{"id":"https://openalex.org/W2009664594","doi":"https://doi.org/10.1109/whispers.2012.6874299","title":"Locality-preserving discriminant analysis and Gaussian mixture models for spectral-spatial classification of hyperspectral imagery","display_name":"Locality-preserving discriminant analysis and Gaussian mixture models for spectral-spatial classification of hyperspectral imagery","publication_year":2012,"publication_date":"2012-06-01","ids":{"openalex":"https://openalex.org/W2009664594","doi":"https://doi.org/10.1109/whispers.2012.6874299","mag":"2009664594"},"language":"en","primary_location":{"id":"doi:10.1109/whispers.2012.6874299","is_oa":false,"landing_page_url":"https://doi.org/10.1109/whispers.2012.6874299","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2012 4th Workshop on Hyperspectral Image and Signal Processing (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/A5100731629","display_name":"Zhen Ye","orcid":"https://orcid.org/0000-0001-5410-863X"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhen Ye","raw_affiliation_strings":["Northwestern Polytechnical University, Xi'an, PR China","Northwestern Polytech. Univ., Xi'an, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Northwestern Polytechnical University, Xi'an, PR China","institution_ids":["https://openalex.org/I17145004"]},{"raw_affiliation_string":"Northwestern Polytech. Univ., Xi'an, China","institution_ids":["https://openalex.org/I17145004"]}]},{"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":["University of Houston, Houston, TX, USA","University of Houston; Houston, TX USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Houston, Houston, TX, USA","institution_ids":["https://openalex.org/I44461941"]},{"raw_affiliation_string":"University of Houston; Houston, TX USA","institution_ids":["https://openalex.org/I44461941"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100317994","display_name":"Wei Li","orcid":"https://orcid.org/0000-0001-7015-7335"},"institutions":[{"id":"https://openalex.org/I99041443","display_name":"Mississippi State University","ror":"https://ror.org/0432jq872","country_code":"US","type":"education","lineage":["https://openalex.org/I4210141039","https://openalex.org/I99041443"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wei Li","raw_affiliation_strings":["Mississippi State University, MS, USA","Mississippi State University; Starkville MS USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Mississippi State University, MS, USA","institution_ids":["https://openalex.org/I99041443"]},{"raw_affiliation_string":"Mississippi State University; Starkville MS USA","institution_ids":["https://openalex.org/I99041443"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078795882","display_name":"James E. Fowler","orcid":"https://orcid.org/0000-0003-2005-405X"},"institutions":[{"id":"https://openalex.org/I99041443","display_name":"Mississippi State University","ror":"https://ror.org/0432jq872","country_code":"US","type":"education","lineage":["https://openalex.org/I4210141039","https://openalex.org/I99041443"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"James E. Fowler","raw_affiliation_strings":["Mississippi State University, MS, USA","Mississippi State University; Starkville MS USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Mississippi State University, MS, USA","institution_ids":["https://openalex.org/I99041443"]},{"raw_affiliation_string":"Mississippi State University; Starkville MS USA","institution_ids":["https://openalex.org/I99041443"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5086729425","display_name":"Mingyi He","orcid":"https://orcid.org/0000-0003-2051-6955"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Mingyi He","raw_affiliation_strings":["Northwestern Polytechnical University, Xi'an, PR China","Northwestern Polytech. Univ., Xi'an, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Northwestern Polytechnical University, Xi'an, PR China","institution_ids":["https://openalex.org/I17145004"]},{"raw_affiliation_string":"Northwestern Polytech. Univ., Xi'an, China","institution_ids":["https://openalex.org/I17145004"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"8","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/T13890","display_name":"Remote Sensing and Land Use","score":0.9948999881744385,"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":0.9401999711990356,"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/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.8424667119979858},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7813115119934082},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7368463277816772},{"id":"https://openalex.org/keywords/subspace-topology","display_name":"Subspace topology","score":0.6078288555145264},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5654918551445007},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.53785240650177},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.4907546937465668},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.48385798931121826},{"id":"https://openalex.org/keywords/wavelet","display_name":"Wavelet","score":0.4664594233036041},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.4612303078174591},{"id":"https://openalex.org/keywords/linear-discriminant-analysis","display_name":"Linear discriminant analysis","score":0.44513779878616333},{"id":"https://openalex.org/keywords/spatial-correlation","display_name":"Spatial correlation","score":0.4248836040496826},{"id":"https://openalex.org/keywords/mixture-model","display_name":"Mixture model","score":0.4216683506965637},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3651573061943054},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.20193907618522644}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.8424667119979858},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7813115119934082},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7368463277816772},{"id":"https://openalex.org/C32834561","wikidata":"https://www.wikidata.org/wiki/Q660730","display_name":"Subspace topology","level":2,"score":0.6078288555145264},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5654918551445007},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.53785240650177},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.4907546937465668},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.48385798931121826},{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.4664594233036041},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.4612303078174591},{"id":"https://openalex.org/C69738355","wikidata":"https://www.wikidata.org/wiki/Q1228929","display_name":"Linear discriminant analysis","level":2,"score":0.44513779878616333},{"id":"https://openalex.org/C150060386","wikidata":"https://www.wikidata.org/wiki/Q7574054","display_name":"Spatial correlation","level":2,"score":0.4248836040496826},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.4216683506965637},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3651573061943054},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.20193907618522644},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"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/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/whispers.2012.6874299","is_oa":false,"landing_page_url":"https://doi.org/10.1109/whispers.2012.6874299","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2012 4th Workshop on Hyperspectral Image and Signal Processing (WHISPERS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","score":0.7799999713897705,"display_name":"Reduced inequalities"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W1546366002","https://openalex.org/W1963738471","https://openalex.org/W2096201283","https://openalex.org/W2097900616","https://openalex.org/W2104269704","https://openalex.org/W2109531142","https://openalex.org/W2112556443","https://openalex.org/W2135758523","https://openalex.org/W2151599207","https://openalex.org/W2154872931","https://openalex.org/W2157621128","https://openalex.org/W2163346236","https://openalex.org/W2164437025","https://openalex.org/W3014771378","https://openalex.org/W6676189307","https://openalex.org/W6682644385"],"related_works":["https://openalex.org/W2072166414","https://openalex.org/W3209970181","https://openalex.org/W2060875994","https://openalex.org/W3034375524","https://openalex.org/W4230131218","https://openalex.org/W2070598848","https://openalex.org/W2404757046","https://openalex.org/W2044184146","https://openalex.org/W4313014865","https://openalex.org/W2019190440"],"abstract_inverted_index":{"Traditional":[0],"hyperspectral":[1,33,113],"image":[2,34],"classification":[3,35,97],"typically":[4],"uses":[5],"raw":[6],"spectral":[7,22],"signatures":[8],"or":[9],"simple":[10],"spatial":[11,24],"characteristics":[12],"such":[13],"as":[14],"textural":[15],"features":[16,52,61],"without":[17],"considering":[18],"the":[19,88,92,118],"correlation":[20],"between":[21],"and":[23,56,65],"information.":[25],"In":[26],"this":[27,77,107],"paper,":[28],"we":[29],"propose":[30],"a":[31,38,83,101],"spectral-spatial":[32],"based":[36],"on":[37],"structured":[39],"multi-modal":[40,89],"statistical":[41,93],"model.":[42],"A":[43],"3D":[44],"wavelet":[45,79],"transform":[46],"is":[47,72],"employed":[48],"to":[49,75],"extract":[50],"relevant":[51],"from":[53],"every":[54],"pixel":[55],"its":[57],"neighboring":[58],"pixels;":[59],"these":[60],"quantify":[62],"local":[63],"orientation":[64],"scale":[66],"characteristics.":[67],"Local":[68],"Fisher's":[69],"discriminant":[70],"analysis":[71],"then":[73,99],"used":[74],"project":[76],"high-dimensional":[78],"coefficient":[80],"space":[81],"onto":[82],"lower-dimensional":[84],"subspace":[85],"while":[86],"preserving":[87],"structure":[90],"of":[91],"distributions.":[94],"The":[95],"proposed":[96,119],"framework":[98],"employs":[100],"Gaussian":[102],"mixture":[103],"model":[104],"classifier":[105],"in":[106],"feature":[108],"subspace.":[109],"Experimental":[110],"results":[111],"at":[112],"image-classification":[114],"tasks":[115],"show":[116],"that":[117],"approach":[120],"substantially":[121],"outperforms":[122],"traditional":[123],"methods.":[124]},"counts_by_year":[{"year":2017,"cited_by_count":1},{"year":2013,"cited_by_count":2}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
