{"id":"https://openalex.org/W2899937451","doi":"https://doi.org/10.1109/tgrs.2018.2870980","title":"Statistical Detection Theory Approach to Hyperspectral Image Classification","display_name":"Statistical Detection Theory Approach to Hyperspectral Image Classification","publication_year":2018,"publication_date":"2018-11-05","ids":{"openalex":"https://openalex.org/W2899937451","doi":"https://doi.org/10.1109/tgrs.2018.2870980","mag":"2899937451"},"language":"en","primary_location":{"id":"doi:10.1109/tgrs.2018.2870980","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2018.2870980","pdf_url":null,"source":{"id":"https://openalex.org/S111326731","display_name":"IEEE Transactions on Geoscience and Remote Sensing","issn_l":"0196-2892","issn":["0196-2892","1558-0644"],"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 Transactions on Geoscience and Remote Sensing","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/A5073412670","display_name":"Chein\u2010I Chang","orcid":"https://orcid.org/0000-0002-5450-4891"},"institutions":[{"id":"https://openalex.org/I126744593","display_name":"University of Maryland, Baltimore","ror":"https://ror.org/04rq5mt64","country_code":"US","type":"education","lineage":["https://openalex.org/I126744593"]},{"id":"https://openalex.org/I4210117728","display_name":"Center For Remote Sensing (United States)","ror":"https://ror.org/021wvg932","country_code":"US","type":"company","lineage":["https://openalex.org/I4210117728"]},{"id":"https://openalex.org/I79272384","display_name":"University of Maryland, Baltimore County","ror":"https://ror.org/02qskvh78","country_code":"US","type":"education","lineage":["https://openalex.org/I79272384"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Chein-I Chang","raw_affiliation_strings":["Remote Sensing Signal and Image Processing Laboratory, University of Maryland, Baltimore County","Remote Sensing Signal and Image Processing Laboratory, University of Maryland, Baltimore, MD, USA"],"raw_orcid":"https://orcid.org/0000-0002-5450-4891","affiliations":[{"raw_affiliation_string":"Remote Sensing Signal and Image Processing Laboratory, University of Maryland, Baltimore County","institution_ids":["https://openalex.org/I4210117728","https://openalex.org/I79272384"]},{"raw_affiliation_string":"Remote Sensing Signal and Image Processing Laboratory, University of Maryland, Baltimore, MD, USA","institution_ids":["https://openalex.org/I126744593"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5073412670"],"corresponding_institution_ids":["https://openalex.org/I126744593","https://openalex.org/I4210117728","https://openalex.org/I79272384"],"apc_list":null,"apc_paid":null,"fwci":4.6031,"has_fulltext":false,"cited_by_count":60,"citation_normalized_percentile":{"value":0.94785115,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"57","issue":"4","first_page":"2057","last_page":"2074"},"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":0.9758999943733215,"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":0.9739999771118164,"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/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7255111336708069},{"id":"https://openalex.org/keywords/a-priori-and-a-posteriori","display_name":"A priori and a posteriori","score":0.7001687288284302},{"id":"https://openalex.org/keywords/bayes-error-rate","display_name":"Bayes error rate","score":0.6863583326339722},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6682629585266113},{"id":"https://openalex.org/keywords/naive-bayes-classifier","display_name":"Naive Bayes classifier","score":0.6371921300888062},{"id":"https://openalex.org/keywords/maximum-a-posteriori-estimation","display_name":"Maximum a posteriori estimation","score":0.6121231317520142},{"id":"https://openalex.org/keywords/bayes-classifier","display_name":"Bayes classifier","score":0.606165885925293},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.5834022164344788},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5623701214790344},{"id":"https://openalex.org/keywords/statistical-classification","display_name":"Statistical classification","score":0.5073181986808777},{"id":"https://openalex.org/keywords/one-class-classification","display_name":"One-class classification","score":0.495357871055603},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4870362877845764},{"id":"https://openalex.org/keywords/bayes-theorem","display_name":"Bayes' theorem","score":0.45047318935394287},{"id":"https://openalex.org/keywords/confusion-matrix","display_name":"Confusion matrix","score":0.44587963819503784},{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.43220055103302},{"id":"https://openalex.org/keywords/linear-discriminant-analysis","display_name":"Linear discriminant analysis","score":0.41492825746536255},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3872813284397125},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.31080490350723267},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.2794663906097412},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.19338425993919373},{"id":"https://openalex.org/keywords/maximum-likelihood","display_name":"Maximum likelihood","score":0.10691013932228088},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.1039426326751709}],"concepts":[{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7255111336708069},{"id":"https://openalex.org/C75553542","wikidata":"https://www.wikidata.org/wiki/Q178161","display_name":"A priori and a posteriori","level":2,"score":0.7001687288284302},{"id":"https://openalex.org/C143809311","wikidata":"https://www.wikidata.org/wiki/Q4874458","display_name":"Bayes error rate","level":5,"score":0.6863583326339722},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6682629585266113},{"id":"https://openalex.org/C52001869","wikidata":"https://www.wikidata.org/wiki/Q812530","display_name":"Naive Bayes classifier","level":3,"score":0.6371921300888062},{"id":"https://openalex.org/C9810830","wikidata":"https://www.wikidata.org/wiki/Q635384","display_name":"Maximum a posteriori estimation","level":3,"score":0.6121231317520142},{"id":"https://openalex.org/C185207860","wikidata":"https://www.wikidata.org/wiki/Q17004744","display_name":"Bayes classifier","level":4,"score":0.606165885925293},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.5834022164344788},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5623701214790344},{"id":"https://openalex.org/C110083411","wikidata":"https://www.wikidata.org/wiki/Q1744628","display_name":"Statistical classification","level":2,"score":0.5073181986808777},{"id":"https://openalex.org/C34872919","wikidata":"https://www.wikidata.org/wiki/Q7092302","display_name":"One-class classification","level":3,"score":0.495357871055603},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4870362877845764},{"id":"https://openalex.org/C207201462","wikidata":"https://www.wikidata.org/wiki/Q182505","display_name":"Bayes' theorem","level":3,"score":0.45047318935394287},{"id":"https://openalex.org/C138602881","wikidata":"https://www.wikidata.org/wiki/Q2709591","display_name":"Confusion matrix","level":2,"score":0.44587963819503784},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.43220055103302},{"id":"https://openalex.org/C69738355","wikidata":"https://www.wikidata.org/wiki/Q1228929","display_name":"Linear discriminant analysis","level":2,"score":0.41492825746536255},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3872813284397125},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.31080490350723267},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.2794663906097412},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.19338425993919373},{"id":"https://openalex.org/C49781872","wikidata":"https://www.wikidata.org/wiki/Q1045555","display_name":"Maximum likelihood","level":2,"score":0.10691013932228088},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.1039426326751709},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tgrs.2018.2870980","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2018.2870980","pdf_url":null,"source":{"id":"https://openalex.org/S111326731","display_name":"IEEE Transactions on Geoscience and Remote Sensing","issn_l":"0196-2892","issn":["0196-2892","1558-0644"],"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 Transactions on Geoscience and Remote Sensing","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320322874","display_name":"Universit\u00e0 degli Studi di Pavia","ror":"https://ror.org/00s6t1f81"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":50,"referenced_works":["https://openalex.org/W1482289351","https://openalex.org/W1494131642","https://openalex.org/W1532610010","https://openalex.org/W1663973292","https://openalex.org/W2001298023","https://openalex.org/W2011092189","https://openalex.org/W2014327494","https://openalex.org/W2029900646","https://openalex.org/W2031510368","https://openalex.org/W2062822804","https://openalex.org/W2092869901","https://openalex.org/W2097915756","https://openalex.org/W2101252963","https://openalex.org/W2101365302","https://openalex.org/W2104269704","https://openalex.org/W2113464037","https://openalex.org/W2113513024","https://openalex.org/W2114819256","https://openalex.org/W2131864940","https://openalex.org/W2132549764","https://openalex.org/W2136251662","https://openalex.org/W2144244295","https://openalex.org/W2160662337","https://openalex.org/W2163599171","https://openalex.org/W2164330327","https://openalex.org/W2164437025","https://openalex.org/W2166923144","https://openalex.org/W2211548590","https://openalex.org/W2291039747","https://openalex.org/W2315347323","https://openalex.org/W2320738207","https://openalex.org/W2344373810","https://openalex.org/W2493273699","https://openalex.org/W2516506474","https://openalex.org/W2522698497","https://openalex.org/W2606507269","https://openalex.org/W2613575128","https://openalex.org/W2620858446","https://openalex.org/W2743091961","https://openalex.org/W2759518055","https://openalex.org/W2772147448","https://openalex.org/W2772762696","https://openalex.org/W2779054585","https://openalex.org/W2795396578","https://openalex.org/W3100245404","https://openalex.org/W3133603318","https://openalex.org/W3195212285","https://openalex.org/W4210699701","https://openalex.org/W4212863985","https://openalex.org/W4248253651"],"related_works":["https://openalex.org/W2360982908","https://openalex.org/W2057359786","https://openalex.org/W2374047926","https://openalex.org/W2899083742","https://openalex.org/W1986699031","https://openalex.org/W2089577785","https://openalex.org/W2070410525","https://openalex.org/W4238503191","https://openalex.org/W145653800","https://openalex.org/W2899937451"],"abstract_inverted_index":{"This":[0],"paper":[1],"presents":[2],"a":[3,29,34,59,63,75,84,105,117,120,122,149,166,185,204,212],"statistical":[4,42],"detection":[5,31,43,72,81,86],"theory":[6,44],"approach":[7],"to":[8,49,70,96,126,140,208],"hyperspectral":[9],"image":[10],"(HSI)":[11],"classification":[12,25,36,51,61,97,101,107,124,137,151,206],"which":[13,172],"is":[14,132,195],"quite":[15],"different":[16],"from":[17,104],"many":[18],"conventional":[19],"approaches":[20],"reported":[21],"in":[22,68,80,138,175,188],"the":[23,40,114,196],"HSI":[24],"literature.":[26],"It":[27],"translates":[28],"multi-target":[30],"problem":[32,37],"into":[33],"multi-class":[35],"so":[38],"that":[39,144,165,199],"well-established":[41],"can":[45,92,145,200],"be":[46,94,127,146,201],"readily":[47],"applicable":[48],"solving":[50],"problems.":[52],"In":[53],"particular,":[54],"two":[55],"types":[56],"of":[57,116,177,190],"classification,":[58,65,119],"priori":[60,150,167],"and":[62,73,88,99,153],"posteriori":[64,76,118,123,186,205],"are":[66],"developed":[67],"corresponding":[69],"Bayes":[71,158,170],"maximum":[74],"(MAP)":[77],"detection,":[78],"respectively,":[79],"theory.":[82],"As":[83],"result,":[85],"probability":[87,91],"false":[89,100],"alarm":[90],"also":[93,133],"translated":[95],"rate":[98,102,130],"derived":[103],"confusion":[106],"matrix":[108],"used":[109,156,202],"for":[110,157],"classification.":[111,159],"To":[112],"evaluate":[113,209],"effectiveness":[115],"new":[121],"measure,":[125],"called":[128],"precision":[129],"(PR),":[131],"introduced":[134],"by":[135],"MAP":[136],"contrast":[139],"overall":[141],"accuracy":[142],"(OA)":[143],"considered":[147],"as":[148,169,184,203],"measure":[152,207],"has":[154],"been":[155],"The":[160],"experimental":[161],"results":[162],"provide":[163],"evidence":[164],"classifier":[168,171,187,213],"performs":[173],"well":[174,183,211],"terms":[176,189],"OA":[178],"does":[179],"not":[180],"necessarily":[181],"perform":[182],"PR.":[191],"That":[192],"is,":[193],"PR":[194],"only":[197],"criterion":[198],"how":[210],"performs.":[214]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":8},{"year":2022,"cited_by_count":12},{"year":2021,"cited_by_count":12},{"year":2020,"cited_by_count":11},{"year":2019,"cited_by_count":4},{"year":2018,"cited_by_count":1}],"updated_date":"2026-07-22T07:51:19.307946","created_date":"2025-10-10T00:00:00"}
