{"id":"https://openalex.org/W2170974716","doi":"https://doi.org/10.1109/whispers.2009.5289019","title":"Improved hyperspectral anomaly detection in heavy-tailed backgrounds","display_name":"Improved hyperspectral anomaly detection in heavy-tailed backgrounds","publication_year":2009,"publication_date":"2009-08-01","ids":{"openalex":"https://openalex.org/W2170974716","doi":"https://doi.org/10.1109/whispers.2009.5289019","mag":"2170974716"},"language":"en","primary_location":{"id":"doi:10.1109/whispers.2009.5289019","is_oa":false,"landing_page_url":"https://doi.org/10.1109/whispers.2009.5289019","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing","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/A5110858032","display_name":"S. M. Adler\u2010Golden","orcid":null},"institutions":[{"id":"https://openalex.org/I178975111","display_name":"Spectral Sciences (United States)","ror":"https://ror.org/026jgjr74","country_code":"US","type":"company","lineage":["https://openalex.org/I178975111"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"S.M. Adler-Golden","raw_affiliation_strings":["Spectral Sciences, Inc., Burlington, MA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Spectral Sciences, Inc., Burlington, MA, USA","institution_ids":["https://openalex.org/I178975111"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5110858032"],"corresponding_institution_ids":["https://openalex.org/I178975111"],"apc_list":null,"apc_paid":null,"fwci":2.0555,"has_fulltext":false,"cited_by_count":14,"citation_normalized_percentile":{"value":0.88032358,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"13","issue":null,"first_page":"1","last_page":"4"},"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/T12157","display_name":"Geochemistry and Geologic Mapping","score":0.9975000023841858,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9911999702453613,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.7484923601150513},{"id":"https://openalex.org/keywords/principal-component-analysis","display_name":"Principal component analysis","score":0.7150117754936218},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.6603283286094666},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.6351070404052734},{"id":"https://openalex.org/keywords/covariance","display_name":"Covariance","score":0.6252027750015259},{"id":"https://openalex.org/keywords/probability-density-function","display_name":"Probability density function","score":0.597504198551178},{"id":"https://openalex.org/keywords/subspace-topology","display_name":"Subspace topology","score":0.5567036867141724},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.5500367283821106},{"id":"https://openalex.org/keywords/covariance-matrix","display_name":"Covariance matrix","score":0.5205639004707336},{"id":"https://openalex.org/keywords/anisotropy","display_name":"Anisotropy","score":0.4942200481891632},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.49099043011665344},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.487148255109787},{"id":"https://openalex.org/keywords/covariance-function","display_name":"Covariance function","score":0.42078715562820435},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.41816964745521545},{"id":"https://openalex.org/keywords/gaussian-noise","display_name":"Gaussian noise","score":0.41607239842414856},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4146074056625366},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.39612773060798645},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.3583572208881378},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.34459635615348816},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.2931564450263977},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.2506667673587799},{"id":"https://openalex.org/keywords/optics","display_name":"Optics","score":0.16702288389205933},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.08310672640800476}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.7484923601150513},{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.7150117754936218},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.6603283286094666},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.6351070404052734},{"id":"https://openalex.org/C178650346","wikidata":"https://www.wikidata.org/wiki/Q201984","display_name":"Covariance","level":2,"score":0.6252027750015259},{"id":"https://openalex.org/C197055811","wikidata":"https://www.wikidata.org/wiki/Q207522","display_name":"Probability density function","level":2,"score":0.597504198551178},{"id":"https://openalex.org/C32834561","wikidata":"https://www.wikidata.org/wiki/Q660730","display_name":"Subspace topology","level":2,"score":0.5567036867141724},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.5500367283821106},{"id":"https://openalex.org/C185142706","wikidata":"https://www.wikidata.org/wiki/Q1134404","display_name":"Covariance matrix","level":2,"score":0.5205639004707336},{"id":"https://openalex.org/C85725439","wikidata":"https://www.wikidata.org/wiki/Q466686","display_name":"Anisotropy","level":2,"score":0.4942200481891632},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.49099043011665344},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.487148255109787},{"id":"https://openalex.org/C137250428","wikidata":"https://www.wikidata.org/wiki/Q5178897","display_name":"Covariance function","level":3,"score":0.42078715562820435},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.41816964745521545},{"id":"https://openalex.org/C4199805","wikidata":"https://www.wikidata.org/wiki/Q2725903","display_name":"Gaussian noise","level":2,"score":0.41607239842414856},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4146074056625366},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.39612773060798645},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.3583572208881378},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.34459635615348816},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2931564450263977},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2506667673587799},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.16702288389205933},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.08310672640800476},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","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/whispers.2009.5289019","is_oa":false,"landing_page_url":"https://doi.org/10.1109/whispers.2009.5289019","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":8,"referenced_works":["https://openalex.org/W2014238333","https://openalex.org/W2047870694","https://openalex.org/W2142552707","https://openalex.org/W2170281198","https://openalex.org/W2338122324","https://openalex.org/W4285719527","https://openalex.org/W6654040368","https://openalex.org/W6703921851"],"related_works":["https://openalex.org/W1983957588","https://openalex.org/W2099754143","https://openalex.org/W2258123186","https://openalex.org/W1987404909","https://openalex.org/W2921280830","https://openalex.org/W2887132723","https://openalex.org/W4311761947","https://openalex.org/W2756533552","https://openalex.org/W2163704724","https://openalex.org/W4282813214"],"abstract_inverted_index":{"A":[0],"new":[1],"metric":[2,60],"for":[3,13,78],"anomaly":[4],"detection":[5,59],"in":[6,17,26,94],"hyperspectral":[7],"imagery":[8],"is":[9,50,61],"developed":[10],"to":[11,45],"account":[12],"anisotropic":[14,75],"heavy":[15],"tails":[16],"covariance-whitened":[18],"data.":[19],"The":[20,58],"anisotropy,":[21],"consisting":[22],"of":[23,38,55,69,87],"a":[24],"variation":[25],"tail":[27],"heaviness":[28],"with":[29,72],"principal":[30],"component":[31],"number,":[32],"commonly":[33],"occurs":[34],"when":[35],"the":[36,43,47,53,65,70,79,88],"number":[37,54],"linearly":[39],"independent":[40],"components":[41],"representing":[42,64],"data":[44,56,71],"within":[46],"noise":[48],"level":[49],"less":[51],"than":[52],"dimensions.":[57],"generated":[62],"by":[63],"probability":[66,80],"density":[67,81],"function":[68],"an":[73],"empirical":[74],"super-Gaussian":[76],"model":[77],"function.":[82],"Its":[83],"performance":[84],"exceeds":[85],"that":[86],"RX":[89,92],"and":[90,99],"Subspace":[91],"methods":[93],"examples":[95],"from":[96],"CAP":[97],"ARCHER":[98],"HyMap":[100],"imagery.":[101]},"counts_by_year":[{"year":2022,"cited_by_count":2},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":1},{"year":2015,"cited_by_count":2},{"year":2014,"cited_by_count":2},{"year":2013,"cited_by_count":1},{"year":2012,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}