{"id":"https://openalex.org/W2959891261","doi":"https://doi.org/10.1109/jstars.2019.2926130","title":"Hyperspectral Anomaly Detection via Convolutional Neural Network and Low Rank With Density-Based Clustering","display_name":"Hyperspectral Anomaly Detection via Convolutional Neural Network and Low Rank With Density-Based Clustering","publication_year":2019,"publication_date":"2019-07-12","ids":{"openalex":"https://openalex.org/W2959891261","doi":"https://doi.org/10.1109/jstars.2019.2926130","mag":"2959891261"},"language":"en","primary_location":{"id":"doi:10.1109/jstars.2019.2926130","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jstars.2019.2926130","pdf_url":null,"source":{"id":"https://openalex.org/S117727964","display_name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","issn_l":"1939-1404","issn":["1939-1404","2151-1535"],"is_oa":false,"is_in_doaj":true,"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 Journal of Selected Topics in Applied Earth Observations and Remote Sensing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"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/A5050294734","display_name":"Shangzhen Song","orcid":"https://orcid.org/0000-0002-0364-6670"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Shangzhen Song","raw_affiliation_strings":["School of Physics and Optoelectronic Engineering, Xidian University, Xi\u2019an, China","School of Physics and Optoelectronic Engineering, Xidian University, Xi'an, China"],"affiliations":[{"raw_affiliation_string":"School of Physics and Optoelectronic Engineering, Xidian University, Xi\u2019an, China","institution_ids":["https://openalex.org/I149594827"]},{"raw_affiliation_string":"School of Physics and Optoelectronic Engineering, Xidian University, Xi'an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5031563962","display_name":"Huixin Zhou","orcid":"https://orcid.org/0009-0008-0742-1621"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Huixin Zhou","raw_affiliation_strings":["School of Physics and Optoelectronic Engineering, Xidian University, Xi\u2019an, China","School of Physics and Optoelectronic Engineering, Xidian University, Xi'an, China"],"affiliations":[{"raw_affiliation_string":"School of Physics and Optoelectronic Engineering, Xidian University, Xi\u2019an, China","institution_ids":["https://openalex.org/I149594827"]},{"raw_affiliation_string":"School of Physics and Optoelectronic Engineering, Xidian University, Xi'an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052864195","display_name":"Yixin Yang","orcid":"https://orcid.org/0000-0003-1635-8835"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yixin Yang","raw_affiliation_strings":["School of Physics and Optoelectronic Engineering, Xidian University, Xi\u2019an, China","School of Physics and Optoelectronic Engineering, Xidian University, Xi'an, China"],"affiliations":[{"raw_affiliation_string":"School of Physics and Optoelectronic Engineering, Xidian University, Xi\u2019an, China","institution_ids":["https://openalex.org/I149594827"]},{"raw_affiliation_string":"School of Physics and Optoelectronic Engineering, Xidian University, Xi'an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5016174909","display_name":"Jiangluqi Song","orcid":"https://orcid.org/0000-0003-1497-4810"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiangluqi Song","raw_affiliation_strings":["School of Physics and Optoelectronic Engineering, Xidian University, Xi\u2019an, China","School of Physics and Optoelectronic Engineering, Xidian University, Xi'an, China"],"affiliations":[{"raw_affiliation_string":"School of Physics and Optoelectronic Engineering, Xidian University, Xi\u2019an, China","institution_ids":["https://openalex.org/I149594827"]},{"raw_affiliation_string":"School of Physics and Optoelectronic Engineering, Xidian University, Xi'an, China","institution_ids":["https://openalex.org/I149594827"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5050294734"],"corresponding_institution_ids":["https://openalex.org/I149594827"],"apc_list":{"value":1250,"currency":"USD","value_usd":1250},"apc_paid":null,"fwci":7.7815,"has_fulltext":false,"cited_by_count":98,"citation_normalized_percentile":{"value":0.97541124,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":"12","issue":"9","first_page":"3637","last_page":"3649"},"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/T11667","display_name":"Advanced Chemical Sensor Technologies","score":0.9776999950408936,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"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":0.9692000150680542,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.8462780714035034},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7712129354476929},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7008843421936035},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6716336011886597},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.596439778804779},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.5921634435653687},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.5533719062805176},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.5318759679794312},{"id":"https://openalex.org/keywords/rank","display_name":"Rank (graph theory)","score":0.4668095111846924},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4506804645061493},{"id":"https://openalex.org/keywords/matrix-decomposition","display_name":"Matrix decomposition","score":0.4407033920288086},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4279375672340393},{"id":"https://openalex.org/keywords/sparse-matrix","display_name":"Sparse matrix","score":0.4123896360397339},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.21683472394943237},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.20826146006584167},{"id":"https://openalex.org/keywords/eigenvalues-and-eigenvectors","display_name":"Eigenvalues and eigenvectors","score":0.15796443819999695}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.8462780714035034},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7712129354476929},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7008843421936035},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6716336011886597},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.596439778804779},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.5921634435653687},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.5533719062805176},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.5318759679794312},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.4668095111846924},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4506804645061493},{"id":"https://openalex.org/C42355184","wikidata":"https://www.wikidata.org/wiki/Q1361088","display_name":"Matrix decomposition","level":3,"score":0.4407033920288086},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4279375672340393},{"id":"https://openalex.org/C56372850","wikidata":"https://www.wikidata.org/wiki/Q1050404","display_name":"Sparse matrix","level":3,"score":0.4123896360397339},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.21683472394943237},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.20826146006584167},{"id":"https://openalex.org/C158693339","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Eigenvalues and eigenvectors","level":2,"score":0.15796443819999695},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","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/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","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},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/jstars.2019.2926130","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jstars.2019.2926130","pdf_url":null,"source":{"id":"https://openalex.org/S117727964","display_name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","issn_l":"1939-1404","issn":["1939-1404","2151-1535"],"is_oa":false,"is_in_doaj":true,"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 Journal of Selected Topics in Applied Earth Observations and Remote Sensing","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G4368698176","display_name":null,"funder_award_id":"51801142","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5941877585","display_name":null,"funder_award_id":"JB180502","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/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":48,"referenced_works":["https://openalex.org/W79405465","https://openalex.org/W1555148682","https://openalex.org/W1587559447","https://openalex.org/W1673310716","https://openalex.org/W1970099214","https://openalex.org/W1976935203","https://openalex.org/W1977556410","https://openalex.org/W1981939910","https://openalex.org/W1983364832","https://openalex.org/W1991190032","https://openalex.org/W1997565609","https://openalex.org/W2004491663","https://openalex.org/W2024288510","https://openalex.org/W2040078680","https://openalex.org/W2047870694","https://openalex.org/W2067782748","https://openalex.org/W2087263574","https://openalex.org/W2088429922","https://openalex.org/W2097381359","https://openalex.org/W2101837437","https://openalex.org/W2106777458","https://openalex.org/W2117741752","https://openalex.org/W2123649031","https://openalex.org/W2124463804","https://openalex.org/W2129498797","https://openalex.org/W2140815491","https://openalex.org/W2141224535","https://openalex.org/W2141494774","https://openalex.org/W2142077116","https://openalex.org/W2145962650","https://openalex.org/W2147042314","https://openalex.org/W2149936180","https://openalex.org/W2158340226","https://openalex.org/W2163886442","https://openalex.org/W2163957348","https://openalex.org/W2165447611","https://openalex.org/W2288752886","https://openalex.org/W2292987679","https://openalex.org/W2295576075","https://openalex.org/W2793367532","https://openalex.org/W2796629918","https://openalex.org/W2803061281","https://openalex.org/W2886042776","https://openalex.org/W2936775474","https://openalex.org/W6603183647","https://openalex.org/W6633204899","https://openalex.org/W6637131181","https://openalex.org/W6681016373"],"related_works":["https://openalex.org/W2072166414","https://openalex.org/W3209970181","https://openalex.org/W2060875994","https://openalex.org/W3034375524","https://openalex.org/W4230131218","https://openalex.org/W2070598848","https://openalex.org/W2404757046","https://openalex.org/W2898722594","https://openalex.org/W2125326641","https://openalex.org/W133838137"],"abstract_inverted_index":{"Over":[0],"the":[1,27,30,36,87,93,106,114,127,148,158,178,186,197,201],"last":[2],"two":[3],"decades,":[4],"anomaly":[5],"detection":[6],"(AD)":[7],"has":[8],"been":[9],"known":[10],"to":[11,25,84,102,124,147,194],"play":[12],"a":[13,22,44,55,72,109,131,143,161,190],"critical":[14],"role":[15],"in":[16,200],"hyperspectral":[17,50,80,204],"image":[18,81],"analysis,":[19],"which":[20,153,169],"provides":[21],"new":[23],"way":[24],"distinguish":[26],"targets":[28],"from":[29,105,177],"background":[31,128],"without":[32],"prior":[33],"knowledge.":[34],"Recently,":[35],"representation-based":[37],"methods":[38,48,199],"were":[39],"proposed":[40,187],"and":[41,65,77],"soon":[42],"became":[43],"significant":[45],"type":[46],"of":[47,118,196,203],"on":[49,60,79,136],"AD.":[51,205],"In":[52,140],"this":[53,141],"paper,":[54],"novel":[56],"AD":[57],"algorithm":[58,123],"based":[59,135],"convolutional":[61],"neural":[62],"network":[63],"(CNN)":[64],"low-rank":[66],"representation":[67],"(LRR)":[68],"is":[69,75,111,138,151,154,170],"proposed.":[70],"First,":[71],"CNN":[73],"model":[74],"built":[76],"trained":[78],"(HSI)":[82],"datasets":[83],"accurately":[85],"obtain":[86],"resulting":[88],"abundance":[89,96],"maps.":[90],"Compared":[91],"with":[92,120],"raw":[94],"dataset,":[95],"maps":[97],"contain":[98],"more":[99],"distinctive":[100],"features":[101],"identify":[103],"anomalies":[104,173],"background.":[107],"Second,":[108],"dictionary":[110,150],"constructed":[112,149],"by":[113],"density-based":[115],"spatial":[116],"clustering":[117],"applications":[119],"noise":[121],"(DBSCAN)":[122],"stably":[125],"represent":[126],"component.":[129],"Third,":[130],"matrix":[132,145,163],"decomposition":[133],"method":[134,188],"LRR":[137],"adopted.":[139],"way,":[142],"coefficient":[144],"corresponding":[146],"obtained,":[152],"low":[155],"rank.":[156],"At":[157],"same":[159],"time,":[160],"residual":[162,179],"can":[164,174],"be":[165,175],"obtained":[166],"as":[167],"well,":[168],"sparse.":[171],"Finally,":[172],"extracted":[176],"matrix.":[180],"The":[181],"experimental":[182],"results":[183],"show":[184],"that":[185],"achieves":[189],"superior":[191],"performance":[192],"compared":[193],"some":[195],"state-of-the-art":[198],"field":[202]},"counts_by_year":[{"year":2026,"cited_by_count":6},{"year":2025,"cited_by_count":18},{"year":2024,"cited_by_count":16},{"year":2023,"cited_by_count":15},{"year":2022,"cited_by_count":13},{"year":2021,"cited_by_count":19},{"year":2020,"cited_by_count":10},{"year":2019,"cited_by_count":1}],"updated_date":"2026-03-27T14:29:43.386196","created_date":"2025-10-10T00:00:00"}
