{"id":"https://openalex.org/W2033888020","doi":"https://doi.org/10.1109/lgrs.2013.2257670","title":"A Locally Adaptive Background Density Estimator: An Evolution for RX-Based Anomaly Detectors","display_name":"A Locally Adaptive Background Density Estimator: An Evolution for RX-Based Anomaly Detectors","publication_year":2013,"publication_date":"2013-06-11","ids":{"openalex":"https://openalex.org/W2033888020","doi":"https://doi.org/10.1109/lgrs.2013.2257670","mag":"2033888020"},"language":"en","primary_location":{"id":"doi:10.1109/lgrs.2013.2257670","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2013.2257670","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/A5073606309","display_name":"Stefania Matteoli","orcid":"https://orcid.org/0000-0001-6940-9881"},"institutions":[{"id":"https://openalex.org/I108290504","display_name":"University of Pisa","ror":"https://ror.org/03ad39j10","country_code":"IT","type":"education","lineage":["https://openalex.org/I108290504"]}],"countries":["IT"],"is_corresponding":true,"raw_author_name":"Stefania Matteoli","raw_affiliation_strings":["Department of Information Engineering, University of Pisa, Pisa, Italy"],"affiliations":[{"raw_affiliation_string":"Department of Information Engineering, University of Pisa, Pisa, Italy","institution_ids":["https://openalex.org/I108290504"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004320088","display_name":"Tiziana Veracini","orcid":null},"institutions":[{"id":"https://openalex.org/I108290504","display_name":"University of Pisa","ror":"https://ror.org/03ad39j10","country_code":"IT","type":"education","lineage":["https://openalex.org/I108290504"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Tiziana Veracini","raw_affiliation_strings":["Department of Information Engineering, University of Pisa, Pisa, Italy"],"affiliations":[{"raw_affiliation_string":"Department of Information Engineering, University of Pisa, Pisa, Italy","institution_ids":["https://openalex.org/I108290504"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064336799","display_name":"Marco Diani","orcid":"https://orcid.org/0000-0003-1520-1991"},"institutions":[{"id":"https://openalex.org/I108290504","display_name":"University of Pisa","ror":"https://ror.org/03ad39j10","country_code":"IT","type":"education","lineage":["https://openalex.org/I108290504"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Marco Diani","raw_affiliation_strings":["Department of Information Engineering, University of Pisa, Pisa, Italy"],"affiliations":[{"raw_affiliation_string":"Department of Information Engineering, University of Pisa, Pisa, Italy","institution_ids":["https://openalex.org/I108290504"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5089641976","display_name":"Giovanni Corsini","orcid":"https://orcid.org/0000-0002-9366-2470"},"institutions":[{"id":"https://openalex.org/I108290504","display_name":"University of Pisa","ror":"https://ror.org/03ad39j10","country_code":"IT","type":"education","lineage":["https://openalex.org/I108290504"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Giovanni Corsini","raw_affiliation_strings":["Department of Information Engineering, University of Pisa, Pisa, Italy"],"affiliations":[{"raw_affiliation_string":"Department of Information Engineering, University of Pisa, Pisa, Italy","institution_ids":["https://openalex.org/I108290504"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5073606309"],"corresponding_institution_ids":["https://openalex.org/I108290504"],"apc_list":null,"apc_paid":null,"fwci":2.9408,"has_fulltext":false,"cited_by_count":75,"citation_normalized_percentile":{"value":0.92006059,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"11","issue":"1","first_page":"323","last_page":"327"},"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":0.987500011920929,"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/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.9781000018119812,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.7177039384841919},{"id":"https://openalex.org/keywords/kernel-density-estimation","display_name":"Kernel density estimation","score":0.6993524432182312},{"id":"https://openalex.org/keywords/multispectral-image","display_name":"Multispectral image","score":0.6799368858337402},{"id":"https://openalex.org/keywords/probability-density-function","display_name":"Probability density function","score":0.6653258204460144},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.6479517817497253},{"id":"https://openalex.org/keywords/bandwidth","display_name":"Bandwidth (computing)","score":0.6137968897819519},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5914503931999207},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.5782811641693115},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.565290093421936},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5318489074707031},{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.530448853969574},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4801604151725769},{"id":"https://openalex.org/keywords/density-estimation","display_name":"Density estimation","score":0.46850326657295227},{"id":"https://openalex.org/keywords/variable-kernel-density-estimation","display_name":"Variable kernel density estimation","score":0.4597502052783966},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.4246056079864502},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3760339915752411},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.32906970381736755},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3078702986240387},{"id":"https://openalex.org/keywords/kernel-method","display_name":"Kernel method","score":0.2267550528049469},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.22228240966796875},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.16694405674934387},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.14820313453674316},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.07498878240585327}],"concepts":[{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.7177039384841919},{"id":"https://openalex.org/C71134354","wikidata":"https://www.wikidata.org/wiki/Q458825","display_name":"Kernel density estimation","level":3,"score":0.6993524432182312},{"id":"https://openalex.org/C173163844","wikidata":"https://www.wikidata.org/wiki/Q1761440","display_name":"Multispectral image","level":2,"score":0.6799368858337402},{"id":"https://openalex.org/C197055811","wikidata":"https://www.wikidata.org/wiki/Q207522","display_name":"Probability density function","level":2,"score":0.6653258204460144},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.6479517817497253},{"id":"https://openalex.org/C2776257435","wikidata":"https://www.wikidata.org/wiki/Q1576430","display_name":"Bandwidth (computing)","level":2,"score":0.6137968897819519},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5914503931999207},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.5782811641693115},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.565290093421936},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5318489074707031},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.530448853969574},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4801604151725769},{"id":"https://openalex.org/C189508267","wikidata":"https://www.wikidata.org/wiki/Q17088227","display_name":"Density estimation","level":3,"score":0.46850326657295227},{"id":"https://openalex.org/C195699287","wikidata":"https://www.wikidata.org/wiki/Q7915722","display_name":"Variable kernel density estimation","level":4,"score":0.4597502052783966},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.4246056079864502},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3760339915752411},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.32906970381736755},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3078702986240387},{"id":"https://openalex.org/C122280245","wikidata":"https://www.wikidata.org/wiki/Q620622","display_name":"Kernel method","level":3,"score":0.2267550528049469},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.22228240966796875},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.16694405674934387},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.14820313453674316},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.07498878240585327},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","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}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/lgrs.2013.2257670","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2013.2257670","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"},{"id":"pmh:oai:arpi.unipi.it:11568/399679","is_oa":false,"landing_page_url":"https://ieeexplore.ieee.org/document/6529136","pdf_url":null,"source":{"id":"https://openalex.org/S4377196265","display_name":"CINECA IRIS Institutial research information system (University of Pisa)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I108290504","host_organization_name":"University of Pisa","host_organization_lineage":["https://openalex.org/I108290504"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"info:eu-repo/semantics/article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W638544165","https://openalex.org/W1970099214","https://openalex.org/W1979546785","https://openalex.org/W1998035413","https://openalex.org/W2005296184","https://openalex.org/W2020107835","https://openalex.org/W2021396690","https://openalex.org/W2047870694","https://openalex.org/W2048021008","https://openalex.org/W2097324202","https://openalex.org/W2124267685","https://openalex.org/W2124463804","https://openalex.org/W2129498797","https://openalex.org/W2140475713","https://openalex.org/W2141620354","https://openalex.org/W2166691113","https://openalex.org/W4248721357","https://openalex.org/W6645104332"],"related_works":["https://openalex.org/W3212687977","https://openalex.org/W3123419490","https://openalex.org/W4241010850","https://openalex.org/W2144201579","https://openalex.org/W2355371556","https://openalex.org/W1528102763","https://openalex.org/W2585385340","https://openalex.org/W1569550976","https://openalex.org/W4297926828","https://openalex.org/W2887481058"],"abstract_inverted_index":{"We":[0],"propose":[1],"a":[2,20],"local":[3],"anomaly":[4],"detection":[5],"strategy":[6,52],"for":[7,40],"multi-hyperspectral":[8],"images":[9,42],"in":[10],"which":[11],"the":[12,30,35,47,50,63,69],"background":[13],"probability":[14],"density":[15,22],"function":[16],"is":[17,32],"estimated":[18],"with":[19],"kernel":[21],"estimator":[23],"and":[24,60],"locally":[25],"adaptive":[26],"information":[27],"extracted":[28],"from":[29],"image":[31],"injected":[33],"into":[34],"bandwidth":[36],"selection":[37],"process.":[38],"Results":[39],"multispectral":[41],"of":[43,49,66],"different":[44],"scenarios":[45],"show":[46],"benefits":[48],"proposed":[51],"regarding":[53],"its":[54],"effectiveness":[55],"both":[56],"at":[57,61],"detecting":[58],"anomalies":[59],"avoiding":[62],"crucial":[64],"issue":[65],"properly":[67],"selecting":[68],"kernel-width":[70],"parameter.":[71]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":11},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":13},{"year":2022,"cited_by_count":8},{"year":2021,"cited_by_count":11},{"year":2020,"cited_by_count":5},{"year":2019,"cited_by_count":7},{"year":2018,"cited_by_count":6},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":1},{"year":2015,"cited_by_count":2},{"year":2014,"cited_by_count":3}],"updated_date":"2026-03-14T08:43:22.919905","created_date":"2025-10-10T00:00:00"}
