{"id":"https://openalex.org/W2113464037","doi":"https://doi.org/10.1109/tgrs.2010.2060550","title":"Semisupervised Hyperspectral Image Segmentation Using Multinomial Logistic Regression With Active Learning","display_name":"Semisupervised Hyperspectral Image Segmentation Using Multinomial Logistic Regression With Active Learning","publication_year":2010,"publication_date":"2010-09-01","ids":{"openalex":"https://openalex.org/W2113464037","doi":"https://doi.org/10.1109/tgrs.2010.2060550","mag":"2113464037"},"language":"en","primary_location":{"id":"doi:10.1109/tgrs.2010.2060550","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2010.2060550","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/A5100362041","display_name":"Jun Li","orcid":"https://orcid.org/0000-0003-1613-9448"},"institutions":[{"id":"https://openalex.org/I4210120471","display_name":"Instituto de Telecomunica\u00e7\u00f5es","ror":"https://ror.org/02ht4fk33","country_code":"PT","type":"nonprofit","lineage":["https://openalex.org/I4210120471"]},{"id":"https://openalex.org/I141596103","display_name":"University of Lisbon","ror":"https://ror.org/01c27hj86","country_code":"PT","type":"education","lineage":["https://openalex.org/I141596103"]}],"countries":["PT"],"is_corresponding":true,"raw_author_name":"Jun Li","raw_affiliation_strings":["Instituto de Telecomunica\u00e7\u00f5es and Instituto Superior T\u00e9cnico (IST), Technical University of Lisbon, Lisboa, Portugal","Inst. de Telecomun. & Inst. Super. Tecnico (IST), Tech. Univ. of Lisbon, Lisbon, Portugal"],"affiliations":[{"raw_affiliation_string":"Instituto de Telecomunica\u00e7\u00f5es and Instituto Superior T\u00e9cnico (IST), Technical University of Lisbon, Lisboa, Portugal","institution_ids":["https://openalex.org/I4210120471"]},{"raw_affiliation_string":"Inst. de Telecomun. & Inst. Super. Tecnico (IST), Tech. Univ. of Lisbon, Lisbon, Portugal","institution_ids":["https://openalex.org/I141596103"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017508063","display_name":"Jos\u00e9 M. Bioucas\u2010Dias","orcid":"https://orcid.org/0000-0002-0166-5149"},"institutions":[{"id":"https://openalex.org/I4210120471","display_name":"Instituto de Telecomunica\u00e7\u00f5es","ror":"https://ror.org/02ht4fk33","country_code":"PT","type":"nonprofit","lineage":["https://openalex.org/I4210120471"]},{"id":"https://openalex.org/I141596103","display_name":"University of Lisbon","ror":"https://ror.org/01c27hj86","country_code":"PT","type":"education","lineage":["https://openalex.org/I141596103"]}],"countries":["PT"],"is_corresponding":false,"raw_author_name":"Jos\u00e9 M. Bioucas-Dias","raw_affiliation_strings":["Instituto de Telecomunica\u00e7\u00f5es and Instituto Superior T\u00e9cnico (IST), Technical University of Lisbon, Lisboa, Portugal","Inst. de Telecomun. & Inst. Super. Tecnico (IST), Tech. Univ. of Lisbon, Lisbon, Portugal"],"affiliations":[{"raw_affiliation_string":"Instituto de Telecomunica\u00e7\u00f5es and Instituto Superior T\u00e9cnico (IST), Technical University of Lisbon, Lisboa, Portugal","institution_ids":["https://openalex.org/I4210120471"]},{"raw_affiliation_string":"Inst. de Telecomun. & Inst. Super. Tecnico (IST), Tech. Univ. of Lisbon, Lisbon, Portugal","institution_ids":["https://openalex.org/I141596103"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5054292278","display_name":"Antonio Plaza","orcid":"https://orcid.org/0000-0002-9613-1659"},"institutions":[{"id":"https://openalex.org/I80606768","display_name":"Universidad de Extremadura","ror":"https://ror.org/0174shg90","country_code":"ES","type":"education","lineage":["https://openalex.org/I80606768"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Antonio Plaza","raw_affiliation_strings":["Department of Technology of Computers and Communications, University of Extremadura, Caceres, Spain","Dept. of Technol. of Comput. & Commun., Univ. of Extremadura, C\u00e1ceres, Spain"],"affiliations":[{"raw_affiliation_string":"Department of Technology of Computers and Communications, University of Extremadura, Caceres, Spain","institution_ids":["https://openalex.org/I80606768"]},{"raw_affiliation_string":"Dept. of Technol. of Comput. & Commun., Univ. of Extremadura, C\u00e1ceres, Spain","institution_ids":["https://openalex.org/I80606768"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5100362041"],"corresponding_institution_ids":["https://openalex.org/I141596103","https://openalex.org/I4210120471"],"apc_list":null,"apc_paid":null,"fwci":40.75,"has_fulltext":false,"cited_by_count":588,"citation_normalized_percentile":{"value":0.9990797,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":100},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"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/T10057","display_name":"Face and Expression Recognition","score":0.9789000153541565,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.968500018119812,"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":0.8284165859222412},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6876555681228638},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6842386722564697},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6197641491889954},{"id":"https://openalex.org/keywords/markov-random-field","display_name":"Markov random field","score":0.6118959784507751},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.5918826460838318},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5783342123031616},{"id":"https://openalex.org/keywords/multinomial-logistic-regression","display_name":"Multinomial logistic regression","score":0.5272009968757629},{"id":"https://openalex.org/keywords/imaging-spectrometer","display_name":"Imaging spectrometer","score":0.41139212250709534},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.20238804817199707}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.8284165859222412},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6876555681228638},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6842386722564697},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6197641491889954},{"id":"https://openalex.org/C2778045648","wikidata":"https://www.wikidata.org/wiki/Q176827","display_name":"Markov random field","level":4,"score":0.6118959784507751},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.5918826460838318},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5783342123031616},{"id":"https://openalex.org/C117568660","wikidata":"https://www.wikidata.org/wiki/Q1650843","display_name":"Multinomial logistic regression","level":2,"score":0.5272009968757629},{"id":"https://openalex.org/C183852935","wikidata":"https://www.wikidata.org/wiki/Q6002848","display_name":"Imaging spectrometer","level":3,"score":0.41139212250709534},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.20238804817199707},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C33390570","wikidata":"https://www.wikidata.org/wiki/Q188463","display_name":"Spectrometer","level":2,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/tgrs.2010.2060550","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2010.2060550","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"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.386.1516","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.386.1516","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.lx.it.pt/~bioucas/files/ieee_tgrs_semi_10.pdf","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.388.1725","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.388.1725","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.umbc.edu/rssipl/people/aplaza/Papers/Conferences/2009.WHISPERS.Semisupervised.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2689612763","display_name":null,"funder_award_id":"Marie","funder_id":"https://openalex.org/F4320320300","funder_display_name":"European Commission"},{"id":"https://openalex.org/G4681381370","display_name":null,"funder_award_id":"Marie Curie","funder_id":"https://openalex.org/F4320320300","funder_display_name":"European Commission"},{"id":"https://openalex.org/G8051717526","display_name":null,"funder_award_id":"Grant","funder_id":"https://openalex.org/F4320320300","funder_display_name":"European Commission"}],"funders":[{"id":"https://openalex.org/F4320320300","display_name":"European Commission","ror":"https://ror.org/00k4n6c32"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":48,"referenced_works":["https://openalex.org/W79315950","https://openalex.org/W109189503","https://openalex.org/W628438000","https://openalex.org/W1506806321","https://openalex.org/W1507028917","https://openalex.org/W1522547150","https://openalex.org/W1560724230","https://openalex.org/W1651266332","https://openalex.org/W1663973292","https://openalex.org/W1986534176","https://openalex.org/W2000687279","https://openalex.org/W2020999234","https://openalex.org/W2039284087","https://openalex.org/W2043665634","https://openalex.org/W2049633694","https://openalex.org/W2101309634","https://openalex.org/W2104269704","https://openalex.org/W2106092565","https://openalex.org/W2106777458","https://openalex.org/W2110764329","https://openalex.org/W2113137767","https://openalex.org/W2114220616","https://openalex.org/W2115305054","https://openalex.org/W2119392375","https://openalex.org/W2122301197","https://openalex.org/W2127802986","https://openalex.org/W2131697388","https://openalex.org/W2142012908","https://openalex.org/W2143516773","https://openalex.org/W2148603752","https://openalex.org/W2150045166","https://openalex.org/W2150579376","https://openalex.org/W2153409933","https://openalex.org/W2155342973","https://openalex.org/W2163614729","https://openalex.org/W2167503608","https://openalex.org/W2478493250","https://openalex.org/W3129711340","https://openalex.org/W3133917342","https://openalex.org/W3144619878","https://openalex.org/W4200526660","https://openalex.org/W4230946174","https://openalex.org/W4232632925","https://openalex.org/W4253515568","https://openalex.org/W4254721730","https://openalex.org/W4320339642","https://openalex.org/W6604418364","https://openalex.org/W6683915439"],"related_works":["https://openalex.org/W2746742660","https://openalex.org/W2082586825","https://openalex.org/W2561005839","https://openalex.org/W2138540356","https://openalex.org/W2119473622","https://openalex.org/W2474469778","https://openalex.org/W2944460852","https://openalex.org/W2171464537","https://openalex.org/W1529476013","https://openalex.org/W2165780522"],"abstract_inverted_index":{"This":[0],"paper":[1],"presents":[2],"a":[3,49,60,84,112,131,217,229],"new":[4],"semisupervised":[5,30],"segmentation":[6,118,133],"algorithm,":[7],"suited":[8],"to":[9,125,228],"high-dimensional":[10],"data,":[11],"of":[12,32,45,99,109,163,176,216,243],"which":[13,41,116,121,232],"remotely":[14],"sensed":[15],"hyperspectral":[16,151,251],"image":[17,44,108,152,252],"data":[18,153],"sets":[19,154],"are":[20,68,77,92,194,238],"an":[21,43],"example.":[22],"The":[23,64,104,129],"algorithm":[24],"implements":[25],"two":[26],"main":[27],"steps:":[28],"1)":[29],"learning":[31],"the":[33,54,75,97,100,107,126,137,157,164,174,186,206,214,223,234,241],"posterior":[34,50,65],"class":[35,46,56,66,102,236],"distributions":[36,57,67],"followed":[37],"by":[38,136,156,201],"2)":[39],"segmentation,":[40],"infers":[42],"labels":[47,110,123],"from":[48],"distribution":[51],"built":[52],"on":[53,59,96,106],"learned":[55,78,235],"and":[58,149,167,180,247],"Markov":[61],"random":[62],"field.":[63],"modeled":[69],"using":[70,79,147],"multinomial":[71],"logistic":[72,114],"regression,":[73],"where":[74],"regressors":[76],"both":[80],"labeled":[81],"and,":[82],"through":[83],"graph-based":[85],"technique,":[86],"unlabeled":[87,90],"samples.":[88],"Such":[89],"samples":[91],"actively":[93],"selected":[94],"based":[95],"entropy":[98],"corresponding":[101],"label.":[103],"prior":[105,219],"is":[111,134],"multilevel":[113],"model,":[115],"enforces":[117],"results":[119,210,225],"in":[120,231,250],"neighboring":[122],"belong":[124],"same":[127],"class.":[128],"maximum":[130],"posteriori":[132],"computed":[135],"\u03b1-expansion":[138],"min-cut-based":[139],"integer":[140],"optimization":[141],"algorithm.":[142],"Our":[143,209],"experimental":[144],"results,":[145],"conducted":[146],"synthetic":[148],"real":[150],"collected":[155],"Airborne":[158],"Visible/Infrared":[159],"Imaging":[160],"Spectrometer":[161],"system":[162],"National":[165],"Aeronautics":[166],"Space":[168],"Administration":[169],"Jet":[170],"Propulsion":[171],"Laboratory":[172],"over":[173],"regions":[175],"Indian":[177],"Pines,":[178],"IN,":[179],"Salinas":[181],"Valley,":[182],"CA,":[183],"reveal":[184],"that":[185,193,213],"proposed":[187],"approach":[188],"can":[189,220],"provide":[190],"classification":[191],"accuracies":[192],"similar":[195],"or":[196],"higher":[197],"than":[198],"those":[199],"achieved":[200],"other":[202],"supervised":[203],"methods":[204],"for":[205],"considered":[207],"scenes.":[208],"also":[211],"indicate":[212],"use":[215],"spatial":[218,246],"greatly":[221],"improve":[222],"final":[224],"with":[226],"respect":[227],"case":[230],"only":[233],"densities":[237],"considered,":[239],"confirming":[240],"importance":[242],"jointly":[244],"considering":[245],"spectral":[248],"information":[249],"segmentation.":[253]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":31},{"year":2024,"cited_by_count":37},{"year":2023,"cited_by_count":36},{"year":2022,"cited_by_count":43},{"year":2021,"cited_by_count":50},{"year":2020,"cited_by_count":63},{"year":2019,"cited_by_count":42},{"year":2018,"cited_by_count":46},{"year":2017,"cited_by_count":57},{"year":2016,"cited_by_count":40},{"year":2015,"cited_by_count":38},{"year":2014,"cited_by_count":28},{"year":2013,"cited_by_count":23},{"year":2012,"cited_by_count":29}],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2025-10-10T00:00:00"}
