{"id":"https://openalex.org/W4387803381","doi":"https://doi.org/10.1109/igarss52108.2023.10283143","title":"Tensor Low-Rank Sparse Representation Learning for Hyperspectral Anomaly Detection","display_name":"Tensor Low-Rank Sparse Representation Learning for Hyperspectral Anomaly Detection","publication_year":2023,"publication_date":"2023-07-16","ids":{"openalex":"https://openalex.org/W4387803381","doi":"https://doi.org/10.1109/igarss52108.2023.10283143"},"language":"en","primary_location":{"id":"doi:10.1109/igarss52108.2023.10283143","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss52108.2023.10283143","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-article"},"type":"conference-paper","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/A5086628774","display_name":"Qingjiang Xiao","orcid":"https://orcid.org/0000-0002-4942-5611"},"institutions":[{"id":"https://openalex.org/I50760025","display_name":"Hangzhou Dianzi University","ror":"https://ror.org/0576gt767","country_code":"CN","type":"education","lineage":["https://openalex.org/I50760025"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qingjiang Xiao","raw_affiliation_strings":["Hangzhou Dianzi University,Computer and Software School,Hangzhou,China,310018"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hangzhou Dianzi University,Computer and Software School,Hangzhou,China,310018","institution_ids":["https://openalex.org/I50760025"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054794945","display_name":"Liaoying Zhao","orcid":"https://orcid.org/0000-0002-9276-8679"},"institutions":[{"id":"https://openalex.org/I50760025","display_name":"Hangzhou Dianzi University","ror":"https://ror.org/0576gt767","country_code":"CN","type":"education","lineage":["https://openalex.org/I50760025"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liaoying Zhao","raw_affiliation_strings":["Hangzhou Dianzi University,Computer and Software School,Hangzhou,China,310018"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hangzhou Dianzi University,Computer and Software School,Hangzhou,China,310018","institution_ids":["https://openalex.org/I50760025"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5028845261","display_name":"Shuhan Chen","orcid":"https://orcid.org/0000-0001-9996-0666"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shuhan Chen","raw_affiliation_strings":["Zhejiang University,College of Electrical Engineering,Hangzhou,China,310027"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Zhejiang University,College of Electrical Engineering,Hangzhou,China,310027","institution_ids":["https://openalex.org/I76130692"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"7356","last_page":"7359"},"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/T13890","display_name":"Remote Sensing and Land Use","score":0.9853000044822693,"subfield":{"id":"https://openalex.org/subfields/1902","display_name":"Atmospheric Science"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9757999777793884,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.9562383890151978},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6451162099838257},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.637673556804657},{"id":"https://openalex.org/keywords/tensor","display_name":"Tensor (intrinsic definition)","score":0.6095225811004639},{"id":"https://openalex.org/keywords/principal-component-analysis","display_name":"Principal component analysis","score":0.6074910163879395},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6042702794075012},{"id":"https://openalex.org/keywords/data-cube","display_name":"Data cube","score":0.5982500314712524},{"id":"https://openalex.org/keywords/full-spectral-imaging","display_name":"Full spectral imaging","score":0.5453327894210815},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5407185554504395},{"id":"https://openalex.org/keywords/rank","display_name":"Rank (graph theory)","score":0.53853839635849},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.4481644630432129},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4141910672187805},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.41218051314353943},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.41013437509536743},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.2427276372909546},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.11615288257598877},{"id":"https://openalex.org/keywords/combinatorics","display_name":"Combinatorics","score":0.0917978286743164}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.9562383890151978},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6451162099838257},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.637673556804657},{"id":"https://openalex.org/C155281189","wikidata":"https://www.wikidata.org/wiki/Q3518150","display_name":"Tensor (intrinsic definition)","level":2,"score":0.6095225811004639},{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.6074910163879395},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6042702794075012},{"id":"https://openalex.org/C78168278","wikidata":"https://www.wikidata.org/wiki/Q5227269","display_name":"Data cube","level":2,"score":0.5982500314712524},{"id":"https://openalex.org/C78660771","wikidata":"https://www.wikidata.org/wiki/Q5508206","display_name":"Full spectral imaging","level":3,"score":0.5453327894210815},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5407185554504395},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.53853839635849},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.4481644630432129},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4141910672187805},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.41218051314353943},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.41013437509536743},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2427276372909546},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.11615288257598877},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0917978286743164},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"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/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss52108.2023.10283143","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss52108.2023.10283143","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320311649","display_name":"Ministry of Education","ror":"https://ror.org/036nq5137"},{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W2004491663","https://openalex.org/W2024288510","https://openalex.org/W2047870694","https://openalex.org/W2067897118","https://openalex.org/W2288752886","https://openalex.org/W2295576075","https://openalex.org/W2972480129","https://openalex.org/W3087883793","https://openalex.org/W3112037842","https://openalex.org/W3130212419","https://openalex.org/W4225850527","https://openalex.org/W4282929851"],"related_works":["https://openalex.org/W4386427838","https://openalex.org/W2889956472","https://openalex.org/W2911259277","https://openalex.org/W1982418987","https://openalex.org/W3178760882","https://openalex.org/W2908721991","https://openalex.org/W1556234160","https://openalex.org/W2039721451","https://openalex.org/W2053822900","https://openalex.org/W2766231676"],"abstract_inverted_index":{"Some":[0],"existing":[1],"anomaly":[2,48,98],"detection":[3,31,49],"methods":[4],"convert":[5],"a":[6,12,38,126],"3-D":[7],"hyperspectral":[8,24,47,60,93,137],"data":[9],"cube":[10],"into":[11],"2-D":[13],"matrix,":[14],"which":[15,51],"inevitably":[16],"destroys":[17],"the":[18,23,28,55,80,85,100,107,114,140,143],"spatial-spectral":[19,56,89],"structure":[20,57,90],"information":[21,91],"of":[22,30,58,92,110,142],"data,":[25],"resulting":[26],"in":[27],"degradation":[29],"performance.":[32],"In":[33],"this":[34],"paper,":[35],"we":[36],"propose":[37],"tensor":[39,65,82,101,115],"low-rank":[40,66,71],"sparse":[41],"representation":[42,67],"learning":[43],"(TLRAD)":[44],"method":[45],"for":[46],"(HAD),":[50],"can":[52],"effectively":[53],"maintain":[54],"raw":[59],"data.":[61],"Specifically,":[62],"based":[63],"on":[64,79],"(TLRR)":[68],"learning,":[69],"both":[70],"constraints":[72,75],"and":[73,87],"sparsity":[74,109],"are":[76],"simultaneously":[77],"imposed":[78],"coefficient":[81],"to":[83,105,124,147],"capture":[84],"global":[86],"local":[88],"image":[94],"(HSI),":[95],"respectively.":[96],"For":[97],"tensor,":[99],"\u211321-norm":[102],"is":[103,122],"applied":[104],"encourage":[106],"group":[108],"anomalous":[111],"pixels.":[112],"Furthermore,":[113],"robust":[116,127],"principal":[117],"component":[118],"analysis":[119],"(TRPCA)":[120],"approach":[121,145],"utilized":[123],"construct":[125],"background":[128],"dictionary":[129],"tensor.":[130],"Experimental":[131],"results":[132],"gained":[133],"employing":[134],"two":[135],"real":[136],"datasets":[138],"prove":[139],"superiority":[141],"proposed":[144],"compared":[146],"some":[148],"state-of-the-art":[149],"algorithms.":[150]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":3}],"updated_date":"2026-07-29T09:40:50.615796","created_date":"2025-10-10T00:00:00"}
