{"id":"https://openalex.org/W2508217043","doi":"https://doi.org/10.1109/jstars.2016.2596040","title":"Incorporating Spectral Similarity Into Markov Chain Geostatistical Cosimulation for Reducing Smoothing Effect in Land Cover Postclassification","display_name":"Incorporating Spectral Similarity Into Markov Chain Geostatistical Cosimulation for Reducing Smoothing Effect in Land Cover Postclassification","publication_year":2016,"publication_date":"2016-08-15","ids":{"openalex":"https://openalex.org/W2508217043","doi":"https://doi.org/10.1109/jstars.2016.2596040","mag":"2508217043"},"language":"en","primary_location":{"id":"doi:10.1109/jstars.2016.2596040","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jstars.2016.2596040","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/A5021300873","display_name":"Weixing Zhang","orcid":"https://orcid.org/0000-0001-8253-3932"},"institutions":[{"id":"https://openalex.org/I140172145","display_name":"University of Connecticut","ror":"https://ror.org/02der9h97","country_code":"US","type":"education","lineage":["https://openalex.org/I140172145"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Weixing Zhang","raw_affiliation_strings":["Department of Geography and Center for Environmental Sciences and Engineering, University of Connecticut, Storrs, CT, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Geography and Center for Environmental Sciences and Engineering, University of Connecticut, Storrs, CT, USA","institution_ids":["https://openalex.org/I140172145"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100710213","display_name":"Weidong Li","orcid":"https://orcid.org/0000-0002-4558-3292"},"institutions":[{"id":"https://openalex.org/I140172145","display_name":"University of Connecticut","ror":"https://ror.org/02der9h97","country_code":"US","type":"education","lineage":["https://openalex.org/I140172145"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Weidong Li","raw_affiliation_strings":["Department of Geography and Center for Environmental Sciences and Engineering, University of Connecticut, Storrs, CT, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Geography and Center for Environmental Sciences and Engineering, University of Connecticut, Storrs, CT, USA","institution_ids":["https://openalex.org/I140172145"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017628382","display_name":"Chuanrong Zhang","orcid":"https://orcid.org/0000-0001-5249-141X"},"institutions":[{"id":"https://openalex.org/I140172145","display_name":"University of Connecticut","ror":"https://ror.org/02der9h97","country_code":"US","type":"education","lineage":["https://openalex.org/I140172145"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chuanrong Zhang","raw_affiliation_strings":["Department of Geography and Center for Environmental Sciences and Engineering, University of Connecticut, Storrs, CT, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Geography and Center for Environmental Sciences and Engineering, University of Connecticut, Storrs, CT, USA","institution_ids":["https://openalex.org/I140172145"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100686305","display_name":"Xiaojiang Li","orcid":"https://orcid.org/0000-0002-4208-1641"},"institutions":[{"id":"https://openalex.org/I140172145","display_name":"University of Connecticut","ror":"https://ror.org/02der9h97","country_code":"US","type":"education","lineage":["https://openalex.org/I140172145"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xiaojiang Li","raw_affiliation_strings":["Department of Geography and Center for Environmental Sciences and Engineering, University of Connecticut, Storrs, CT, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Geography and Center for Environmental Sciences and Engineering, University of Connecticut, Storrs, CT, USA","institution_ids":["https://openalex.org/I140172145"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1250,"currency":"USD","value_usd":1250},"apc_paid":null,"fwci":2.5049,"has_fulltext":false,"cited_by_count":16,"citation_normalized_percentile":{"value":0.88228978,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":"10","issue":"3","first_page":"1082","last_page":"1095"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10226","display_name":"Land Use and Ecosystem Services","score":0.9987000226974487,"subfield":{"id":"https://openalex.org/subfields/2306","display_name":"Global and Planetary Change"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10226","display_name":"Land Use and Ecosystem Services","score":0.9987000226974487,"subfield":{"id":"https://openalex.org/subfields/2306","display_name":"Global and Planetary Change"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10111","display_name":"Remote Sensing in Agriculture","score":0.9980999827384949,"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"}},{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9950000047683716,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/land-cover","display_name":"Land cover","score":0.707702100276947},{"id":"https://openalex.org/keywords/jaccard-index","display_name":"Jaccard index","score":0.6575708389282227},{"id":"https://openalex.org/keywords/smoothing","display_name":"Smoothing","score":0.6289929151535034},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6250765323638916},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.589587390422821},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.5377402901649475},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.5239786505699158},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5104783773422241},{"id":"https://openalex.org/keywords/markov-random-field","display_name":"Markov random field","score":0.4970147907733917},{"id":"https://openalex.org/keywords/markov-chain","display_name":"Markov chain","score":0.48288822174072266},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.4715937376022339},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.43356359004974365},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.4234880805015564},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3814288377761841},{"id":"https://openalex.org/keywords/land-use","display_name":"Land use","score":0.2834928631782532},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.22374454140663147},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.21306660771369934},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.16640841960906982},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.12281540036201477}],"concepts":[{"id":"https://openalex.org/C2780648208","wikidata":"https://www.wikidata.org/wiki/Q3001793","display_name":"Land cover","level":3,"score":0.707702100276947},{"id":"https://openalex.org/C203519979","wikidata":"https://www.wikidata.org/wiki/Q865360","display_name":"Jaccard index","level":3,"score":0.6575708389282227},{"id":"https://openalex.org/C3770464","wikidata":"https://www.wikidata.org/wiki/Q775963","display_name":"Smoothing","level":2,"score":0.6289929151535034},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6250765323638916},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.589587390422821},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.5377402901649475},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.5239786505699158},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5104783773422241},{"id":"https://openalex.org/C2778045648","wikidata":"https://www.wikidata.org/wiki/Q176827","display_name":"Markov random field","level":4,"score":0.4970147907733917},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.48288822174072266},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.4715937376022339},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.43356359004974365},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.4234880805015564},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3814288377761841},{"id":"https://openalex.org/C4792198","wikidata":"https://www.wikidata.org/wiki/Q1165944","display_name":"Land use","level":2,"score":0.2834928631782532},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.22374454140663147},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.21306660771369934},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.16640841960906982},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.12281540036201477},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C147176958","wikidata":"https://www.wikidata.org/wiki/Q77590","display_name":"Civil engineering","level":1,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/jstars.2016.2596040","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jstars.2016.2596040","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":[{"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11","score":0.699999988079071}],"awards":[{"id":"https://openalex.org/G8041360414","display_name":null,"funder_award_id":"1414108","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":53,"referenced_works":["https://openalex.org/W159883374","https://openalex.org/W1973749534","https://openalex.org/W1979797212","https://openalex.org/W1983019866","https://openalex.org/W1996587674","https://openalex.org/W1997319273","https://openalex.org/W1997443969","https://openalex.org/W2001068443","https://openalex.org/W2012111704","https://openalex.org/W2021303382","https://openalex.org/W2026745084","https://openalex.org/W2029951041","https://openalex.org/W2049793200","https://openalex.org/W2055702796","https://openalex.org/W2057826737","https://openalex.org/W2059545443","https://openalex.org/W2069820899","https://openalex.org/W2076656703","https://openalex.org/W2082732714","https://openalex.org/W2082819134","https://openalex.org/W2082874195","https://openalex.org/W2083620454","https://openalex.org/W2089685636","https://openalex.org/W2090116710","https://openalex.org/W2090303181","https://openalex.org/W2093321230","https://openalex.org/W2093628091","https://openalex.org/W2105325887","https://openalex.org/W2107966405","https://openalex.org/W2109742272","https://openalex.org/W2117741117","https://openalex.org/W2121517407","https://openalex.org/W2123460341","https://openalex.org/W2123800205","https://openalex.org/W2124706543","https://openalex.org/W2129031459","https://openalex.org/W2130269771","https://openalex.org/W2141926053","https://openalex.org/W2144841545","https://openalex.org/W2144925768","https://openalex.org/W2144961153","https://openalex.org/W2156220628","https://openalex.org/W2158660073","https://openalex.org/W2167798235","https://openalex.org/W2168809519","https://openalex.org/W2172000360","https://openalex.org/W2254479484","https://openalex.org/W2319224732","https://openalex.org/W2534557021","https://openalex.org/W3103590434","https://openalex.org/W4285719527","https://openalex.org/W6676261910","https://openalex.org/W6678799126"],"related_works":["https://openalex.org/W4254879869","https://openalex.org/W3022576529","https://openalex.org/W2628526247","https://openalex.org/W2596401011","https://openalex.org/W2913569734","https://openalex.org/W4401519790","https://openalex.org/W2702570413","https://openalex.org/W3127229356","https://openalex.org/W2901284887","https://openalex.org/W3120359844"],"abstract_inverted_index":{"Spatial":[0],"statistics":[1],"provides":[2],"useful":[3],"methods":[4],"for":[5,64,119],"incorporating":[6,212],"spatial":[7,99,110],"dependence":[8],"into":[9,43,215],"land":[10,18,65,134,189,207,216,232],"cover":[11,19,66,135,190,208,217,233],"classification.":[12],"However,":[13,179],"the":[14,44,85,183,225],"geometric":[15,74,196,228],"features":[16,197],"of":[17,35,166,206,230],"classes":[20],"are":[21],"difficult":[22],"to":[23,30,39,55,132,143,224],"be":[24],"captured":[25],"by":[26,169,192],"geostatistical":[27],"models":[28,171],"due":[29],"smoothing":[31],"effect.":[32],"The":[33],"objective":[34],"this":[36],"study":[37],"is":[38],"incorporate":[40],"spectral":[41,58,79,86,164,213],"similarity":[42,80,214],"Markov":[45],"chain":[46],"random":[47],"field":[48],"(MCRF)":[49],"cosimulation":[50,61],"(coMCRF)":[51],"model,":[52,63],"that":[53,69,160],"is,":[54],"propose":[56],"a":[57,92,103],"similarity-enhanced":[59],"MCRF":[60],"(SS-coMCRF)":[62],"postclassification":[67,70,218],"so":[68],"will":[71],"cause":[72],"less":[73],"loss.":[75],"Two":[76],"mutually":[77],"complementary":[78],"measures,":[81],"Jaccard":[82],"index":[83],"and":[84,106,125,139,200,203],"correlation":[87],"measure,":[88],"were":[89,117,130,141],"employed":[90],"as":[91],"constraining":[93],"factor":[94],"in":[95,176],"SS-coMCRF.":[96],"One":[97],"medium":[98],"resolution":[100,111],"scene":[101,112],"with":[102,113,157,181],"complex":[104],"landscape":[105,116],"one":[107],"very":[108],"high":[109],"an":[114],"urban":[115],"selected":[118],"case":[120],"studies.":[121],"Neural":[122],"network":[123],"classifier":[124,129],"support":[126],"vector":[127],"machine":[128],"used":[131,142],"conduct":[133],"preclassifications.":[136],"Both":[137],"coMCRF":[138],"SS-coMCRF":[140,184,220],"postprocess":[144],"preclassified":[145,158],"images":[146],"based":[147],"on":[148,162],"expert-interpreted":[149],"sample":[150],"datasets":[151],"from":[152],"multiple":[153],"data":[154],"sources.":[155],"Compared":[156],"results":[159],"depend":[161],"only":[163],"information":[165],"pixels,":[167],"postclassifications":[168],"both":[170],"achieved":[172],"similar":[173],"significant":[174],"improvements":[175],"overall":[177],"accuracy.":[178],"compared":[180],"coMCRF,":[182],"model":[185],"apparently":[186],"improved":[187],"postclassified":[188],"patterns":[191],"effectively":[193],"capturing":[194],"some":[195],"(e.g.,":[198],"boundaries":[199],"linear":[201],"stripes)":[202],"more":[204],"details":[205],"classes.":[209,234],"In":[210],"general,":[211],"through":[219],"may":[221],"contribute":[222],"significantly":[223],"\u201cshape\u201d":[226],"or":[227],"accuracy":[229],"classified":[231]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":8},{"year":2017,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
