{"id":"https://openalex.org/W2424277038","doi":"https://doi.org/10.1109/tgrs.2016.2572400","title":"A Tensor Decomposition-Based Anomaly Detection Algorithm for Hyperspectral Image","display_name":"A Tensor Decomposition-Based Anomaly Detection Algorithm for Hyperspectral Image","publication_year":2016,"publication_date":"2016-06-16","ids":{"openalex":"https://openalex.org/W2424277038","doi":"https://doi.org/10.1109/tgrs.2016.2572400","mag":"2424277038"},"language":"en","primary_location":{"id":"doi:10.1109/tgrs.2016.2572400","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2016.2572400","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/A5100625600","display_name":"Xing Zhang","orcid":"https://orcid.org/0000-0001-6791-5748"},"institutions":[{"id":"https://openalex.org/I170215575","display_name":"National University of Defense Technology","ror":"https://ror.org/05d2yfz11","country_code":"CN","type":"education","lineage":["https://openalex.org/I170215575"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xing Zhang","raw_affiliation_strings":["Automatic Target Recognition Laboratory, School of Electronic Science and Engineering, National University of Defense Technology, Changsha, China"],"affiliations":[{"raw_affiliation_string":"Automatic Target Recognition Laboratory, School of Electronic Science and Engineering, National University of Defense Technology, Changsha, China","institution_ids":["https://openalex.org/I170215575"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110203773","display_name":"Gongjian Wen","orcid":null},"institutions":[{"id":"https://openalex.org/I170215575","display_name":"National University of Defense Technology","ror":"https://ror.org/05d2yfz11","country_code":"CN","type":"education","lineage":["https://openalex.org/I170215575"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Gongjian Wen","raw_affiliation_strings":["Automatic Target Recognition Laboratory, School of Electronic Science and Engineering, National University of Defense Technology, Changsha, China"],"affiliations":[{"raw_affiliation_string":"Automatic Target Recognition Laboratory, School of Electronic Science and Engineering, National University of Defense Technology, Changsha, China","institution_ids":["https://openalex.org/I170215575"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100671164","display_name":"Wei Dai","orcid":"https://orcid.org/0000-0002-7571-4863"},"institutions":[{"id":"https://openalex.org/I170215575","display_name":"National University of Defense Technology","ror":"https://ror.org/05d2yfz11","country_code":"CN","type":"education","lineage":["https://openalex.org/I170215575"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Dai","raw_affiliation_strings":["Automatic Target Recognition Laboratory, School of Electronic Science and Engineering, National University of Defense Technology, Changsha, China"],"affiliations":[{"raw_affiliation_string":"Automatic Target Recognition Laboratory, School of Electronic Science and Engineering, National University of Defense Technology, Changsha, China","institution_ids":["https://openalex.org/I170215575"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5100625600"],"corresponding_institution_ids":["https://openalex.org/I170215575"],"apc_list":null,"apc_paid":null,"fwci":11.6262,"has_fulltext":false,"cited_by_count":159,"citation_normalized_percentile":{"value":0.98667968,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":100},"biblio":{"volume":"54","issue":"10","first_page":"5801","last_page":"5820"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9994000196456909,"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":0.9994000196456909,"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/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9926000237464905,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"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/T12303","display_name":"Tensor decomposition and applications","score":0.9894000291824341,"subfield":{"id":"https://openalex.org/subfields/2605","display_name":"Computational Mathematics"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.8297649621963501},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.7000830173492432},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.6677934527397156},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.612199604511261},{"id":"https://openalex.org/keywords/tensor","display_name":"Tensor (intrinsic definition)","score":0.5892603397369385},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.557537853717804},{"id":"https://openalex.org/keywords/tucker-decomposition","display_name":"Tucker decomposition","score":0.4921721816062927},{"id":"https://openalex.org/keywords/matrix-decomposition","display_name":"Matrix decomposition","score":0.4732915759086609},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4412304759025574},{"id":"https://openalex.org/keywords/principal-component-analysis","display_name":"Principal component analysis","score":0.43193119764328003},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.42271688580513},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.374123215675354},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.36066651344299316},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.22432035207748413},{"id":"https://openalex.org/keywords/tensor-decomposition","display_name":"Tensor decomposition","score":0.21827059984207153},{"id":"https://openalex.org/keywords/eigenvalues-and-eigenvectors","display_name":"Eigenvalues and eigenvectors","score":0.1625669300556183}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.8297649621963501},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.7000830173492432},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.6677934527397156},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.612199604511261},{"id":"https://openalex.org/C155281189","wikidata":"https://www.wikidata.org/wiki/Q3518150","display_name":"Tensor (intrinsic definition)","level":2,"score":0.5892603397369385},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.557537853717804},{"id":"https://openalex.org/C42704193","wikidata":"https://www.wikidata.org/wiki/Q7851097","display_name":"Tucker decomposition","level":4,"score":0.4921721816062927},{"id":"https://openalex.org/C42355184","wikidata":"https://www.wikidata.org/wiki/Q1361088","display_name":"Matrix decomposition","level":3,"score":0.4732915759086609},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4412304759025574},{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.43193119764328003},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.42271688580513},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.374123215675354},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.36066651344299316},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.22432035207748413},{"id":"https://openalex.org/C2986737658","wikidata":"https://www.wikidata.org/wiki/Q30103009","display_name":"Tensor decomposition","level":3,"score":0.21827059984207153},{"id":"https://openalex.org/C158693339","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Eigenvalues and eigenvectors","level":2,"score":0.1625669300556183},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"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/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tgrs.2016.2572400","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2016.2572400","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":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":45,"referenced_works":["https://openalex.org/W32702425","https://openalex.org/W1970099214","https://openalex.org/W1972578813","https://openalex.org/W1977355761","https://openalex.org/W1988177629","https://openalex.org/W1990953362","https://openalex.org/W1991042426","https://openalex.org/W1991190032","https://openalex.org/W1993337810","https://openalex.org/W2004491663","https://openalex.org/W2005106632","https://openalex.org/W2007449884","https://openalex.org/W2011147915","https://openalex.org/W2013912476","https://openalex.org/W2017014096","https://openalex.org/W2018282388","https://openalex.org/W2024138258","https://openalex.org/W2024165284","https://openalex.org/W2037034832","https://openalex.org/W2040078680","https://openalex.org/W2045431957","https://openalex.org/W2047519171","https://openalex.org/W2047870694","https://openalex.org/W2064916278","https://openalex.org/W2069138919","https://openalex.org/W2070721261","https://openalex.org/W2086506050","https://openalex.org/W2094492907","https://openalex.org/W2097381359","https://openalex.org/W2116793806","https://openalex.org/W2118996198","https://openalex.org/W2124267685","https://openalex.org/W2129498797","https://openalex.org/W2137972458","https://openalex.org/W2140340527","https://openalex.org/W2145858287","https://openalex.org/W2147042314","https://openalex.org/W2149936180","https://openalex.org/W2158340226","https://openalex.org/W2161037015","https://openalex.org/W2163816481","https://openalex.org/W2163957348","https://openalex.org/W2167708128","https://openalex.org/W2538782815","https://openalex.org/W6677508755"],"related_works":["https://openalex.org/W2116776498","https://openalex.org/W2806741695","https://openalex.org/W3210364259","https://openalex.org/W4290647774","https://openalex.org/W3189286258","https://openalex.org/W3207797160","https://openalex.org/W2912112202","https://openalex.org/W2667207928","https://openalex.org/W4300558037","https://openalex.org/W4377864969"],"abstract_inverted_index":{"Anomalies":[0],"usually":[1],"refer":[2],"to":[3,26,47,119,124,202,204,224,233,270],"targets":[4],"with":[5,260,279,296],"a":[6,92,148,168,219,261,265],"spot":[7],"of":[8,72,114,142,208,238,257],"pixels":[9,20,61],"(even":[10,62],"subpixels)":[11,63],"that":[12,178,288],"stand":[13],"out":[14],"from":[15,44,174,181,274],"their":[16,39,48,192],"neighboring":[17],"background":[18],"clutter":[19],"in":[21,65,91],"hyperspectral":[22],"imagery":[23],"(HSI).":[24],"Compared":[25],"backgrounds,":[27],"anomalies":[28,57,143,259,273],"have":[29],"two":[30,87,140],"main":[31],"characteristics.":[32],"One":[33],"is":[34,52,135,157,179,222,268],"the":[35,53,66,73,82,86,126,139,175,182,188,205,209,212,216,226,235,239,247,254,258,272,275,289],"spectral":[36,40,83,129,206,236,255],"anomaly,":[37,55],"i.e.,":[38,56],"signatures":[41,207,237,256],"are":[42,89,171,199],"different":[43],"those":[45,293],"associated":[46],"surrounding":[49],"backgrounds;":[50],"another":[51],"spatial":[54,127],"occur":[58],"as":[59,241,243,253],"few":[60],"embedded":[64],"local":[67],"homogeneous":[68],"backgrounds.":[69],"However,":[70],"most":[71],"existing":[74],"anomaly":[75,150],"detection":[76,93,151,298],"algorithms":[77],"for":[78,105,110,137],"HSI":[79,115,183,284],"only":[80],"employed":[81],"anomaly.":[84],"If":[85],"characteristics":[88,141],"exploited":[90],"method":[94,152,221,291],"simultaneously,":[95],"better":[96,297],"performance":[97],"may":[98,250],"be":[99,120,251],"achieved.":[100],"The":[101],"third-order":[102,176],"(two":[103],"modes":[104],"space":[106],"and":[107,128,159,167,191,194,211,245,282,300],"one":[108],"mode":[109,232],"spectra)":[111],"tensor":[112,133,155,170,177],"representation":[113,134],"has":[116],"been":[117],"proved":[118],"an":[121],"effective":[122],"tool":[123],"describe":[125],"information":[130],"equivalently;":[131],"therefore,":[132],"convenient":[136],"exhibiting":[138],"simultaneously.":[144],"In":[145,215],"this":[146],"paper,":[147],"new":[149],"based":[153],"on":[154],"decomposition":[156],"proposed":[158,290],"divided":[160],"into":[161],"three":[162],"steps.":[163],"Three":[164],"factor":[165],"matrices":[166],"core":[169],"first":[172,227],"estimated":[173],"constructed":[180],"data":[184,249,285],"cube":[185],"by":[186],"using":[187],"Tucker":[189],"decomposition,":[190],"major":[193],"minor":[195],"principal":[196],"components":[197],"(PCs)":[198],"more":[200],"likely":[201],"correspond":[203],"backgrounds":[210,240],"anomalies,":[213],"respectively.":[214],"second":[217],"step,":[218],"reconstruction-error-based":[220],"presented":[223],"find":[225],"largest":[228],"PCs":[229],"along":[230],"each":[231],"eliminate":[234],"much":[242],"possible,":[244],"thus,":[246],"remaining":[248,276],"modeled":[252],"Gaussian":[262],"noise.":[263],"Finally,":[264],"CFAR":[266],"test":[267],"implemented":[269],"detect":[271],"data.":[277],"Experiments":[278],"simulated,":[280],"synthetic,":[281],"real":[283],"sets":[286],"reveal":[287],"outperforms":[292],"spectral-anomaly-based":[294],"methods":[295],"probability":[299],"less":[301],"false":[302],"alarm":[303],"rate.":[304]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":18},{"year":2024,"cited_by_count":12},{"year":2023,"cited_by_count":18},{"year":2022,"cited_by_count":32},{"year":2021,"cited_by_count":25},{"year":2020,"cited_by_count":16},{"year":2019,"cited_by_count":15},{"year":2018,"cited_by_count":15},{"year":2017,"cited_by_count":6}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
