{"id":"https://openalex.org/W2004553866","doi":"https://doi.org/10.1109/whispers.2009.5289028","title":"Kernel subspace-based anomaly detection for hyperspectral imagery","display_name":"Kernel subspace-based anomaly detection for hyperspectral imagery","publication_year":2009,"publication_date":"2009-08-01","ids":{"openalex":"https://openalex.org/W2004553866","doi":"https://doi.org/10.1109/whispers.2009.5289028","mag":"2004553866"},"language":"en","primary_location":{"id":"doi:10.1109/whispers.2009.5289028","is_oa":false,"landing_page_url":"https://doi.org/10.1109/whispers.2009.5289028","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":true,"oa_status":"green","oa_url":"https://zenodo.org/record/1282945","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5017489351","display_name":"N.M. Nasrabadi","orcid":null},"institutions":[{"id":"https://openalex.org/I166416128","display_name":"DEVCOM Army Research Laboratory","ror":"https://ror.org/011hc8f90","country_code":"US","type":"government","lineage":["https://openalex.org/I1304082316","https://openalex.org/I1330347796","https://openalex.org/I166416128","https://openalex.org/I2802705668","https://openalex.org/I4210154437"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"N.M. Nasrabadi","raw_affiliation_strings":["US Army Research Laboratory, Adelphi, MD, USA","US Army Research Laboratory, 2800 Powder Mill Rd., Adelphi, MD 20783, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"US Army Research Laboratory, Adelphi, MD, USA","institution_ids":["https://openalex.org/I166416128"]},{"raw_affiliation_string":"US Army Research Laboratory, 2800 Powder Mill Rd., Adelphi, MD 20783, USA","institution_ids":["https://openalex.org/I166416128"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5017489351"],"corresponding_institution_ids":["https://openalex.org/I166416128"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.10217236,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"biblio":{"volume":null,"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":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":0.986299991607666,"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/T11667","display_name":"Advanced Chemical Sensor Technologies","score":0.9307000041007996,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.8464809656143188},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.739972710609436},{"id":"https://openalex.org/keywords/linear-subspace","display_name":"Linear subspace","score":0.6891871690750122},{"id":"https://openalex.org/keywords/subspace-topology","display_name":"Subspace topology","score":0.6635441780090332},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.6611069440841675},{"id":"https://openalex.org/keywords/principal-component-analysis","display_name":"Principal component analysis","score":0.6410136222839355},{"id":"https://openalex.org/keywords/kernel-principal-component-analysis","display_name":"Kernel principal component analysis","score":0.6383116245269775},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6211693286895752},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6092596650123596},{"id":"https://openalex.org/keywords/linear-discriminant-analysis","display_name":"Linear discriminant analysis","score":0.6087875962257385},{"id":"https://openalex.org/keywords/projection","display_name":"Projection (relational algebra)","score":0.5720339417457581},{"id":"https://openalex.org/keywords/kernel-fisher-discriminant-analysis","display_name":"Kernel Fisher discriminant analysis","score":0.48772773146629333},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.45469561219215393},{"id":"https://openalex.org/keywords/principal-component-regression","display_name":"Principal component regression","score":0.4544856548309326},{"id":"https://openalex.org/keywords/kernel-method","display_name":"Kernel method","score":0.44848453998565674},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.42336606979370117},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.40811917185783386},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.2984147071838379},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.23047631978988647},{"id":"https://openalex.org/keywords/combinatorics","display_name":"Combinatorics","score":0.07135909795761108}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.8464809656143188},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.739972710609436},{"id":"https://openalex.org/C12362212","wikidata":"https://www.wikidata.org/wiki/Q728435","display_name":"Linear subspace","level":2,"score":0.6891871690750122},{"id":"https://openalex.org/C32834561","wikidata":"https://www.wikidata.org/wiki/Q660730","display_name":"Subspace topology","level":2,"score":0.6635441780090332},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.6611069440841675},{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.6410136222839355},{"id":"https://openalex.org/C182335926","wikidata":"https://www.wikidata.org/wiki/Q17093020","display_name":"Kernel principal component analysis","level":4,"score":0.6383116245269775},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6211693286895752},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6092596650123596},{"id":"https://openalex.org/C69738355","wikidata":"https://www.wikidata.org/wiki/Q1228929","display_name":"Linear discriminant analysis","level":2,"score":0.6087875962257385},{"id":"https://openalex.org/C57493831","wikidata":"https://www.wikidata.org/wiki/Q3134666","display_name":"Projection (relational algebra)","level":2,"score":0.5720339417457581},{"id":"https://openalex.org/C181367576","wikidata":"https://www.wikidata.org/wiki/Q6394184","display_name":"Kernel Fisher discriminant analysis","level":4,"score":0.48772773146629333},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.45469561219215393},{"id":"https://openalex.org/C74887250","wikidata":"https://www.wikidata.org/wiki/Q3455892","display_name":"Principal component regression","level":3,"score":0.4544856548309326},{"id":"https://openalex.org/C122280245","wikidata":"https://www.wikidata.org/wiki/Q620622","display_name":"Kernel method","level":3,"score":0.44848453998565674},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.42336606979370117},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.40811917185783386},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2984147071838379},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.23047631978988647},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.07135909795761108},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/whispers.2009.5289028","is_oa":false,"landing_page_url":"https://doi.org/10.1109/whispers.2009.5289028","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"},{"id":"pmh:oai:zenodo.org:1282945","is_oa":true,"landing_page_url":"https://zenodo.org/record/1282945","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":"public-domain","license_id":"https://openalex.org/licenses/public-domain","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"info:eu-repo/semantics/conferencePaper"}],"best_oa_location":{"id":"pmh:oai:zenodo.org:1282945","is_oa":true,"landing_page_url":"https://zenodo.org/record/1282945","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":"public-domain","license_id":"https://openalex.org/licenses/public-domain","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"info:eu-repo/semantics/conferencePaper"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.75}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":5,"referenced_works":["https://openalex.org/W1970099214","https://openalex.org/W1972023071","https://openalex.org/W2023155403","https://openalex.org/W2047870694","https://openalex.org/W2124463804"],"related_works":["https://openalex.org/W2905418897","https://openalex.org/W1607829095","https://openalex.org/W4318562271","https://openalex.org/W3033319502","https://openalex.org/W1982817239","https://openalex.org/W3093470103","https://openalex.org/W2046363782","https://openalex.org/W1967057085","https://openalex.org/W2890196891","https://openalex.org/W2375053148"],"abstract_inverted_index":{"This":[0],"paper":[1],"provides":[2],"a":[3],"performance":[4],"comparison":[5],"of":[6,41],"various":[7],"linear":[8,22,37,44,71],"and":[9,26,61,72],"nonlinear":[10,52],"subspace-based":[11],"anomaly":[12,59,74],"detectors.":[13],"Three":[14],"different":[15],"techniques,":[16],"principal":[17],"component":[18],"analysis":[19],"(PCA),":[20],"fisher":[21],"discriminant":[23],"(FLD)":[24],"analysis,":[25],"the":[27,36],"eigenspace":[28],"separation":[29],"transform":[30],"(EST),":[31],"are":[32,65,76],"used":[33],"to":[34,49],"generate":[35],"projection":[38],"subspaces.":[39],"Each":[40],"these":[42],"three":[43],"methods":[45],"is":[46],"then":[47],"extended":[48],"its":[50],"corresponding":[51],"kernel":[53],"version.":[54],"The":[55],"well-known":[56],"Reed-Xiaoli":[57],"(RX)":[58],"detector":[60],"itskernelversion":[62],"(kernel":[63],"RX)":[64],"also":[66],"implemented.":[67],"Comparisons":[68],"between":[69],"all":[70],"non-linear":[73],"detectors":[75],"made":[77],"using":[78],"receiver":[79],"operating":[80],"characteristics":[81],"(ROC)":[82],"curves":[83],"for":[84],"several":[85],"hyperspectral":[86],"imagery.":[87]},"counts_by_year":[{"year":2016,"cited_by_count":2}],"updated_date":"2026-08-26T07:47:46.906454","created_date":"2025-10-10T00:00:00"}
