{"id":"https://openalex.org/W2145649962","doi":"https://doi.org/10.1109/tgrs.2011.2162246","title":"A Spatial\u2013Contextual Support Vector Machine for Remotely Sensed Image Classification","display_name":"A Spatial\u2013Contextual Support Vector Machine for Remotely Sensed Image Classification","publication_year":2011,"publication_date":"2011-08-31","ids":{"openalex":"https://openalex.org/W2145649962","doi":"https://doi.org/10.1109/tgrs.2011.2162246","mag":"2145649962"},"language":"en","primary_location":{"id":"doi:10.1109/tgrs.2011.2162246","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2011.2162246","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/A5006434175","display_name":"Cheng\u2010Hsuan Li","orcid":"https://orcid.org/0000-0001-5059-8256"},"institutions":[{"id":"https://openalex.org/I148366613","display_name":"National Yang Ming Chiao Tung University","ror":"https://ror.org/00se2k293","country_code":"TW","type":"education","lineage":["https://openalex.org/I148366613"]},{"id":"https://openalex.org/I34574549","display_name":"National Taichung University of Education","ror":"https://ror.org/006tnfe02","country_code":"TW","type":"education","lineage":["https://openalex.org/I34574549"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Cheng-Hsuan Li","raw_affiliation_strings":["Graduate Institute of Educational Measurement and Statistics, National Taichung University of Education, Taichung, Taiwan","Institute of Electrical Control Engineering, National Chiao Tung University, Hsinchu, Taiwan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Graduate Institute of Educational Measurement and Statistics, National Taichung University of Education, Taichung, Taiwan","institution_ids":["https://openalex.org/I34574549"]},{"raw_affiliation_string":"Institute of Electrical Control Engineering, National Chiao Tung University, Hsinchu, Taiwan","institution_ids":["https://openalex.org/I148366613"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054302891","display_name":"Bor\u2010Chen Kuo","orcid":"https://orcid.org/0000-0003-1741-2450"},"institutions":[{"id":"https://openalex.org/I34574549","display_name":"National Taichung University of Education","ror":"https://ror.org/006tnfe02","country_code":"TW","type":"education","lineage":["https://openalex.org/I34574549"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Bor-Chen Kuo","raw_affiliation_strings":["Graduate Institute of Educational Measurement and Statistics, National Taichung University of Education, Taichung, Taiwan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Graduate Institute of Educational Measurement and Statistics, National Taichung University of Education, Taichung, Taiwan","institution_ids":["https://openalex.org/I34574549"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058936239","display_name":"Chin\u2010Teng Lin","orcid":"https://orcid.org/0000-0001-8371-8197"},"institutions":[{"id":"https://openalex.org/I148366613","display_name":"National Yang Ming Chiao Tung University","ror":"https://ror.org/00se2k293","country_code":"TW","type":"education","lineage":["https://openalex.org/I148366613"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Chin-Teng Lin","raw_affiliation_strings":["Brain Research Center and the Institute of Electrical Control Engineering, National Chiao Tung University, Hsinchu, Taiwan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Brain Research Center and the Institute of Electrical Control Engineering, National Chiao Tung University, Hsinchu, Taiwan","institution_ids":["https://openalex.org/I148366613"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102836324","display_name":"Chih-Sheng Huang","orcid":"https://orcid.org/0000-0002-8836-4970"},"institutions":[{"id":"https://openalex.org/I148366613","display_name":"National Yang Ming Chiao Tung University","ror":"https://ror.org/00se2k293","country_code":"TW","type":"education","lineage":["https://openalex.org/I148366613"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Chih-Sheng Huang","raw_affiliation_strings":["Brain Research Center and the Institute of Electrical Control Engineering, National Chiao Tung University, Hsinchu, Taiwan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Brain Research Center and the Institute of Electrical Control Engineering, National Chiao Tung University, Hsinchu, Taiwan","institution_ids":["https://openalex.org/I148366613"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":17.7636,"has_fulltext":false,"cited_by_count":105,"citation_normalized_percentile":{"value":0.99302012,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":100},"biblio":{"volume":"50","issue":"3","first_page":"784","last_page":"799"},"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":0.9919999837875366,"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":0.939300000667572,"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/support-vector-machine","display_name":"Support vector machine","score":0.7812662720680237},{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.778132438659668},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7616081237792969},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7366790771484375},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6700126528739929},{"id":"https://openalex.org/keywords/spatial-contextual-awareness","display_name":"Spatial contextual awareness","score":0.6455420255661011},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.5977445244789124},{"id":"https://openalex.org/keywords/naive-bayes-classifier","display_name":"Naive Bayes classifier","score":0.5408023595809937},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5152412056922913},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.5025894641876221},{"id":"https://openalex.org/keywords/spatial-analysis","display_name":"Spatial analysis","score":0.49417927861213684},{"id":"https://openalex.org/keywords/k-nearest-neighbors-algorithm","display_name":"k-nearest neighbors algorithm","score":0.4878368675708771},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.48483335971832275},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.36312246322631836},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.27084338665008545},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.2316015064716339},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.09514293074607849}],"concepts":[{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.7812662720680237},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.778132438659668},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7616081237792969},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7366790771484375},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6700126528739929},{"id":"https://openalex.org/C64754055","wikidata":"https://www.wikidata.org/wiki/Q7574053","display_name":"Spatial contextual awareness","level":2,"score":0.6455420255661011},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.5977445244789124},{"id":"https://openalex.org/C52001869","wikidata":"https://www.wikidata.org/wiki/Q812530","display_name":"Naive Bayes classifier","level":3,"score":0.5408023595809937},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5152412056922913},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.5025894641876221},{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.49417927861213684},{"id":"https://openalex.org/C113238511","wikidata":"https://www.wikidata.org/wiki/Q1071612","display_name":"k-nearest neighbors algorithm","level":2,"score":0.4878368675708771},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.48483335971832275},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.36312246322631836},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.27084338665008545},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2316015064716339},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.09514293074607849}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/tgrs.2011.2162246","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2011.2162246","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:10.1.1.974.2047","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.974.2047","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"https://ir.nctu.edu.tw:443/bitstream/11536/15589/1/000300724300010.pdf","raw_type":"text"},{"id":"pmh:oai:opus.lib.uts.edu.au:10453/116328","is_oa":false,"landing_page_url":"http://hdl.handle.net/10453/116328","pdf_url":null,"source":{"id":"https://openalex.org/S4306401357","display_name":"UTS ePRESS (University of Technology Sydney)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I114017466","host_organization_name":"University of Technology Sydney","host_organization_lineage":["https://openalex.org/I114017466"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Journal Article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","score":0.8100000023841858,"id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":52,"referenced_works":["https://openalex.org/W34176136","https://openalex.org/W206338564","https://openalex.org/W324611197","https://openalex.org/W628438000","https://openalex.org/W1510073064","https://openalex.org/W1567885833","https://openalex.org/W1770825568","https://openalex.org/W1975749078","https://openalex.org/W2041797434","https://openalex.org/W2043665634","https://openalex.org/W2049633694","https://openalex.org/W2087347434","https://openalex.org/W2095595743","https://openalex.org/W2096634748","https://openalex.org/W2098057602","https://openalex.org/W2098511344","https://openalex.org/W2099129687","https://openalex.org/W2104269704","https://openalex.org/W2107966405","https://openalex.org/W2108651654","https://openalex.org/W2113464037","https://openalex.org/W2114819256","https://openalex.org/W2115451191","https://openalex.org/W2118796925","https://openalex.org/W2129652905","https://openalex.org/W2135346934","https://openalex.org/W2136251662","https://openalex.org/W2140934966","https://openalex.org/W2143506329","https://openalex.org/W2146952738","https://openalex.org/W2146987667","https://openalex.org/W2151194815","https://openalex.org/W2152523366","https://openalex.org/W2153409933","https://openalex.org/W2154952114","https://openalex.org/W2156316030","https://openalex.org/W2156909104","https://openalex.org/W2163114261","https://openalex.org/W2164330327","https://openalex.org/W2164437025","https://openalex.org/W2167685974","https://openalex.org/W2168228682","https://openalex.org/W2169201488","https://openalex.org/W2169650425","https://openalex.org/W2172000360","https://openalex.org/W2308181013","https://openalex.org/W2478493250","https://openalex.org/W4230674625","https://openalex.org/W4285719527","https://openalex.org/W4320339642","https://openalex.org/W6681132568","https://openalex.org/W6681582014"],"related_works":["https://openalex.org/W2394466068","https://openalex.org/W1987683558","https://openalex.org/W2726838704","https://openalex.org/W4220802396","https://openalex.org/W2393473353","https://openalex.org/W2373790322","https://openalex.org/W2171665309","https://openalex.org/W4387382336","https://openalex.org/W1983936910","https://openalex.org/W3217780232"],"abstract_inverted_index":{"Recent":[0],"studies":[1],"show":[2,167],"that":[3,8,23,119,168],"hyperspectral":[4,54,178,198],"image":[5,55,199],"classification":[6,174,194],"techniques":[7],"use":[9,24],"both":[10],"spectral":[11,26,113],"and":[12,19,38,46,115,161,185,217],"spatial":[13,72,84],"information":[14,73,85],"are":[15],"more":[16],"suitable,":[17],"effective,":[18],"robust":[20],"than":[21],"those":[22],"only":[25],"information.":[27],"Using":[28],"a":[29,41,144,148,153],"spatial-contextual":[30,51],"term,":[31],"this":[32,134],"study":[33,135],"modifies":[34],"the":[35,63,71,75,83,87,92,96,127,131,137,169,186,197,201,218],"decision":[36],"function":[37],"constraints":[39],"of":[40,50,65,107,129,139,196,200,221],"support":[42],"vector":[43],"machine":[44,81],"(SVM)":[45],"proposes":[47],"two":[48],"kinds":[49],"SVMs":[52],"for":[53],"classification.":[56],"One":[57],"machine,":[58],"which":[59],"is":[60,101,208,213,224],"based":[61,157],"on":[62,158,176],"concept":[64],"Markov":[66],"random":[67],"fields":[68],"(MRFs),":[69],"uses":[70,82],"in":[74,86,95,133],"original":[76],"space":[77,89],"(SCSVM).":[78],"The":[79,99,192,210],"other":[80,140],"feature":[88,97],"(SCSVMF),":[90],"i.e.,":[91],"nearest":[93,162],"neighbors":[94],"space.":[98],"SCSVM":[100],"better":[102],"able":[103],"to":[104,215,226],"classify":[105],"pixels":[106],"different":[108],"class":[109,223],"labels":[110],"with":[111,117,205],"similar":[112],"values":[114],"deal":[116],"data":[118,190,203],"have":[120],"no":[121],"clear":[122],"numerical":[123],"interpretation.":[124],"To":[125],"evaluate":[126],"effectiveness":[128],"SCSVM,":[130],"experiments":[132],"compare":[136],"performances":[138],"classifiers:":[141],"an":[142],"SVM,":[143,147],"context-sensitive":[145],"semisupervised":[146],"maximum":[149],"likelihood":[150],"(ML)":[151],"classifier,":[152],"Bayesian":[154],"contextual":[155],"classifier":[156],"MRFs":[159],"(ML_MRF),":[160],"neighbor":[163],"classifier.":[164],"Experimental":[165],"results":[166],"proposed":[170],"method":[171],"achieves":[172],"good":[173],"performance":[175],"famous":[177],"images":[179],"(the":[180],"Indian":[181],"Pine":[182],"site":[183],"(IPS)":[184],"Washington,":[187],"DC":[188],"mall":[189],"sets).":[191],"overall":[193],"accuracy":[195,212,220],"IPS":[202],"set":[204],"16":[206],"classes":[207],"95.5%.":[209],"kappa":[211],"up":[214,225],"94.9%,":[216],"average":[219],"each":[222],"94.2%.":[227]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":6},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":5},{"year":2018,"cited_by_count":8},{"year":2017,"cited_by_count":17},{"year":2016,"cited_by_count":6},{"year":2015,"cited_by_count":14},{"year":2014,"cited_by_count":11},{"year":2013,"cited_by_count":19},{"year":2012,"cited_by_count":5}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
