{"id":"https://openalex.org/W2078264890","doi":"https://doi.org/10.1109/jstars.2014.2348537","title":"Locality Preserving Composite Kernel Feature Extraction for Multi-Source Geospatial Image Analysis","display_name":"Locality Preserving Composite Kernel Feature Extraction for Multi-Source Geospatial Image Analysis","publication_year":2014,"publication_date":"2014-09-12","ids":{"openalex":"https://openalex.org/W2078264890","doi":"https://doi.org/10.1109/jstars.2014.2348537","mag":"2078264890"},"language":"en","primary_location":{"id":"doi:10.1109/jstars.2014.2348537","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jstars.2014.2348537","pdf_url":null,"source":{"id":"https://openalex.org/S117727964","display_name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","issn_l":"1939-1404","issn":["1939-1404","2151-1535"],"is_oa":false,"is_in_doaj":true,"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 Journal of Selected Topics in Applied Earth Observations and Remote Sensing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"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/A5100321494","display_name":"Yuhang Zhang","orcid":"https://orcid.org/0000-0001-5382-6496"},"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"]},{"id":"https://openalex.org/I79463011","display_name":"Analysis Group (United States)","ror":"https://ror.org/044jp1563","country_code":"US","type":"company","lineage":["https://openalex.org/I79463011"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yuhang Zhang","raw_affiliation_strings":["Hyperspectral Image Analysis Group, Electrical and Computer Engineering Department, University of Houston, Houston, TX, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hyperspectral Image Analysis Group, Electrical and Computer Engineering Department, University of Houston, Houston, TX, USA","institution_ids":["https://openalex.org/I44461941","https://openalex.org/I79463011"]}]},{"author_position":"last","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"]},{"id":"https://openalex.org/I79463011","display_name":"Analysis Group (United States)","ror":"https://ror.org/044jp1563","country_code":"US","type":"company","lineage":["https://openalex.org/I79463011"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Saurabh Prasad","raw_affiliation_strings":["Hyperspectral Image Analysis Group, Electrical and Computer Engineering Department, University of Houston, Houston, TX, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hyperspectral Image Analysis Group, Electrical and Computer Engineering Department, University of Houston, Houston, TX, USA","institution_ids":["https://openalex.org/I44461941","https://openalex.org/I79463011"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1250,"currency":"USD","value_usd":1250},"apc_paid":null,"fwci":8.0565,"has_fulltext":false,"cited_by_count":33,"citation_normalized_percentile":{"value":0.97154851,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":"8","issue":"3","first_page":"1385","last_page":"1392"},"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":0.9957000017166138,"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/T10111","display_name":"Remote Sensing in Agriculture","score":0.989300012588501,"subfield":{"id":"https://openalex.org/subfields/2303","display_name":"Ecology"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"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":0.6812021732330322},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.638653039932251},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6234256625175476},{"id":"https://openalex.org/keywords/linear-discriminant-analysis","display_name":"Linear discriminant analysis","score":0.5689166188240051},{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.5573608875274658},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5536332726478577},{"id":"https://openalex.org/keywords/geospatial-analysis","display_name":"Geospatial analysis","score":0.507644534111023},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.49150311946868896},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.336515873670578},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.21656212210655212}],"concepts":[{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6812021732330322},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.638653039932251},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6234256625175476},{"id":"https://openalex.org/C69738355","wikidata":"https://www.wikidata.org/wiki/Q1228929","display_name":"Linear discriminant analysis","level":2,"score":0.5689166188240051},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.5573608875274658},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5536332726478577},{"id":"https://openalex.org/C9770341","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Geospatial analysis","level":2,"score":0.507644534111023},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.49150311946868896},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.336515873670578},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.21656212210655212},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/jstars.2014.2348537","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jstars.2014.2348537","pdf_url":null,"source":{"id":"https://openalex.org/S117727964","display_name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","issn_l":"1939-1404","issn":["1939-1404","2151-1535"],"is_oa":false,"is_in_doaj":true,"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 Journal of Selected Topics in Applied Earth Observations and Remote Sensing","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.75,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[{"id":"https://openalex.org/G2827628297","display_name":"IN THIS CONTEXT, THE CENTRAL OBJECTIVES OF THIS 3-YEAR NIP PROJECT ARE AS FOLLOWS (1) DEVELOPMENT, OPTIMIZATION AND VALIDATION OF A NOVEL STATISTICAL","funder_award_id":"NNX14AI47G","funder_id":"https://openalex.org/F4320306101","funder_display_name":"National Aeronautics and Space Administration"},{"id":"https://openalex.org/G7791526279","display_name":"ADVANCES IN OPTICAL REMOTE SENSING TECHNOLOGY OVER THE PAST TWO DECADES HAVE ENABLED THE DRAMATIC INCREASE IN SPATIAL, SPECTRAL, AND TEMPORAL DATA NO","funder_award_id":"NNX12AL49G","funder_id":"https://openalex.org/F4320306101","funder_display_name":"National Aeronautics and Space Administration"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320306101","display_name":"National Aeronautics and Space Administration","ror":"https://ror.org/027ka1x80"},{"id":"https://openalex.org/F4320309549","display_name":"University of Houston","ror":"https://ror.org/040vwpm13"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W1978625957","https://openalex.org/W2001150117","https://openalex.org/W2029691617","https://openalex.org/W2063385051","https://openalex.org/W2067532478","https://openalex.org/W2083522613","https://openalex.org/W2091757609","https://openalex.org/W2109531142","https://openalex.org/W2127199143","https://openalex.org/W2137933418","https://openalex.org/W2140996489","https://openalex.org/W2142387771","https://openalex.org/W2149324654","https://openalex.org/W2150579376","https://openalex.org/W2151288205","https://openalex.org/W2159070926","https://openalex.org/W2164330327","https://openalex.org/W2165796970","https://openalex.org/W3014771378","https://openalex.org/W3119651796","https://openalex.org/W6673782220","https://openalex.org/W6676189307"],"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/W2404757046","https://openalex.org/W2070598848","https://openalex.org/W2019190440","https://openalex.org/W3034864990","https://openalex.org/W2343470940"],"abstract_inverted_index":{"Multi-source":[0],"data,":[1],"either":[2],"from":[3,10,45,141],"different":[4,46,122],"sensors":[5],"or":[6],"disparate":[7],"features":[8],"extracted":[9],"the":[11,76,113,131,142,166],"same":[12],"sensor,":[13],"are":[14,48,145,175],"valuable":[15],"for":[16,24,38,108,150,159],"geospatial":[17,126,204],"image":[18,144,152],"analysis":[19,70,186],"due":[20],"to":[21,60,74],"their":[22],"potential":[23],"providing":[25],"complementary":[26],"features.":[27],"In":[28],"this":[29],"paper,":[30],"a":[31,52,61,85,148],"composite-kernel-based":[32],"feature":[33,129,198],"extraction":[34,199],"method":[35,115],"is":[36,72,116,194],"proposed":[37,114],"multi-source":[39,87,125,151,156,203],"remote":[40],"sensing":[41],"data":[42,88,134,174],"classification.":[43,109],"Features":[44],"sources":[47],"first":[49],"fused":[50],"via":[51,118],"weighted":[53],"composite":[54,181],"kernel":[55,66,182],"mapping,":[56],"and":[57,96,135,168,171,200],"then":[58],"projected":[59],"lower-dimensional":[62],"subspace":[63],"in":[64,164],"which":[65,165],"local":[67,183],"Fisher":[68],"discriminant":[69,185],"(KLFDA)":[71],"used":[73,146,158],"extract":[75],"most":[77],"discriminative":[78],"information.":[79],"We":[80],"hypothesize":[81],"that":[82,180],"after":[83],"such":[84],"projection,":[86],"would":[89,105],"have":[90],"better":[91],"class":[92],"separability":[93],"between":[94],"classes,":[95],"an":[97],"efficient":[98],"linear":[99],"classification":[100,201],"model-multinomial":[101],"logistic":[102],"regression":[103],"(MLR)":[104],"be":[106],"suitable":[107],"The":[110,154],"efficacy":[111],"of":[112,124,202],"demonstrated":[117],"experiments":[119],"using":[120],"two":[121],"sets":[123],"data.":[127],"For":[128],"fusion,":[130,163],"raw":[132],"spectral":[133],"extended":[136],"multi-attribute":[137],"profiles":[138],"(EMAPs)":[139],"derived":[140],"hyperspectral":[143,167],"as":[147],"testbed":[149,157],"analysis.":[153],"second":[155],"validation":[160],"involves":[161],"sensor":[162],"light":[169],"detection":[170],"ranging":[172],"(LiDAR)":[173],"utilized.":[176],"Experimental":[177],"results":[178],"show":[179],"Fisher's":[184],"when":[187],"combined":[188],"with":[189],"MLR":[190],"based":[191],"classifier":[192],"(CKLFDA-MLR)":[193],"very":[195],"effective":[196],"at":[197],"images.":[205]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":4},{"year":2017,"cited_by_count":5},{"year":2016,"cited_by_count":9},{"year":2015,"cited_by_count":4},{"year":2014,"cited_by_count":1}],"updated_date":"2026-07-22T07:51:19.307946","created_date":"2025-10-10T00:00:00"}