{"id":"https://openalex.org/W2765847871","doi":"https://doi.org/10.1109/whispers.2013.8080615","title":"Target detection in inhomogenous non-Gaussian hyperspectral data, based on non-parametric density estimation","display_name":"Target detection in inhomogenous non-Gaussian hyperspectral data, based on non-parametric density estimation","publication_year":2013,"publication_date":"2013-06-01","ids":{"openalex":"https://openalex.org/W2765847871","doi":"https://doi.org/10.1109/whispers.2013.8080615","mag":"2765847871"},"language":"en","primary_location":{"id":"doi:10.1109/whispers.2013.8080615","is_oa":false,"landing_page_url":"https://doi.org/10.1109/whispers.2013.8080615","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2013 5th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)","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/A5074162120","display_name":"Gil Tidhar","orcid":"https://orcid.org/0000-0002-7094-4870"},"institutions":[{"id":"https://openalex.org/I124227911","display_name":"Ben-Gurion University of the Negev","ror":"https://ror.org/05tkyf982","country_code":"IL","type":"education","lineage":["https://openalex.org/I124227911"]},{"id":"https://openalex.org/I4210089528","display_name":"The Technological College of Beer Sheva","ror":"https://ror.org/00bzfhs56","country_code":"IL","type":"education","lineage":["https://openalex.org/I4210089528"]}],"countries":["IL"],"is_corresponding":false,"raw_author_name":"G. A. Tidhar","raw_affiliation_strings":["Department of Electrical and Computer Engineering, P.O. Box 653, Beer-Sheva 84105, Israel","Department of Electrical and Computer Engineering, Ben-Gurion Univ. of the Negev (Israel), Beer-Sheva, Israel"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, P.O. Box 653, Beer-Sheva 84105, Israel","institution_ids":["https://openalex.org/I4210089528"]},{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Ben-Gurion Univ. of the Negev (Israel), Beer-Sheva, Israel","institution_ids":["https://openalex.org/I124227911"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5065825035","display_name":"Stanley R. Rotman","orcid":"https://orcid.org/0000-0002-9264-174X"},"institutions":[{"id":"https://openalex.org/I124227911","display_name":"Ben-Gurion University of the Negev","ror":"https://ror.org/05tkyf982","country_code":"IL","type":"education","lineage":["https://openalex.org/I124227911"]},{"id":"https://openalex.org/I4210089528","display_name":"The Technological College of Beer Sheva","ror":"https://ror.org/00bzfhs56","country_code":"IL","type":"education","lineage":["https://openalex.org/I4210089528"]}],"countries":["IL"],"is_corresponding":false,"raw_author_name":"S. R. Rotman","raw_affiliation_strings":["Department of Electrical and Computer Engineering, P.O. Box 653, Beer-Sheva 84105, Israel","Department of Electrical and Computer Engineering, Ben-Gurion Univ. of the Negev (Israel), Beer-Sheva, Israel"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, P.O. Box 653, Beer-Sheva 84105, Israel","institution_ids":["https://openalex.org/I4210089528"]},{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Ben-Gurion Univ. of the Negev (Israel), Beer-Sheva, Israel","institution_ids":["https://openalex.org/I124227911"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.38166511,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"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":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":0.9896000027656555,"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/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.9420999884605408,"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/linear-subspace","display_name":"Linear subspace","score":0.7113431692123413},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.68739253282547},{"id":"https://openalex.org/keywords/subspace-topology","display_name":"Subspace topology","score":0.681380033493042},{"id":"https://openalex.org/keywords/parametric-statistics","display_name":"Parametric statistics","score":0.5558772087097168},{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.5514017343521118},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.5476526021957397},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.531437337398529},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5093764066696167},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4991576671600342},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.48663029074668884},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.48476260900497437},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.48268604278564453},{"id":"https://openalex.org/keywords/density-estimation","display_name":"Density estimation","score":0.4566274583339691},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.22425314784049988}],"concepts":[{"id":"https://openalex.org/C12362212","wikidata":"https://www.wikidata.org/wiki/Q728435","display_name":"Linear subspace","level":2,"score":0.7113431692123413},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.68739253282547},{"id":"https://openalex.org/C32834561","wikidata":"https://www.wikidata.org/wiki/Q660730","display_name":"Subspace topology","level":2,"score":0.681380033493042},{"id":"https://openalex.org/C117251300","wikidata":"https://www.wikidata.org/wiki/Q1849855","display_name":"Parametric statistics","level":2,"score":0.5558772087097168},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.5514017343521118},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.5476526021957397},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.531437337398529},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5093764066696167},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4991576671600342},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.48663029074668884},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.48476260900497437},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.48268604278564453},{"id":"https://openalex.org/C189508267","wikidata":"https://www.wikidata.org/wiki/Q17088227","display_name":"Density estimation","level":3,"score":0.4566274583339691},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.22425314784049988},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/whispers.2013.8080615","is_oa":false,"landing_page_url":"https://doi.org/10.1109/whispers.2013.8080615","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2013 5th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W1746680969","https://openalex.org/W2040373367","https://openalex.org/W2047519171","https://openalex.org/W2070424424","https://openalex.org/W2078438223","https://openalex.org/W2091397530","https://openalex.org/W2110211064","https://openalex.org/W2117366020","https://openalex.org/W2124119235","https://openalex.org/W2124267685","https://openalex.org/W2128800160","https://openalex.org/W2163166770","https://openalex.org/W2188607913","https://openalex.org/W2803316390","https://openalex.org/W4285719527","https://openalex.org/W6677469383"],"related_works":["https://openalex.org/W3100286349","https://openalex.org/W2896134808","https://openalex.org/W4289378085","https://openalex.org/W4294291164","https://openalex.org/W3172436493","https://openalex.org/W1887135636","https://openalex.org/W4287164812","https://openalex.org/W2386063599","https://openalex.org/W1975884855","https://openalex.org/W3213150849"],"abstract_inverted_index":{"Performance":[0],"of":[1,67,97],"algorithms":[2,82,117],"for":[3],"target":[4,107],"signal":[5],"detection":[6,45,81],"in":[7,124],"Hyperspectral":[8],"Imagery":[9],"(HSI)":[10],"is":[11,17,112,119],"often":[12],"deteriorated":[13],"when":[14,24],"the":[15,65,68,85,94,98],"data":[16,52],"neither":[18],"statistically":[19],"homogeneous":[20],"nor":[21],"Gaussian":[22,56],"or":[23],"its":[25],"Joint":[26],"Probability":[27],"Density":[28],"(JPD)":[29],"does":[30],"not":[31],"match":[32],"any":[33],"presumed":[34],"particular":[35],"parametric":[36],"model.":[37],"In":[38],"this":[39],"paper":[40],"we":[41],"propose":[42],"a":[43,72],"novel":[44],"algorithm":[46,110],"which":[47],"first":[48],"attempts":[49],"at":[50],"dividing":[51],"domain":[53],"into":[54],"mostly":[55,58],"and":[57,62,88,104,118],"Non-Gaussian":[59],"(NG)":[60],"subspaces,":[61],"then":[63,77],"estimates":[64],"JPD":[66,96],"NG":[69,99],"subspace":[70],"with":[71,93],"non-parametric":[73],"Graph-based":[74],"estimator.":[75],"It":[76],"combines":[78],"commonly":[79,115],"used":[80,116],"operating":[83],"on":[84],"mostly-Gaussian":[86],"sub-space":[87],"an":[89],"LRT":[90],"calculated":[91],"directly":[92],"estimated":[95],"subspace,":[100],"to":[101,114,121],"detect":[102],"anomalies":[103],"known":[105],"additive-type":[106],"signals.":[108],"The":[109],"performance":[111],"compared":[113],"found":[120],"be":[122],"superior":[123],"some":[125],"important":[126],"cases.":[127]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}