{"id":"https://openalex.org/W2047650348","doi":"https://doi.org/10.1109/igarss.2013.6721342","title":"Hyperspectral image classification based on Dirichlet Process mixture models","display_name":"Hyperspectral image classification based on Dirichlet Process mixture models","publication_year":2013,"publication_date":"2013-07-01","ids":{"openalex":"https://openalex.org/W2047650348","doi":"https://doi.org/10.1109/igarss.2013.6721342","mag":"2047650348"},"language":"en","primary_location":{"id":"doi:10.1109/igarss.2013.6721342","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2013.6721342","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2013 IEEE International Geoscience and Remote Sensing Symposium - IGARSS","raw_type":"proceedings-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/A5100702419","display_name":"Hao Wu","orcid":"https://orcid.org/0000-0001-8899-2734"},"institutions":[{"id":"https://openalex.org/I44461941","display_name":"University of Houston","ror":"https://ror.org/048sx0r50","country_code":"US","type":"education","lineage":["https://openalex.org/I44461941"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Hao Wu","raw_affiliation_strings":["Department of Electrical and Computer Engineering, University of Houston, Houston, TX, USA","Dept. of Electr. & Comput. Eng., Univ. Of Houston, Houston, TX, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of Houston, Houston, TX, USA","institution_ids":["https://openalex.org/I44461941"]},{"raw_affiliation_string":"Dept. of Electr. & Comput. Eng., Univ. Of Houston, Houston, TX, USA","institution_ids":["https://openalex.org/I44461941"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037823063","display_name":"Saurabh Prasad","orcid":"https://orcid.org/0000-0003-3729-9360"},"institutions":[{"id":"https://openalex.org/I44461941","display_name":"University of Houston","ror":"https://ror.org/048sx0r50","country_code":"US","type":"education","lineage":["https://openalex.org/I44461941"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Saurabh Prasad","raw_affiliation_strings":["Department of Electrical and Computer Engineering, University of Houston, Houston, TX, USA","Dept. of Electr. & Comput. Eng., Univ. Of Houston, Houston, TX, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of Houston, Houston, TX, USA","institution_ids":["https://openalex.org/I44461941"]},{"raw_affiliation_string":"Dept. of Electr. & Comput. Eng., Univ. Of Houston, Houston, TX, USA","institution_ids":["https://openalex.org/I44461941"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111740765","display_name":"Minshan Cui","orcid":null},"institutions":[{"id":"https://openalex.org/I44461941","display_name":"University of Houston","ror":"https://ror.org/048sx0r50","country_code":"US","type":"education","lineage":["https://openalex.org/I44461941"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Minshan Cui","raw_affiliation_strings":["Department of Electrical and Computer Engineering, University of Houston, Houston, TX, USA","Dept. of Electr. & Comput. Eng., Univ. Of Houston, Houston, TX, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of Houston, Houston, TX, USA","institution_ids":["https://openalex.org/I44461941"]},{"raw_affiliation_string":"Dept. of Electr. & Comput. Eng., Univ. Of Houston, Houston, TX, USA","institution_ids":["https://openalex.org/I44461941"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101949671","display_name":"Nam Nguyen","orcid":"https://orcid.org/0009-0006-4548-3669"},"institutions":[{"id":"https://openalex.org/I44461941","display_name":"University of Houston","ror":"https://ror.org/048sx0r50","country_code":"US","type":"education","lineage":["https://openalex.org/I44461941"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Nam Tuan Nguyen","raw_affiliation_strings":["Department of Electrical and Computer Engineering, University of Houston, Houston, TX, USA","Dept. of Electr. & Comput. Eng., Univ. Of Houston, Houston, TX, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of Houston, Houston, TX, USA","institution_ids":["https://openalex.org/I44461941"]},{"raw_affiliation_string":"Dept. of Electr. & Comput. Eng., Univ. Of Houston, Houston, TX, USA","institution_ids":["https://openalex.org/I44461941"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5063667378","display_name":"Zhu Han","orcid":"https://orcid.org/0000-0002-6606-5822"},"institutions":[{"id":"https://openalex.org/I44461941","display_name":"University of Houston","ror":"https://ror.org/048sx0r50","country_code":"US","type":"education","lineage":["https://openalex.org/I44461941"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zhu Han","raw_affiliation_strings":["Department of Electrical and Computer Engineering, University of Houston, Houston, TX, USA","Dept. of Electr. & Comput. Eng., Univ. Of Houston, Houston, TX, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of Houston, Houston, TX, USA","institution_ids":["https://openalex.org/I44461941"]},{"raw_affiliation_string":"Dept. of Electr. & Comput. Eng., Univ. Of Houston, Houston, TX, USA","institution_ids":["https://openalex.org/I44461941"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5100702419"],"corresponding_institution_ids":["https://openalex.org/I44461941"],"apc_list":null,"apc_paid":null,"fwci":1.4427,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.85690274,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"8","issue":null,"first_page":"1043","last_page":"1046"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9973000288009644,"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"}},"topics":[{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9973000288009644,"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/T10689","display_name":"Remote-Sensing Image Classification","score":0.9930999875068665,"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/T11667","display_name":"Advanced Chemical Sensor Technologies","score":0.9666000008583069,"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":0.7659645080566406},{"id":"https://openalex.org/keywords/mixture-model","display_name":"Mixture model","score":0.7090311050415039},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6569467782974243},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6358513236045837},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.6025035977363586},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5785809755325317},{"id":"https://openalex.org/keywords/dirichlet-process","display_name":"Dirichlet process","score":0.4746953845024109},{"id":"https://openalex.org/keywords/dimensionality-reduction","display_name":"Dimensionality reduction","score":0.4744965732097626},{"id":"https://openalex.org/keywords/parametric-statistics","display_name":"Parametric statistics","score":0.43328091502189636},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.42089200019836426},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3746338486671448},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.34845373034477234},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.29647698998451233},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.1265367865562439}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.7659645080566406},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.7090311050415039},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6569467782974243},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6358513236045837},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.6025035977363586},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5785809755325317},{"id":"https://openalex.org/C2781280628","wikidata":"https://www.wikidata.org/wiki/Q5280766","display_name":"Dirichlet process","level":3,"score":0.4746953845024109},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.4744965732097626},{"id":"https://openalex.org/C117251300","wikidata":"https://www.wikidata.org/wiki/Q1849855","display_name":"Parametric statistics","level":2,"score":0.43328091502189636},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.42089200019836426},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3746338486671448},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.34845373034477234},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.29647698998451233},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.1265367865562439},{"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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss.2013.6721342","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2013.6721342","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2013 IEEE International Geoscience and Remote Sensing Symposium - IGARSS","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.699999988079071,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W195465510","https://openalex.org/W1570876803","https://openalex.org/W2080972498","https://openalex.org/W2109531142","https://openalex.org/W2111957265","https://openalex.org/W2120184245","https://openalex.org/W2120636621","https://openalex.org/W2121416338","https://openalex.org/W2128002512","https://openalex.org/W2151599207","https://openalex.org/W2168175751","https://openalex.org/W3014771378","https://openalex.org/W6676189307","https://openalex.org/W6678007500","https://openalex.org/W6678207219","https://openalex.org/W7038517902"],"related_works":["https://openalex.org/W2292979300","https://openalex.org/W4386716613","https://openalex.org/W4293482264","https://openalex.org/W1614270630","https://openalex.org/W1605972624","https://openalex.org/W1833498382","https://openalex.org/W3194589442","https://openalex.org/W1602151161","https://openalex.org/W2972093345","https://openalex.org/W1854524123"],"abstract_inverted_index":{"In":[0],"this":[1,49],"work,":[2],"we":[3,46,105,116],"propose":[4],"a":[5,63,102,192],"new":[6],"density":[7],"estimation":[8],"method":[9,66,146,168,171],"for":[10,112],"hyperspectral":[11,41,127,155],"image":[12],"data":[13],"based":[14,60,90,144],"on":[15,91],"Dirichlet":[16],"Process":[17],"Gaussian":[18,25,84],"mixture":[19,26,44,85],"models":[20,27,86],"(also":[21],"known":[22],"as":[23],"infinite":[24],"-":[28],"IGMMs),":[29],"which":[30,74,129],"successfully":[31],"captures":[32],"the":[33,55,69,96,121,126,134,148,165,174,183],"complex":[34],"multi-modal":[35,122],"(potentially":[36],"non-Gaussian)":[37],"statistical":[38],"structure":[39,124],"of":[40,71,98,125,136,160,195],"data.":[42],"The":[43,158],"model":[45,72,89,99],"get":[47],"from":[48],"will":[50,130],"then":[51],"be":[52],"applied":[53],"to":[54,119,147],"classification":[56,145,151,187],"problem.":[57],"This":[58],"IGMM":[59,143],"approach":[61],"is":[62,75,191],"non-parametric":[64],"Bayesian":[65],"helping":[67],"circumvent":[68],"problem":[70],"selection,":[73],"unavoidable":[76],"and":[77,169,180],"often":[78],"difficult":[79],"when":[80,189],"employing":[81],"traditional":[82],"parametric":[83],"(GMM).":[87],"Inference":[88],"Gibbs":[92],"sampling":[93],"employed":[94],"during":[95],"inference":[97],"parameters.":[100],"As":[101],"preprocessing":[103],"step,":[104],"use":[106],"Local":[107],"Fisher's":[108],"Discriminant":[109],"Analysis":[110],"(LFDA)":[111],"dimension":[113],"reduction":[114],"since":[115],"expect":[117],"it":[118],"preserve":[120],"non-Gaussian":[123],"data,":[128],"benefit":[131],"much":[132],"in":[133],"aspect":[135],"computation":[137],"cost.":[138],"We":[139],"compared":[140],"our":[141,161],"proposed":[142,166],"existing":[149],"state-of-the-art":[150],"methods":[152],"using":[153],"popular":[154],"imagery":[156],"datasets.":[157],"results":[159],"experiments":[162],"show":[163],"that":[164],"LFDA-IGMM":[167],"GMM":[170],"have":[172],"almost":[173],"same":[175],"performance":[176],"(sometimes":[177],"outperforming":[178],"LFDA-GMM),":[179],"they":[181],"outperform":[182],"other":[184],"commonly":[185],"used":[186],"approaches":[188],"there":[190],"sufficient":[193],"number":[194],"training":[196],"samples.":[197]},"counts_by_year":[{"year":2021,"cited_by_count":1},{"year":2016,"cited_by_count":1},{"year":2014,"cited_by_count":1},{"year":2013,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}