{"id":"https://openalex.org/W3130436279","doi":"https://doi.org/10.1109/igarss39084.2020.9323918","title":"Improving Physical and Statistical Models for Detecting Difficult Targets with LRT Detectors in Closed-Form","display_name":"Improving Physical and Statistical Models for Detecting Difficult Targets with LRT Detectors in Closed-Form","publication_year":2020,"publication_date":"2020-09-26","ids":{"openalex":"https://openalex.org/W3130436279","doi":"https://doi.org/10.1109/igarss39084.2020.9323918","mag":"3130436279"},"language":"en","primary_location":{"id":"doi:10.1109/igarss39084.2020.9323918","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss39084.2020.9323918","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium","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/A5073606309","display_name":"Stefania Matteoli","orcid":"https://orcid.org/0000-0001-6940-9881"},"institutions":[{"id":"https://openalex.org/I4210100607","display_name":"Institute of Electronics, Computer and Telecommunication Engineering","ror":"https://ror.org/00n4jbh84","country_code":"IT","type":"facility","lineage":["https://openalex.org/I4210100607","https://openalex.org/I4210155236"]},{"id":"https://openalex.org/I4210155236","display_name":"National Research Council","ror":"https://ror.org/04zaypm56","country_code":"IT","type":"nonprofit","lineage":["https://openalex.org/I4210155236"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Stefania Matteoli","raw_affiliation_strings":["National Research Council (CNR), Institute of Electronics, Computer and Telecommunication Engineering (IEIIT), Pisa, Italy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Research Council (CNR), Institute of Electronics, Computer and Telecommunication Engineering (IEIIT), Pisa, Italy","institution_ids":["https://openalex.org/I4210100607","https://openalex.org/I4210155236"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064336799","display_name":"Marco Diani","orcid":"https://orcid.org/0000-0003-1520-1991"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Marco Diani","raw_affiliation_strings":["Italian Naval Academy, Livorno, Italy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Italian Naval Academy, Livorno, Italy","institution_ids":[]}]},{"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":["University of Pisa, Pisa, Italy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Pisa, Pisa, Italy","institution_ids":["https://openalex.org/I108290504"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"9088","issue":null,"first_page":"3959","last_page":"3962"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9998000264167786,"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":0.9998000264167786,"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/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.9793999791145325,"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"}},{"id":"https://openalex.org/T11871","display_name":"Advanced Statistical Methods and Models","score":0.977400004863739,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.8432141542434692},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.7583643198013306},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6966220140457153},{"id":"https://openalex.org/keywords/likelihood-ratio-test","display_name":"Likelihood-ratio test","score":0.6466271281242371},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5836622714996338},{"id":"https://openalex.org/keywords/statistical-hypothesis-testing","display_name":"Statistical hypothesis testing","score":0.4740683436393738},{"id":"https://openalex.org/keywords/detection-theory","display_name":"Detection theory","score":0.45530882477760315},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4492083489894867},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.44850224256515503},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4454251825809479},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.44163939356803894},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.38425779342651367},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.21103674173355103},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.19510355591773987}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.8432141542434692},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.7583643198013306},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6966220140457153},{"id":"https://openalex.org/C9483764","wikidata":"https://www.wikidata.org/wiki/Q585740","display_name":"Likelihood-ratio test","level":2,"score":0.6466271281242371},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5836622714996338},{"id":"https://openalex.org/C87007009","wikidata":"https://www.wikidata.org/wiki/Q210832","display_name":"Statistical hypothesis testing","level":2,"score":0.4740683436393738},{"id":"https://openalex.org/C137270730","wikidata":"https://www.wikidata.org/wiki/Q120811","display_name":"Detection theory","level":3,"score":0.45530882477760315},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4492083489894867},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.44850224256515503},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4454251825809479},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.44163939356803894},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.38425779342651367},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.21103674173355103},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.19510355591773987},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss39084.2020.9323918","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss39084.2020.9323918","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7599999904632568,"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W1971358070","https://openalex.org/W1986921156","https://openalex.org/W2008007550","https://openalex.org/W2013029777","https://openalex.org/W2013211397","https://openalex.org/W2026918110","https://openalex.org/W2046049381","https://openalex.org/W2050947749","https://openalex.org/W2052868189","https://openalex.org/W2096972831","https://openalex.org/W2115647817","https://openalex.org/W2118996198","https://openalex.org/W2129498797","https://openalex.org/W2129905273","https://openalex.org/W2144158572","https://openalex.org/W2800463836","https://openalex.org/W2951039757","https://openalex.org/W2964064133","https://openalex.org/W2968738690","https://openalex.org/W2990709406","https://openalex.org/W4233014035","https://openalex.org/W6663612057","https://openalex.org/W6677508755","https://openalex.org/W6766854654"],"related_works":["https://openalex.org/W1856957993","https://openalex.org/W1966535600","https://openalex.org/W2012141031","https://openalex.org/W2015465174","https://openalex.org/W2408444874","https://openalex.org/W2155293550","https://openalex.org/W1966599233","https://openalex.org/W2123226256","https://openalex.org/W2542257450","https://openalex.org/W3216095215"],"abstract_inverted_index":{"This":[0],"work":[1],"examines":[2],"classical,":[3],"more":[4],"recent,":[5],"and":[6,42],"new":[7],"hyperspectral":[8,80],"detection":[9,74,87,97],"algorithms":[10,63,92],"that":[11,53],"stem":[12],"from":[13],"the":[14,18,29,40,62,86,90],"common":[15],"framework":[16],"of":[17,34,61,89],"decision-theory":[19],"based":[20],"statistical":[21],"likelihood":[22],"ratio":[23],"test":[24],"(LRT).":[25],"Within":[26],"this":[27],"context,":[28],"tradeoffs":[30],"involve":[31],"improving":[32],"models":[33],"target":[35,73,96],"spectral":[36],"variability,":[37],"accurately":[38],"characterizing":[39],"background,":[41],"producing":[43],"a":[44,71],"detector":[45],"with":[46,70],"closed-form":[47],"solution.":[48],"There":[49],"is":[50],"no":[51],"algorithm":[52],"has":[54],"shown":[55,83],"universally":[56],"best":[57],"performance,":[58],"but":[59],"each":[60],"can":[64],"be":[65],"specifically":[66],"suited":[67],"to":[68,84],"deal":[69],"given":[72],"scenario.":[75],"Experimental":[76],"results":[77],"featuring":[78],"real":[79],"data":[81],"are":[82],"compare":[85],"performance":[88],"examined":[91],"on":[93],"two":[94],"case-study":[95],"scenarios.":[98]},"counts_by_year":[{"year":2021,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
