{"id":"https://openalex.org/W2541076639","doi":"https://doi.org/10.1109/whispers.2012.6874224","title":"Semi-supervised discriminative random field for hyperspectral image classification","display_name":"Semi-supervised discriminative random field for hyperspectral image classification","publication_year":2012,"publication_date":"2012-06-01","ids":{"openalex":"https://openalex.org/W2541076639","doi":"https://doi.org/10.1109/whispers.2012.6874224","mag":"2541076639"},"language":"en","primary_location":{"id":"doi:10.1109/whispers.2012.6874224","is_oa":false,"landing_page_url":"https://doi.org/10.1109/whispers.2012.6874224","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":"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/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":true,"raw_author_name":"Jun Li","raw_affiliation_strings":["Department of Technology of Computers and Communications, University of Extremadura, Caceres, Spain"],"affiliations":[{"raw_affiliation_string":"Department of Technology of Computers and Communications, University of Extremadura, Caceres, Spain","institution_ids":["https://openalex.org/I80606768"]}]},{"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"]}],"countries":["PT"],"is_corresponding":false,"raw_author_name":"Jose M. Bioucas-Dias","raw_affiliation_strings":["Instituto de Telecomunica\u00e7\u00f5es, TULisbon, Lisbon, Portugal"],"affiliations":[{"raw_affiliation_string":"Instituto de Telecomunica\u00e7\u00f5es, TULisbon, Lisbon, Portugal","institution_ids":["https://openalex.org/I4210120471"]}]},{"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"],"affiliations":[{"raw_affiliation_string":"Department of Technology of Computers and Communications, University of Extremadura, Caceres, 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/I80606768"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.45940096,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"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":1.0,"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":1.0,"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":0.9876999855041504,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.963699996471405,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.9716733694076538},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.8487482070922852},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7030798196792603},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.698453962802887},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6313714981079102},{"id":"https://openalex.org/keywords/imaging-spectrometer","display_name":"Imaging spectrometer","score":0.5830587148666382},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5678434371948242},{"id":"https://openalex.org/keywords/curse-of-dimensionality","display_name":"Curse of dimensionality","score":0.5668892860412598},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.4820319414138794},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.4214964807033539},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.3430538773536682},{"id":"https://openalex.org/keywords/spectrometer","display_name":"Spectrometer","score":0.13158458471298218}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.9716733694076538},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.8487482070922852},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7030798196792603},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.698453962802887},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6313714981079102},{"id":"https://openalex.org/C183852935","wikidata":"https://www.wikidata.org/wiki/Q6002848","display_name":"Imaging spectrometer","level":3,"score":0.5830587148666382},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5678434371948242},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.5668892860412598},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.4820319414138794},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.4214964807033539},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3430538773536682},{"id":"https://openalex.org/C33390570","wikidata":"https://www.wikidata.org/wiki/Q188463","display_name":"Spectrometer","level":2,"score":0.13158458471298218},{"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/whispers.2012.6874224","is_oa":false,"landing_page_url":"https://doi.org/10.1109/whispers.2012.6874224","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":[{"score":0.7599999904632568,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":32,"referenced_works":["https://openalex.org/W1522547150","https://openalex.org/W1585385982","https://openalex.org/W1938542669","https://openalex.org/W1964803612","https://openalex.org/W1986534176","https://openalex.org/W2019599312","https://openalex.org/W2048679005","https://openalex.org/W2079057609","https://openalex.org/W2100705753","https://openalex.org/W2101210369","https://openalex.org/W2101365302","https://openalex.org/W2104269704","https://openalex.org/W2105386417","https://openalex.org/W2107008379","https://openalex.org/W2113464037","https://openalex.org/W2121169814","https://openalex.org/W2136504847","https://openalex.org/W2140676093","https://openalex.org/W2142012908","https://openalex.org/W2147880316","https://openalex.org/W2148603752","https://openalex.org/W2150579376","https://openalex.org/W2153409933","https://openalex.org/W2158485347","https://openalex.org/W2169650425","https://openalex.org/W2478493250","https://openalex.org/W6635184699","https://openalex.org/W6640522805","https://openalex.org/W6676132248","https://openalex.org/W6680140577","https://openalex.org/W6682082992","https://openalex.org/W6683465899"],"related_works":["https://openalex.org/W2746742660","https://openalex.org/W2132083814","https://openalex.org/W2082586825","https://openalex.org/W2119473622","https://openalex.org/W2561005839","https://openalex.org/W2474469778","https://openalex.org/W2944460852","https://openalex.org/W2138540356","https://openalex.org/W2171464537","https://openalex.org/W1529476013"],"abstract_inverted_index":{"Remotely":[0],"sensed":[1],"hyperspectral":[2,30,95,140,150],"imaging":[3,17],"allows":[4],"for":[5,138],"the":[6,10,13,39,42,46,49,61,86,106,162,176,198,204,207,211],"detailed":[7],"analysis":[8,58],"of":[9,12,26,45,52,63,88,101,118,186,195],"surface":[11],"Earth":[14],"using":[15,148,153],"advanced":[16],"instruments":[18],"which":[19],"can":[20,74,111],"produce":[21],"high-dimensional":[22],"images":[23],"with":[24,190,206],"hundreds":[25],"spectral":[27],"bands.":[28],"Supervised":[29],"image":[31,96,141],"classification":[32],"is":[33,66,104,146],"a":[34,78,115,130,149],"difficult":[35],"task":[36],"due":[37],"to":[38],"unbalance":[40],"between":[41],"high":[43],"dimensionality":[44],"data":[47],"and":[48,70,214],"limited":[50],"availability":[51],"labeled":[53,64,120],"training":[54,109],"samples":[55,65,73,110,121],"in":[56,77,94,184,188],"real":[57],"scenarios.":[59],"While":[60],"collection":[62],"generally":[67],"difficult,":[68],"expensive":[69],"time-consuming,":[71],"unlabeled":[72,174],"be":[75,112],"generated":[76],"much":[79],"easier":[80],"way.":[81],"This":[82],"observation":[83],"has":[84],"fostered":[85],"idea":[87],"adopting":[89],"semi-supervised":[90,132],"learning":[91],"(SSL)":[92],"techniques":[93,103],"classification.":[97,142],"The":[98,143,166],"main":[99],"assumption":[100],"such":[102],"that":[105],"new":[107,131],"(unlabeled)":[108],"obtained":[113,167],"from":[114],"(limited)":[116],"set":[117],"available":[119],"without":[122],"significant":[123],"effort/cost.":[124],"In":[125,193],"this":[126],"work,":[127],"we":[128],"propose":[129],"discriminative":[133],"random":[134],"field":[135],"(SSDRF)":[136],"technique":[137],"spectral-spatial":[139],"proposed":[144,177,199],"approach":[145],"validated":[147],"dataset":[151],"collected":[152],"NASA's":[154],"Airborne":[155],"Visible":[156],"Infra-Red":[157],"Imaging":[158],"Spectrometer":[159],"(AVIRIS)":[160],"over":[161],"Indian":[163],"Pines":[164],"region.":[165],"results":[168],"indicate":[169],"that,":[170],"by":[171],"automatically":[172],"generating":[173],"information,":[175],"SSDRF":[178,200],"algorithm":[179,201],"exhibits":[180],"very":[181,216],"good":[182],"performance":[183],"terms":[185,194],"accuracies":[187],"comparison":[189],"supervised":[191,212],"algorithms.":[192],"computational":[196],"cost,":[197],"self":[202],"learns":[203],"classifier":[205],"same":[208],"complexity":[209],"as":[210],"algorithm,":[213],"converges":[215],"efficiently.":[217]},"counts_by_year":[{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":2},{"year":2016,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}