2110034185","https://openalex.org/W2112229432","https://openalex.org/W2121824258","https://openalex.org/W2124267685","https://openalex.org/W2124463804","https://openalex.org/W2135038283","https://openalex.org/W2142552707","https://openalex.org/W2144158572","https://openalex.org/W2146152062","https://openalex.org/W2228126342","https://openalex.org/W2288752886","https://openalex.org/W2335197470","https://openalex.org/W2610857016","https://openalex.org/W2768241383","https://openalex.org/W2796629918","https://openalex.org/W2898121906","https://openalex.org/W2900199428","https://openalex.org/W3100364246","https://openalex.org/W4246024215"],"related_works":["https://openalex.org/W2159195662","https://openalex.org/W1983393909","https://openalex.org/W2093700538","https://openalex.org/W2069215735","https://openalex.org/W2354373235","https://openalex.org/W3003095441","https://openalex.org/W2998914743","https://openalex.org/W2040150569","https://openalex.org/W2374441430","https://openalex.org/W1972093502"],"abstract_inverted_index":{"In":[0,60],"this":[1],"paper,":[2],"we":[3],"investigate":[4],"the":[5,33,36,41,45,65,75,100],"anomaly":[6],"detection":[7,73,121],"problem":[8],"for":[9,64],"widespread":[10],"targets":[11],"with":[12],"known":[13],"spacial":[14],"patterns":[15],"under":[16],"a":[17,94],"local":[18],"Gaussian":[19],"model":[20],"when":[21],"training":[22,115,127],"data":[23,111,116],"are":[24,29,54,78,81],"available.":[25],"Three":[26],"adaptive":[27],"detectors":[28,77,92,118],"proposed":[30,76],"based":[31],"on":[32],"principles":[34],"of":[35,67,72,74],"generalized":[37],"likelihood":[38],"ratio":[39],"test,":[40,43,47],"Rao":[42],"and":[44,70,108],"Wald":[46],"respectively.":[48],"We":[49],"prove":[50],"that":[51,90,113],"these":[52,91,114],"tests":[53],"statistically":[55],"equivalent":[56],"to":[57],"each":[58],"other.":[59],"addition,":[61],"analytical":[62],"expressions":[63],"probability":[66,71],"false":[68,96],"alarm":[69,97],"obtained,":[79],"which":[80],"verified":[82],"through":[83],"Monte":[84],"Carlo":[85],"simulations.":[86],"It":[87],"is":[88],"shown":[89],"have":[93,119],"constant":[95],"rate":[98],"against":[99],"covariance":[101],"matrix.":[102],"Finally,":[103],"numerical":[104],"examples":[105],"using":[106],"synthetic":[107],"real":[109],"hyperspectral":[110],"demonstrate":[112],"assisted":[117],"better":[120],"performance":[122],"than":[123],"their":[124],"counterparts":[125],"without":[126],"data.":[128]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":7},{"year":2020,"cited_by_count":2}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}