{"id":"https://openalex.org/W2969209006","doi":"https://doi.org/10.1109/lgrs.2019.2929314","title":"A New Hyperspectral Anomaly Detection Method Based on Higher Order Statistics and Adaptive Cosine Estimator","display_name":"A New Hyperspectral Anomaly Detection Method Based on Higher Order Statistics and Adaptive Cosine Estimator","publication_year":2019,"publication_date":"2019-08-14","ids":{"openalex":"https://openalex.org/W2969209006","doi":"https://doi.org/10.1109/lgrs.2019.2929314","mag":"2969209006"},"language":"en","primary_location":{"id":"doi:10.1109/lgrs.2019.2929314","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2019.2929314","pdf_url":null,"source":{"id":"https://openalex.org/S126920919","display_name":"IEEE Geoscience and Remote Sensing Letters","issn_l":"1545-598X","issn":["1545-598X","1558-0571"],"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 Geoscience and Remote Sensing Letters","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/A5100393939","display_name":"Zhuang Li","orcid":"https://orcid.org/0000-0001-9740-1588"},"institutions":[{"id":"https://openalex.org/I204983213","display_name":"Harbin Institute of Technology","ror":"https://ror.org/01yqg2h08","country_code":"CN","type":"education","lineage":["https://openalex.org/I204983213"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhuang Li","raw_affiliation_strings":["Department of Information Engineering, School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin, China"],"raw_orcid":"https://orcid.org/0000-0001-9740-1588","affiliations":[{"raw_affiliation_string":"Department of Information Engineering, School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin, China","institution_ids":["https://openalex.org/I204983213"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100449291","display_name":"Ye Zhang","orcid":"https://orcid.org/0000-0001-8721-4535"},"institutions":[{"id":"https://openalex.org/I204983213","display_name":"Harbin Institute of Technology","ror":"https://ror.org/01yqg2h08","country_code":"CN","type":"education","lineage":["https://openalex.org/I204983213"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ye Zhang","raw_affiliation_strings":["Department of Information Engineering, School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin, China"],"raw_orcid":"https://orcid.org/0000-0001-8721-4535","affiliations":[{"raw_affiliation_string":"Department of Information Engineering, School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin, China","institution_ids":["https://openalex.org/I204983213"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I204983213"],"apc_list":null,"apc_paid":null,"fwci":1.7693,"has_fulltext":false,"cited_by_count":15,"citation_normalized_percentile":{"value":0.8697452,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":"17","issue":"4","first_page":"661","last_page":"665"},"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":0.991100013256073,"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/T10111","display_name":"Remote Sensing in Agriculture","score":0.9387999773025513,"subfield":{"id":"https://openalex.org/subfields/2303","display_name":"Ecology"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental 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":0.8537061214447021},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.8500840067863464},{"id":"https://openalex.org/keywords/constant-false-alarm-rate","display_name":"Constant false alarm rate","score":0.8012740612030029},{"id":"https://openalex.org/keywords/outlier","display_name":"Outlier","score":0.7173683643341064},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.6576068997383118},{"id":"https://openalex.org/keywords/false-alarm","display_name":"False alarm","score":0.6083388924598694},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.5587535500526428},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5436022877693176},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5331897735595703},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.5078051686286926},{"id":"https://openalex.org/keywords/order-statistic","display_name":"Order statistic","score":0.49132195115089417},{"id":"https://openalex.org/keywords/robust-statistics","display_name":"Robust statistics","score":0.4602985978126526},{"id":"https://openalex.org/keywords/trigonometric-functions","display_name":"Trigonometric functions","score":0.4396604299545288},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4250182807445526},{"id":"https://openalex.org/keywords/probability-distribution","display_name":"Probability distribution","score":0.4125123620033264},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.36237603425979614},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.3282402455806732},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.09882056713104248}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.8537061214447021},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.8500840067863464},{"id":"https://openalex.org/C77052588","wikidata":"https://www.wikidata.org/wiki/Q644307","display_name":"Constant false alarm rate","level":2,"score":0.8012740612030029},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.7173683643341064},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.6576068997383118},{"id":"https://openalex.org/C2776836416","wikidata":"https://www.wikidata.org/wiki/Q1364844","display_name":"False alarm","level":2,"score":0.6083388924598694},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.5587535500526428},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5436022877693176},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5331897735595703},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.5078051686286926},{"id":"https://openalex.org/C44082924","wikidata":"https://www.wikidata.org/wiki/Q1767128","display_name":"Order statistic","level":2,"score":0.49132195115089417},{"id":"https://openalex.org/C67226441","wikidata":"https://www.wikidata.org/wiki/Q1665389","display_name":"Robust statistics","level":3,"score":0.4602985978126526},{"id":"https://openalex.org/C178009071","wikidata":"https://www.wikidata.org/wiki/Q93344","display_name":"Trigonometric functions","level":2,"score":0.4396604299545288},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4250182807445526},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.4125123620033264},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.36237603425979614},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3282402455806732},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.09882056713104248},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"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/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/lgrs.2019.2929314","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2019.2929314","pdf_url":null,"source":{"id":"https://openalex.org/S126920919","display_name":"IEEE Geoscience and Remote Sensing Letters","issn_l":"1545-598X","issn":["1545-598X","1558-0571"],"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 Geoscience and Remote Sensing Letters","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1849479095","display_name":"\u5bbd\u6ce2\u6bb5\u591a\u7ef4\u5ea6\u9ad8\u5149\u8c31\u9065\u611f\u56fe\u50cf\u5206\u6790\u53ca\u89e3\u8bd1\u65b0\u65b9\u6cd5\u7814\u7a76","funder_award_id":"61871150","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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W2004491663","https://openalex.org/W2008775992","https://openalex.org/W2014238333","https://openalex.org/W2040078680","https://openalex.org/W2047870694","https://openalex.org/W2124267685","https://openalex.org/W2124463804","https://openalex.org/W2131697388","https://openalex.org/W2167320870","https://openalex.org/W2308804271","https://openalex.org/W2424277038","https://openalex.org/W2586973374","https://openalex.org/W2590856740","https://openalex.org/W2592141703","https://openalex.org/W2640276232","https://openalex.org/W2796629918","https://openalex.org/W2886820760","https://openalex.org/W2898121906","https://openalex.org/W2914004410"],"related_works":["https://openalex.org/W2998914036","https://openalex.org/W1983393909","https://openalex.org/W2040150569","https://openalex.org/W2132174924","https://openalex.org/W1911540634","https://openalex.org/W2013909972","https://openalex.org/W2059254588","https://openalex.org/W1989057380","https://openalex.org/W2067817134","https://openalex.org/W2062462149"],"abstract_inverted_index":{"Hyperspectral":[0],"anomaly":[1,59,74],"detection":[2,46,75,161,178],"is":[3,77,127],"a":[4,29,71],"hot":[5],"topic":[6],"in":[7,32,39],"remote":[8],"sensing":[9],"applications.":[10],"Most":[11],"of":[12,48,154,163],"the":[13,19,37,40,45,52,56,63,82,106,112,115,120,124,131,152,160,173],"conventional":[14],"detectors":[15],"are":[16,99],"based":[17],"on":[18,137],"Reed-Xiaoli":[20],"(RX)":[21],"method":[22,76,89,126,146,175],"and":[23,26,51,58,96,110],"assumedly":[24],"targets":[25,60],"backgrounds":[27,57,132],"follow":[28],"Gaussian":[30,41],"distribution":[31,42],"which":[33,79],"two":[34,83,91],"problems":[35,84],"exist:":[36],"outliers":[38],"statistics":[43,156],"limit":[44],"accuracy":[47],"RX":[49,125],"method,":[50],"larger":[53],"proportions":[54],"between":[55],"account":[61],"for":[62,123,133],"higher":[64,177],"false":[65,182],"alarm":[66,183],"rate.":[67],"In":[68],"this":[69],"letter,":[70],"new":[72,88],"hyperspectral":[73,139],"proposed,":[78],"can":[80],"solve":[81],"mentioned":[85],"above.":[86],"The":[87],"includes":[90],"improved":[92],"ideas.":[93],"First,":[94],"third-":[95],"fourth-order":[97],"moments":[98],"used":[100,128],"as":[101,119],"statistical":[102],"features":[103],"to":[104,129],"improve":[105,159],"outlier":[107,165],"peak":[108],"values":[109],"highlight":[111],"targets.":[113,135],"Second,":[114],"adaptive":[116],"cosine":[117],"estimation":[118],"structural":[121],"assumption":[122],"suppress":[130],"anomalous":[134],"Experiments":[136],"real":[138],"data":[140],"sets":[141],"suggest":[142],"that":[143,172],"our":[144],"proposed":[145,174],"could":[147],"not":[148],"only":[149],"effectively":[150],"decrease":[151],"impact":[153],"background":[155],"but":[157],"also":[158],"ability":[162],"such":[164],"values.":[166],"Furthermore,":[167],"comparative":[168],"experimental":[169],"results":[170],"revealed":[171],"achieves":[176],"rates":[179],"with":[180],"lower":[181],"rates.":[184]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
