{"id":"https://openalex.org/W4387595369","doi":"https://doi.org/10.1109/tgrs.2023.3324147","title":"A Tensor-Based Hyperspectral Anomaly Detection Method Under Prior Physical Constraints","display_name":"A Tensor-Based Hyperspectral Anomaly Detection Method Under Prior Physical Constraints","publication_year":2023,"publication_date":"2023-01-01","ids":{"openalex":"https://openalex.org/W4387595369","doi":"https://doi.org/10.1109/tgrs.2023.3324147"},"language":"en","primary_location":{"id":"doi:10.1109/tgrs.2023.3324147","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2023.3324147","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/A5027080860","display_name":"Xin Li","orcid":"https://orcid.org/0000-0001-5883-4540"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xin Li","raw_affiliation_strings":["School of Computer Science and the School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University, Xi&#x2019;an, China"],"raw_orcid":"https://orcid.org/0000-0001-5883-4540","affiliations":[{"raw_affiliation_string":"School of Computer Science and the School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University, Xi&#x2019;an, China","institution_ids":["https://openalex.org/I17145004"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5032595371","display_name":"Yuan Yuan","orcid":"https://orcid.org/0000-0003-1860-3275"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuan Yuan","raw_affiliation_strings":["School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University, Xi&#x2019;an, China"],"raw_orcid":"https://orcid.org/0000-0003-1860-3275","affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University, Xi&#x2019;an, China","institution_ids":["https://openalex.org/I17145004"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5027080860"],"corresponding_institution_ids":["https://openalex.org/I17145004"],"apc_list":null,"apc_paid":null,"fwci":1.0908,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.80744417,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":98},"biblio":{"volume":"61","issue":null,"first_page":"1","last_page":"12"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9071000218391418,"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":0.9071000218391418,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.8629069328308105},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.6983646154403687},{"id":"https://openalex.org/keywords/tensor","display_name":"Tensor (intrinsic definition)","score":0.5979089736938477},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.5598745942115784},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5490425825119019},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4788346588611603},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.47559723258018494},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3591090738773346},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.14423754811286926}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.8629069328308105},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.6983646154403687},{"id":"https://openalex.org/C155281189","wikidata":"https://www.wikidata.org/wiki/Q3518150","display_name":"Tensor (intrinsic definition)","level":2,"score":0.5979089736938477},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.5598745942115784},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5490425825119019},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4788346588611603},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.47559723258018494},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3591090738773346},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.14423754811286926},{"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/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tgrs.2023.3324147","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2023.3324147","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":0.5899999737739563,"id":"https://metadata.un.org/sdg/13","display_name":"Climate action"}],"awards":[{"id":"https://openalex.org/G2210533046","display_name":null,"funder_award_id":"61825603","funder_id":"https://openalex.org/F4320336125","funder_display_name":"National Science Fund for Distinguished Young Scholars"}],"funders":[{"id":"https://openalex.org/F4320336125","display_name":"National Science Fund for Distinguished Young Scholars","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":56,"referenced_works":["https://openalex.org/W1944540851","https://openalex.org/W2004491663","https://openalex.org/W2006262045","https://openalex.org/W2018990310","https://openalex.org/W2021877372","https://openalex.org/W2037034832","https://openalex.org/W2047870694","https://openalex.org/W2084252873","https://openalex.org/W2288752886","https://openalex.org/W2292803159","https://openalex.org/W2295576075","https://openalex.org/W2343117455","https://openalex.org/W2496621835","https://openalex.org/W2505029951","https://openalex.org/W2767039949","https://openalex.org/W2791928749","https://openalex.org/W2796629918","https://openalex.org/W2801018674","https://openalex.org/W2811215163","https://openalex.org/W2948363198","https://openalex.org/W2972480129","https://openalex.org/W2975506318","https://openalex.org/W2986829670","https://openalex.org/W2987226824","https://openalex.org/W2987637330","https://openalex.org/W2987833009","https://openalex.org/W2988699317","https://openalex.org/W3009562877","https://openalex.org/W3012495827","https://openalex.org/W3015560401","https://openalex.org/W3028138626","https://openalex.org/W3034493263","https://openalex.org/W3038851053","https://openalex.org/W3042747521","https://openalex.org/W3080792885","https://openalex.org/W3112037842","https://openalex.org/W3122722892","https://openalex.org/W3124851637","https://openalex.org/W3137199127","https://openalex.org/W3153686193","https://openalex.org/W3175405112","https://openalex.org/W3176520651","https://openalex.org/W3177186825","https://openalex.org/W3189910728","https://openalex.org/W3195044646","https://openalex.org/W3198480870","https://openalex.org/W4200272877","https://openalex.org/W4210576732","https://openalex.org/W4214806231","https://openalex.org/W4225648534","https://openalex.org/W4226078717","https://openalex.org/W4289823735","https://openalex.org/W4294643360","https://openalex.org/W4310854277","https://openalex.org/W4327663519","https://openalex.org/W4383220261"],"related_works":["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","https://openalex.org/W3030345572"],"abstract_inverted_index":{"Efficient":[0],"and":[1,32,74,123,138,156,174,225,262],"precise":[2],"modeling":[3,76],"of":[4,53,77,115,178],"the":[5,12,75,78,105,116,133,136,145,176,182,207,215,226,232,245,252,267,277],"background":[6,79,137,146,183,200,227],"to":[7,14,61,104,125,171,195,211],"accurately":[8],"identify":[9],"anomalies":[10],"is":[11,59,80,184,193,219,228,249],"cornerstone":[13],"hyperspectral":[15,90],"anomaly":[16,91,139,216,233,246,253,274],"detection.":[17],"Hyperspectral":[18],"image":[19,134,191],"(HSI)":[20],"can":[21,168],"be":[22],"regarded":[23],"as":[24,101],"3-D":[25],"cube":[26],"data,":[27],"which":[28,48,118],"contains":[29],"both":[30,154],"spatial":[31,155,204],"spectral":[33,157,223],"information.":[34,55],"The":[35,108],"data":[36],"are":[37,141],"converted":[38],"into":[39,96],"2-D":[40],"matrices":[41],"for":[42],"processing":[43],"in":[44,153,202,221],"most":[45],"existing":[46],"method,":[47],"loses":[49],"a":[50,63,88,102,112,197],"large":[51],"amount":[52],"structural":[54],"In":[56],"addition,":[57],"it":[58],"difficult":[60],"construct":[62],"model":[64,201],"with":[65],"strong":[66],"representation":[67,114],"ability":[68],"without":[69],"enough":[70],"prior":[71,98],"knowledge":[72],"constraints,":[73],"easily":[81],"polluted":[82],"by":[83,159,234,251],"anomalies.":[84,213],"This":[85,167],"article":[86],"introduces":[87],"tensor-based":[89],"detection":[92,247,275],"method":[93,110,208,269],"that":[94,266],"takes":[95],"account":[97],"physical":[99,127],"constraints":[100,152],"solution":[103],"aforementioned":[106],"problems.":[107],"proposed":[109,268],"uses":[111],"tensor":[113,254],"image,":[117],"preserves":[119],"its":[120,203,222],"geometrical":[121],"properties":[122],"adheres":[124],"fundamental":[126],"principles.":[128],"After":[129],"separating":[130],"them":[131],"from":[132,231],"tensor,":[135,147,217],"tensors":[140],"treated":[142],"separately.":[143],"For":[144,214],"we":[148],"introduce":[149],"segmented":[150],"smoothness":[151],"dimensions":[158],"applying":[160],"linear":[161],"total":[162],"variation":[163],"(TV)":[164],"norm":[165],"regularization.":[166],"improve":[169],"resistance":[170],"complicated":[172],"backgrounds":[173],"lessen":[175],"introduction":[177],"extra":[179],"noise":[180],"when":[181],"restored.":[185],"A":[186],"low-rank":[187],"constraint":[188],"based":[189],"on":[190,259],"eigenvalues":[192],"intended":[194],"generate":[196],"more":[198,209],"realistic":[199],"dimension,":[205,224],"making":[206],"sensitive":[210],"tiny":[212],"there":[218],"sparsity":[220],"effectively":[229],"separated":[230],"<inline-formula":[235],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[236],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">":[237],"<tex-math":[238],"notation=\"LaTeX\">$l_{1}":[239],"$":[240],"</tex-math></inline-formula>":[241],"-norm":[242],"constraints.":[243],"Eventually,":[244],"map":[248],"decided":[250],"computed":[255],"iteratively.":[256],"Comprehensive":[257],"experiments":[258],"several":[260],"genuine":[261],"simulated":[263],"datasets":[264],"show":[265],"performs":[270],"significantly":[271],"better":[272],"at":[273],"than":[276],"state-of-the-art":[278],"methods.":[279]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}