{"id":"https://openalex.org/W4220910234","doi":"https://doi.org/10.1109/tgrs.2022.3161462","title":"Hyperspectral Anomaly Detection by Data Sphering and Sparsity Density Peaks","display_name":"Hyperspectral Anomaly Detection by Data Sphering and Sparsity Density Peaks","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W4220910234","doi":"https://doi.org/10.1109/tgrs.2022.3161462"},"language":"en","primary_location":{"id":"doi:10.1109/tgrs.2022.3161462","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2022.3161462","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/A5073412670","display_name":"Chein\u2010I Chang","orcid":"https://orcid.org/0000-0002-5450-4891"},"institutions":[{"id":"https://openalex.org/I126744593","display_name":"University of Maryland, Baltimore","ror":"https://ror.org/04rq5mt64","country_code":"US","type":"education","lineage":["https://openalex.org/I126744593"]},{"id":"https://openalex.org/I43313876","display_name":"Dalian Maritime University","ror":"https://ror.org/002b7nr53","country_code":"CN","type":"education","lineage":["https://openalex.org/I43313876"]}],"countries":["CN","US"],"is_corresponding":false,"raw_author_name":"Chein-I Chang","raw_affiliation_strings":["Center for Hyperspectral Imaging in Remote Sensing (CHIRS), Information and Technology College, Dalian Maritime University, Dalian, China","Department of Computer Science and Electrical Engineering, Remote Sensing Signal and Image Processing Laboratory, University of Maryland, Baltimore, MD, USA"],"raw_orcid":"https://orcid.org/0000-0002-5450-4891","affiliations":[{"raw_affiliation_string":"Center for Hyperspectral Imaging in Remote Sensing (CHIRS), Information and Technology College, Dalian Maritime University, Dalian, China","institution_ids":["https://openalex.org/I43313876"]},{"raw_affiliation_string":"Department of Computer Science and Electrical Engineering, Remote Sensing Signal and Image Processing Laboratory, University of Maryland, Baltimore, MD, USA","institution_ids":["https://openalex.org/I126744593"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5084958983","display_name":"Jie Chen","orcid":"https://orcid.org/0000-0002-7838-9293"},"institutions":[{"id":"https://openalex.org/I79272384","display_name":"University of Maryland, Baltimore County","ror":"https://ror.org/02qskvh78","country_code":"US","type":"education","lineage":["https://openalex.org/I79272384"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jie Chen","raw_affiliation_strings":["Department of Computer Science and Electrical Engineering, Remote Sensing Signal and Image Processing Laboratory, University of Maryland, Baltimore County, Baltimore, MD, USA"],"raw_orcid":"https://orcid.org/0000-0002-7838-9293","affiliations":[{"raw_affiliation_string":"Department of Computer Science and Electrical Engineering, Remote Sensing Signal and Image Processing Laboratory, University of Maryland, Baltimore County, Baltimore, MD, USA","institution_ids":["https://openalex.org/I79272384"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.1947,"has_fulltext":false,"cited_by_count":23,"citation_normalized_percentile":{"value":0.88556068,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":97,"max":99},"biblio":{"volume":"60","issue":null,"first_page":"1","last_page":"21"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9998999834060669,"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":0.9998999834060669,"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/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.9944999814033508,"subfield":{"id":"https://openalex.org/subfields/1602","display_name":"Analytical Chemistry"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13890","display_name":"Remote Sensing and Land Use","score":0.97079998254776,"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":0.7080982327461243},{"id":"https://openalex.org/keywords/sparse-approximation","display_name":"Sparse approximation","score":0.6372613906860352},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5860261917114258},{"id":"https://openalex.org/keywords/singular-value-decomposition","display_name":"Singular value decomposition","score":0.5349482297897339},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.5349416136741638},{"id":"https://openalex.org/keywords/dimensionality-reduction","display_name":"Dimensionality reduction","score":0.4923590421676636},{"id":"https://openalex.org/keywords/principal-component-analysis","display_name":"Principal component analysis","score":0.4900625944137573},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4834612309932709},{"id":"https://openalex.org/keywords/independent-component-analysis","display_name":"Independent component analysis","score":0.46307623386383057},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4527941942214966},{"id":"https://openalex.org/keywords/curse-of-dimensionality","display_name":"Curse of dimensionality","score":0.4413388669490814},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.4189344048500061},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.38387563824653625},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3779865503311157},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.08086124062538147}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.7080982327461243},{"id":"https://openalex.org/C124066611","wikidata":"https://www.wikidata.org/wiki/Q28684319","display_name":"Sparse approximation","level":2,"score":0.6372613906860352},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5860261917114258},{"id":"https://openalex.org/C22789450","wikidata":"https://www.wikidata.org/wiki/Q420904","display_name":"Singular value decomposition","level":2,"score":0.5349482297897339},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.5349416136741638},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.4923590421676636},{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.4900625944137573},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4834612309932709},{"id":"https://openalex.org/C51432778","wikidata":"https://www.wikidata.org/wiki/Q1259145","display_name":"Independent component analysis","level":2,"score":0.46307623386383057},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4527941942214966},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.4413388669490814},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.4189344048500061},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.38387563824653625},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3779865503311157},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.08086124062538147},{"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.2022.3161462","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2022.3161462","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":[{"id":"https://openalex.org/G1447177257","display_name":null,"funder_award_id":"3132019341","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"}],"funders":[{"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":59,"referenced_works":["https://openalex.org/W1970099214","https://openalex.org/W1972578813","https://openalex.org/W1983077328","https://openalex.org/W2004491663","https://openalex.org/W2011147915","https://openalex.org/W2019502123","https://openalex.org/W2033888020","https://openalex.org/W2040078680","https://openalex.org/W2045983409","https://openalex.org/W2047870694","https://openalex.org/W2067782748","https://openalex.org/W2095832449","https://openalex.org/W2121789508","https://openalex.org/W2124463804","https://openalex.org/W2142552707","https://openalex.org/W2145962650","https://openalex.org/W2163547919","https://openalex.org/W2165447611","https://openalex.org/W2165755981","https://openalex.org/W2167708128","https://openalex.org/W2219019129","https://openalex.org/W2288752886","https://openalex.org/W2295576075","https://openalex.org/W2493273699","https://openalex.org/W2549107715","https://openalex.org/W2590856740","https://openalex.org/W2789806937","https://openalex.org/W2791725003","https://openalex.org/W2795739134","https://openalex.org/W2885159437","https://openalex.org/W2901773810","https://openalex.org/W2911876518","https://openalex.org/W2949079224","https://openalex.org/W2951401720","https://openalex.org/W2969635036","https://openalex.org/W3003955104","https://openalex.org/W3005109735","https://openalex.org/W3008839601","https://openalex.org/W3042747521","https://openalex.org/W3080792885","https://openalex.org/W3087883793","https://openalex.org/W3087931076","https://openalex.org/W3090548235","https://openalex.org/W3107126660","https://openalex.org/W3112037842","https://openalex.org/W3129037655","https://openalex.org/W3133603318","https://openalex.org/W3137199127","https://openalex.org/W3152468234","https://openalex.org/W3156696558","https://openalex.org/W3177111766","https://openalex.org/W3195212285","https://openalex.org/W3207817117","https://openalex.org/W3210780861","https://openalex.org/W4211083646","https://openalex.org/W4220831207","https://openalex.org/W4248253651","https://openalex.org/W4302444439","https://openalex.org/W6681016373"],"related_works":["https://openalex.org/W4252230435","https://openalex.org/W2121429698","https://openalex.org/W2182042810","https://openalex.org/W55679925","https://openalex.org/W3080404860","https://openalex.org/W2046761971","https://openalex.org/W1720164552","https://openalex.org/W3144722888","https://openalex.org/W2364896863","https://openalex.org/W2532569109"],"abstract_inverted_index":{"Many":[0],"approaches":[1],"have":[2],"been":[3],"developed":[4],"for":[5],"hyperspectral":[6],"anomaly":[7,157],"detection":[8],"(AD).":[9],"Of":[10],"particular":[11],"interest":[12],"are":[13,175],"low":[14,28],"rank":[15,29],"and":[16,30,45,60,171,177,193],"sparse":[17,31,91,108,135],"representation":[18],"(LRaSR)":[19],"model-based":[20,216],"methods":[21,217],"which":[22,40,143],"decompose":[23],"a":[24,36,68,90,118,138,144,194],"data":[25,52,70,131,147],"space":[26,109,136],"into":[27],"spaces.":[32],"This":[33],"article":[34],"develops":[35],"rather":[37],"different":[38],"approach,":[39],"assumes":[41],"that":[42,204],"background":[43],"(BKG)":[44],"anomalies":[46,104],"can":[47,73,83,151],"be,":[48],"respectively,":[49],"characterized":[50],"by":[51,100,189],"statistics":[53],"of":[54,121,146,161,182],"the":[55,77,106,130,134,180],"first":[56],"two":[57],"orders":[58,62],"(2OS)":[59],"high":[61],"(HOS)":[63],"greater":[64],"than":[65,211],"2.":[66],"As":[67],"result,":[69],"sphering":[71],"(DS)":[72],"remove":[74],"BKG,":[75],"while":[76],"fast":[78],"independent":[79,85],"component":[80],"analysis":[81],"(FastICA)":[82],"generate":[84],"components":[86],"(ICs)":[87],"to":[88,128,154,184],"form":[89,155],"space.":[92,158],"However,":[93],"since":[94],"non-Gaussian":[95],"noises":[96],"cannot":[97],"be":[98,112,152,185],"separated":[99],"FastICA,":[101],"directly":[102],"extracting":[103],"from":[105,142],"ICs-formed":[107],"may":[110],"not":[111],"effective.":[113],"To":[114],"address":[115],"this":[116],"issue,":[117],"new":[119],"concept":[120],"sparsity":[122],"density":[123,140],"peak":[124,173],"(SDP)":[125],"is":[126,187],"proposed":[127],"represent":[129],"samples":[132,148],"in":[133,218],"as":[137],"probability":[139],"function":[141],"set":[145],"with":[149,207],"peaks":[150,183],"extracted":[153,186],"an":[156],"Three":[159],"versions":[160],"SDP,":[162],"fixed":[163,167],"spectral":[164],"SDP":[165,169,208],"(FS-SDP),":[166],"band":[168],"(FB-SDP),":[170],"spectral\u2013spatial\u2013sparsity":[172],"(SS-SDP)":[174],"derived":[176],"used":[178,214],"where":[179],"number":[181],"determined":[188],"virtual":[190],"dimensionality":[191],"(VD)":[192],"minimax-singular":[195],"value":[196],"decomposition":[197],"(MX-SVD)":[198],"algorithm.":[199],"The":[200],"experimental":[201],"results":[202],"demonstrate":[203],"DS":[205],"coupled":[206],"performs":[209],"better":[210],"currently":[212],"being":[213],"LRaSR":[215],"AD.":[219]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":5}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
