{"id":"https://openalex.org/W2972480129","doi":"https://doi.org/10.1109/tgrs.2019.2936609","title":"Graph and Total Variation Regularized Low-Rank Representation for Hyperspectral Anomaly Detection","display_name":"Graph and Total Variation Regularized Low-Rank Representation for Hyperspectral Anomaly Detection","publication_year":2019,"publication_date":"2019-09-11","ids":{"openalex":"https://openalex.org/W2972480129","doi":"https://doi.org/10.1109/tgrs.2019.2936609","mag":"2972480129"},"language":"en","primary_location":{"id":"doi:10.1109/tgrs.2019.2936609","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2019.2936609","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/A5076569259","display_name":"Tongkai Cheng","orcid":"https://orcid.org/0000-0003-3940-7089"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Tongkai Cheng","raw_affiliation_strings":["Key Laboratory for Information Science of Electromagnetic Waves (MoE), Fudan University, Shanghai, China","Research Center of Smart Networks and Systems, School of Information Science and Technology, Fudan University, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0003-3940-7089","affiliations":[{"raw_affiliation_string":"Key Laboratory for Information Science of Electromagnetic Waves (MoE), Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]},{"raw_affiliation_string":"Research Center of Smart Networks and Systems, School of Information Science and Technology, Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5059575276","display_name":"Bin Wang","orcid":"https://orcid.org/0000-0003-4748-6426"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bin Wang","raw_affiliation_strings":["Key Laboratory for Information Science of Electromagnetic Waves (MoE), Fudan University, Shanghai, China","Research Center of Smart Networks and Systems, School of Information Science and Technology, Fudan University, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0003-4748-6426","affiliations":[{"raw_affiliation_string":"Key Laboratory for Information Science of Electromagnetic Waves (MoE), Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]},{"raw_affiliation_string":"Research Center of Smart Networks and Systems, School of Information Science and Technology, Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5076569259"],"corresponding_institution_ids":["https://openalex.org/I24943067"],"apc_list":null,"apc_paid":null,"fwci":12.6836,"has_fulltext":false,"cited_by_count":246,"citation_normalized_percentile":{"value":0.98795249,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":100},"biblio":{"volume":"58","issue":"1","first_page":"391","last_page":"406"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":1.0,"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":1.0,"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":0.9907000064849854,"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.9883999824523926,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.9679353833198547},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.7503941655158997},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.6107956171035767},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5933665037155151},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5907192230224609},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5461221933364868},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.5122991800308228},{"id":"https://openalex.org/keywords/full-spectral-imaging","display_name":"Full spectral imaging","score":0.5092805624008179},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4972987473011017},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.4201418161392212},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.34110480546951294},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.3373580574989319},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.12149304151535034},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.07874670624732971}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.9679353833198547},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.7503941655158997},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.6107956171035767},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5933665037155151},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5907192230224609},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5461221933364868},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.5122991800308228},{"id":"https://openalex.org/C78660771","wikidata":"https://www.wikidata.org/wiki/Q5508206","display_name":"Full spectral imaging","level":3,"score":0.5092805624008179},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4972987473011017},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.4201418161392212},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.34110480546951294},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.3373580574989319},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.12149304151535034},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.07874670624732971},{"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.2019.2936609","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2019.2936609","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":0.5199999809265137,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"},{"score":0.47999998927116394,"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions"}],"awards":[{"id":"https://openalex.org/G3455612598","display_name":null,"funder_award_id":"61572133","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5041054916","display_name":null,"funder_award_id":"61971141","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5277018974","display_name":null,"funder_award_id":"61731021","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"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":59,"referenced_works":["https://openalex.org/W1736339626","https://openalex.org/W1964570608","https://openalex.org/W1988177629","https://openalex.org/W1990953362","https://openalex.org/W1997201895","https://openalex.org/W2001141328","https://openalex.org/W2001515539","https://openalex.org/W2004491663","https://openalex.org/W2011181254","https://openalex.org/W2017014096","https://openalex.org/W2037034832","https://openalex.org/W2040078680","https://openalex.org/W2042442393","https://openalex.org/W2047519171","https://openalex.org/W2047870694","https://openalex.org/W2048976066","https://openalex.org/W2053186076","https://openalex.org/W2063069198","https://openalex.org/W2067782748","https://openalex.org/W2070424424","https://openalex.org/W2073774279","https://openalex.org/W2097381359","https://openalex.org/W2097915756","https://openalex.org/W2098392363","https://openalex.org/W2103559027","https://openalex.org/W2103972604","https://openalex.org/W2107799335","https://openalex.org/W2116793806","https://openalex.org/W2118384613","https://openalex.org/W2118996198","https://openalex.org/W2124267685","https://openalex.org/W2124463804","https://openalex.org/W2127495569","https://openalex.org/W2140245639","https://openalex.org/W2144719328","https://openalex.org/W2145962650","https://openalex.org/W2146820417","https://openalex.org/W2156718197","https://openalex.org/W2159966040","https://openalex.org/W2163129097","https://openalex.org/W2163816481","https://openalex.org/W2163957348","https://openalex.org/W2164278908","https://openalex.org/W2165447611","https://openalex.org/W2167320870","https://openalex.org/W2262946425","https://openalex.org/W2288752886","https://openalex.org/W2295576075","https://openalex.org/W2303627748","https://openalex.org/W2326057689","https://openalex.org/W2756635220","https://openalex.org/W2782517596","https://openalex.org/W2898121906","https://openalex.org/W2962898849","https://openalex.org/W4292363360","https://openalex.org/W6669036798","https://openalex.org/W6677508755","https://openalex.org/W6682755970","https://openalex.org/W6929385289"],"related_works":["https://openalex.org/W2911259277","https://openalex.org/W2800956885","https://openalex.org/W2533019003","https://openalex.org/W1788560349","https://openalex.org/W2626158795","https://openalex.org/W2078656815","https://openalex.org/W2391021239","https://openalex.org/W2806741695","https://openalex.org/W2560525382","https://openalex.org/W2126575155"],"abstract_inverted_index":{"Anomaly":[0],"detection":[1,26,145,196],"is":[2,112],"of":[3,24,36,45,59,74,90,108,187],"great":[4,57],"importance":[5],"among":[6],"hyperspectral":[7,75,96,110,166,178],"applications,":[8],"which":[9],"aims":[10],"at":[11],"locating":[12],"targets":[13],"that":[14,103],"are":[15],"spectrally":[16],"different":[17],"from":[18],"their":[19],"surrounding":[20],"background.":[21],"A":[22],"variety":[23],"anomaly":[25,144,195],"methods":[27],"have":[28,170],"been":[29,101,171],"proposed":[30,189],"in":[31,61,95,165],"the":[32,41,47,70,78,84,91,104,109,118,128,137,158,185,188],"past.":[33],"However,":[34],"most":[35],"them":[37],"fail":[38],"to":[39,68,156],"take":[40],"high":[42],"spectral":[43,85],"correlations":[44],"all":[46],"pixels":[48],"into":[49,136],"consideration.":[50],"Low-rank":[51],"representation":[52],"(LRR)":[53],"has":[54,100],"drawn":[55],"a":[56,65,142],"deal":[58],"interest":[60],"recent":[62],"years,":[63],"as":[64],"promising":[66],"model":[67,81],"exploit":[69],"intrinsic":[71],"low-rank":[72],"property":[73],"images.":[76,97,167],"Nevertheless,":[77],"original":[79],"LRR":[80,138,153],"only":[82],"analyzes":[83],"signatures":[86],"without":[87],"taking":[88],"advantage":[89],"valuable":[92],"spatial":[93,163],"information":[94,107],"Furthermore,":[98],"it":[99],"shown":[102],"local":[105,159],"geometrical":[106,160],"data":[111,179],"also":[113],"important":[114],"for":[115],"discrimination":[116],"between":[117],"anomalies":[119],"and":[120,131,140,150,162,176,193],"background":[121],"pixels.":[122],"In":[123],"this":[124],"article,":[125],"we":[126],"incorporate":[127],"graph":[129,149],"regularization":[130,135],"total":[132],"variation":[133],"(TV)":[134],"formulation":[139],"propose":[141],"novel":[143],"method":[146,190],"based":[147],"on":[148,173],"TV":[151],"regularized":[152],"(GTVLRR)":[154],"model,":[155],"preserve":[157],"structure":[161],"relationships":[164],"Extensive":[168],"experiments":[169],"conducted":[172],"both":[174],"simulated":[175],"real":[177],"sets.":[180],"The":[181],"experimental":[182],"results":[183],"demonstrate":[184],"superiority":[186],"over":[191],"conventional":[192],"state-of-the-art":[194],"methods.":[197]},"counts_by_year":[{"year":2026,"cited_by_count":10},{"year":2025,"cited_by_count":69},{"year":2024,"cited_by_count":55},{"year":2023,"cited_by_count":42},{"year":2022,"cited_by_count":41},{"year":2021,"cited_by_count":25},{"year":2020,"cited_by_count":4}],"updated_date":"2026-05-10T08:33:47.465468","created_date":"2025-10-10T00:00:00"}
