{"id":"https://openalex.org/W2766516024","doi":"https://doi.org/10.1109/whispers.2013.8080717","title":"Robust sub-pixel hyperspectral classification via locality preserving feature extraction and a dirichlet process mixture model","display_name":"Robust sub-pixel hyperspectral classification via locality preserving feature extraction and a dirichlet process mixture model","publication_year":2013,"publication_date":"2013-06-01","ids":{"openalex":"https://openalex.org/W2766516024","doi":"https://doi.org/10.1109/whispers.2013.8080717","mag":"2766516024"},"language":"en","primary_location":{"id":"doi:10.1109/whispers.2013.8080717","is_oa":false,"landing_page_url":"https://doi.org/10.1109/whispers.2013.8080717","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/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":false,"raw_author_name":"Hao Wu","raw_affiliation_strings":["Department of Electrical and Computer Engineering, University of Houston"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of Houston","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"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of Houston","institution_ids":["https://openalex.org/I44461941"]}]},{"author_position":"last","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"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of Houston","institution_ids":["https://openalex.org/I44461941"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I44461941"],"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":"9","issue":null,"first_page":"1","last_page":"4"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9962999820709229,"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":0.9962999820709229,"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":0.9947999715805054,"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":0.9781000018119812,"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":0.7856298089027405},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7473282814025879},{"id":"https://openalex.org/keywords/mixture-model","display_name":"Mixture model","score":0.7064796686172485},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6874980330467224},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5827034711837769},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.5307435989379883},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.5006129741668701},{"id":"https://openalex.org/keywords/dirichlet-distribution","display_name":"Dirichlet distribution","score":0.4809074401855469},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4519680142402649},{"id":"https://openalex.org/keywords/parametric-statistics","display_name":"Parametric statistics","score":0.42277437448501587},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.41730278730392456},{"id":"https://openalex.org/keywords/dimensionality-reduction","display_name":"Dimensionality reduction","score":0.41590744256973267},{"id":"https://openalex.org/keywords/dirichlet-process","display_name":"Dirichlet process","score":0.41144225001335144},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.37088048458099365},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3101513981819153},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.2993919849395752},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.17797726392745972},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.13242751359939575}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.7856298089027405},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7473282814025879},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.7064796686172485},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6874980330467224},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5827034711837769},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.5307435989379883},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.5006129741668701},{"id":"https://openalex.org/C169214877","wikidata":"https://www.wikidata.org/wiki/Q981016","display_name":"Dirichlet distribution","level":3,"score":0.4809074401855469},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4519680142402649},{"id":"https://openalex.org/C117251300","wikidata":"https://www.wikidata.org/wiki/Q1849855","display_name":"Parametric statistics","level":2,"score":0.42277437448501587},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.41730278730392456},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.41590744256973267},{"id":"https://openalex.org/C2781280628","wikidata":"https://www.wikidata.org/wiki/Q5280766","display_name":"Dirichlet process","level":3,"score":0.41144225001335144},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.37088048458099365},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3101513981819153},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.2993919849395752},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.17797726392745972},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.13242751359939575},{"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/C182310444","wikidata":"https://www.wikidata.org/wiki/Q1332643","display_name":"Boundary value problem","level":2,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","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/whispers.2013.8080717","is_oa":false,"landing_page_url":"https://doi.org/10.1109/whispers.2013.8080717","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":[{"display_name":"Reduced inequalities","score":0.6899999976158142,"id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W84941271","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/W2144675138","https://openalex.org/W2151599207","https://openalex.org/W2168175751","https://openalex.org/W3014771378","https://openalex.org/W3150904112","https://openalex.org/W4232383088","https://openalex.org/W6676189307","https://openalex.org/W7038517902"],"related_works":["https://openalex.org/W4386716613","https://openalex.org/W4293482264","https://openalex.org/W1902157726","https://openalex.org/W2393894849","https://openalex.org/W2172796364","https://openalex.org/W197558907","https://openalex.org/W1605972624","https://openalex.org/W3122449418","https://openalex.org/W3190171609","https://openalex.org/W1833498382"],"abstract_inverted_index":{"In":[0,58,80],"this":[1,30],"work":[2],"we":[3,37,66,84,95,133],"present":[4],"and":[5,47,139,155],"apply":[6],"a":[7,33,68],"new":[8],"Hyperspectral":[9],"image":[10],"classification":[11,114,120,145,186],"method":[12,71,115,154,157,200],"based":[13,113,199],"on":[14],"Dirichlet":[15],"Process":[16],"Gaussian":[17,24,54],"mixture":[18,25,55],"models":[19,26,56],"(also":[20],"known":[21],"as":[22],"infinite":[23],"-":[27,169],"IGMMs).":[28],"Since":[29],"approach":[31],"is":[32,45,193,201],"non-parametric":[34],"Bayesian":[35],"method,":[36],"circumvent":[38],"the":[39,74,81,100,105,117,141,151,160,182,189,197],"problem":[40],"of":[41,77,104,143],"model":[42,62],"selection":[43],"which":[44],"unavoidable":[46],"often":[48],"difficult":[49],"when":[50,188],"employing":[51],"traditional":[52],"parametric":[53],"(GMM).":[57],"order":[59],"to":[60,72,98,116,204],"infer":[61],"parameters":[63],"from":[64],"observations,":[65],"adopt":[67],"Gibbs":[69],"Sampling":[70],"sample":[73,191],"posterior":[75],"distributions":[76],"these":[78,144],"parameters.":[79],"preprocessing":[82],"step,":[83],"use":[85],"Local":[86],"Fisher's":[87],"Discriminant":[88],"Analysis":[89],"(LFDA)":[90],"for":[91,163,176],"dimension":[92],"reduction":[93],"since":[94],"expect":[96],"it":[97],"preserve":[99],"multi-modal,":[101],"non-Gaussian":[102],"structure":[103],"hyperspectral":[106,124],"data.":[107],"We":[108],"compared":[109],"our":[110],"proposed":[111,152],"IGMM":[112,198],"existing":[118],"state-of-the-art":[119],"methods":[121,180],"using":[122],"popular":[123],"imagery":[125],"datasets.":[126],"To":[127],"simulate":[128],"challenging":[129],"real-world":[130],"sensing":[131],"environment,":[132],"mix":[134],"every":[135],"pixel":[136,205],"with":[137,170],"background":[138],"study":[140],"performance":[142,162],"approaches.":[146],"Our":[147],"experiments":[148],"show":[149],"that":[150],"LFDA-IGMM":[153,173],"GMM":[156,175],"have":[158],"almost":[159],"same":[161],"most":[164],"cases,":[165],"given":[166],"\u201cpure\u201d":[167],"data":[168],"mixed":[171],"pixels,":[172],"outperformed":[174,181],"sub-pixel":[177],"classification.":[178],"Both":[179],"other":[183,208],"commonly":[184],"used":[185],"approaches":[187],"training":[190],"size":[192],"relatively":[194],"large.":[195],"Also,":[196],"less":[202],"sensitive":[203],"mixing":[206],"than":[207],"methods.":[209]},"counts_by_year":[{"year":2013,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}