{"id":"https://openalex.org/W2045161375","doi":"https://doi.org/10.1109/tgrs.2012.2186582","title":"Multiscale Classification of Remote Sensing Images","display_name":"Multiscale Classification of Remote Sensing Images","publication_year":2012,"publication_date":"2012-03-19","ids":{"openalex":"https://openalex.org/W2045161375","doi":"https://doi.org/10.1109/tgrs.2012.2186582","mag":"2045161375"},"language":"en","primary_location":{"id":"doi:10.1109/tgrs.2012.2186582","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2012.2186582","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":null,"display_name":"Jefersson Alex dos Santos","orcid":null},"institutions":[{"id":"https://openalex.org/I181391015","display_name":"Universidade Estadual de Campinas (UNICAMP)","ror":"https://ror.org/04wffgt70","country_code":"BR","type":"education","lineage":["https://openalex.org/I181391015"]},{"id":"https://openalex.org/I4210142324","display_name":"CY Cergy Paris Universit\u00e9","ror":"https://ror.org/043htjv09","country_code":"FR","type":"education","lineage":["https://openalex.org/I4210142324"]},{"id":"https://openalex.org/I4210148552","display_name":"Equipes Traitement de l'Information et Syst\u00e8mes","ror":"https://ror.org/0592ata02","country_code":"FR","type":"facility","lineage":["https://openalex.org/I1294671590","https://openalex.org/I1294671590","https://openalex.org/I4210142324","https://openalex.org/I4210148552","https://openalex.org/I4210159245","https://openalex.org/I86175216"]}],"countries":["BR","FR"],"is_corresponding":true,"raw_author_name":"Jefersson Alex dos Santos","raw_affiliation_strings":["ETIS, University of Cergy-Pontoise, Cergy-Pontoise, France","Institute of Computing, University of Campinas, Campinas, SP, Brazil"],"affiliations":[{"raw_affiliation_string":"ETIS, University of Cergy-Pontoise, Cergy-Pontoise, France","institution_ids":["https://openalex.org/I4210148552","https://openalex.org/I4210142324"]},{"raw_affiliation_string":"Institute of Computing, University of Campinas, Campinas, SP, Brazil","institution_ids":["https://openalex.org/I181391015"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079925915","display_name":"Philippe-Henri Gosselin","orcid":"https://orcid.org/0000-0002-0973-4030"},"institutions":[{"id":"https://openalex.org/I4210142324","display_name":"CY Cergy Paris Universit\u00e9","ror":"https://ror.org/043htjv09","country_code":"FR","type":"education","lineage":["https://openalex.org/I4210142324"]},{"id":"https://openalex.org/I4210148552","display_name":"Equipes Traitement de l'Information et Syst\u00e8mes","ror":"https://ror.org/0592ata02","country_code":"FR","type":"facility","lineage":["https://openalex.org/I1294671590","https://openalex.org/I1294671590","https://openalex.org/I4210142324","https://openalex.org/I4210148552","https://openalex.org/I4210159245","https://openalex.org/I86175216"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Philippe-Henri Gosselin","raw_affiliation_strings":["ETIS, University of Cergy-Pontoise, Cergy-Pontoise, France"],"affiliations":[{"raw_affiliation_string":"ETIS, University of Cergy-Pontoise, Cergy-Pontoise, France","institution_ids":["https://openalex.org/I4210148552","https://openalex.org/I4210142324"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074950150","display_name":"Sylvie Philipp\u2010Foliguet","orcid":null},"institutions":[{"id":"https://openalex.org/I4210142324","display_name":"CY Cergy Paris Universit\u00e9","ror":"https://ror.org/043htjv09","country_code":"FR","type":"education","lineage":["https://openalex.org/I4210142324"]},{"id":"https://openalex.org/I4210148552","display_name":"Equipes Traitement de l'Information et Syst\u00e8mes","ror":"https://ror.org/0592ata02","country_code":"FR","type":"facility","lineage":["https://openalex.org/I1294671590","https://openalex.org/I1294671590","https://openalex.org/I4210142324","https://openalex.org/I4210148552","https://openalex.org/I4210159245","https://openalex.org/I86175216"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Sylvie Philipp-Foliguet","raw_affiliation_strings":["ETIS, University of Cergy-Pontoise, Cergy-Pontoise, France"],"affiliations":[{"raw_affiliation_string":"ETIS, University of Cergy-Pontoise, Cergy-Pontoise, France","institution_ids":["https://openalex.org/I4210148552","https://openalex.org/I4210142324"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087058987","display_name":"Ricardo da Silva Torres","orcid":"https://orcid.org/0000-0001-9772-263X"},"institutions":[{"id":"https://openalex.org/I181391015","display_name":"Universidade Estadual de Campinas (UNICAMP)","ror":"https://ror.org/04wffgt70","country_code":"BR","type":"education","lineage":["https://openalex.org/I181391015"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Ricardo da S. Torres","raw_affiliation_strings":["Institute of Computing, University of Campinas, Campinas, SP, Brazil"],"affiliations":[{"raw_affiliation_string":"Institute of Computing, University of Campinas, Campinas, SP, Brazil","institution_ids":["https://openalex.org/I181391015"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5106433328","display_name":"Alexandre Xavier Falao","orcid":null},"institutions":[{"id":"https://openalex.org/I181391015","display_name":"Universidade Estadual de Campinas (UNICAMP)","ror":"https://ror.org/04wffgt70","country_code":"BR","type":"education","lineage":["https://openalex.org/I181391015"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Alexandre Xavier Falao","raw_affiliation_strings":["Universidade Estadual de Campinas, Campinas, SP, BR"],"affiliations":[{"raw_affiliation_string":"Universidade Estadual de Campinas, Campinas, SP, BR","institution_ids":["https://openalex.org/I181391015"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I181391015","https://openalex.org/I4210142324","https://openalex.org/I4210148552"],"apc_list":null,"apc_paid":null,"fwci":16.1379,"has_fulltext":false,"cited_by_count":71,"citation_normalized_percentile":{"value":0.99220809,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"50","issue":"10","first_page":"3764","last_page":"3775"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9998000264167786,"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":0.9998000264167786,"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":0.9968000054359436,"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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9941999912261963,"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/support-vector-machine","display_name":"Support vector machine","score":0.7337740659713745},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7308700680732727},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.690706193447113},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6472431421279907},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.6396340131759644},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5944806933403015},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5553704500198364},{"id":"https://openalex.org/keywords/thematic-map","display_name":"Thematic map","score":0.5166500210762024},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.5090231895446777},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.48758047819137573},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.43021467328071594},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.4150474965572357},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4062908887863159},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3254581689834595},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.18578267097473145},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.15859052538871765},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.08910131454467773},{"id":"https://openalex.org/keywords/cartography","display_name":"Cartography","score":0.065614253282547}],"concepts":[{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.7337740659713745},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7308700680732727},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.690706193447113},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6472431421279907},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.6396340131759644},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5944806933403015},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5553704500198364},{"id":"https://openalex.org/C93692415","wikidata":"https://www.wikidata.org/wiki/Q1502030","display_name":"Thematic map","level":2,"score":0.5166500210762024},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.5090231895446777},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.48758047819137573},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.43021467328071594},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.4150474965572357},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4062908887863159},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3254581689834595},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.18578267097473145},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.15859052538871765},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.08910131454467773},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.065614253282547},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tgrs.2012.2186582","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2012.2186582","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"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6200000047683716,"id":"https://metadata.un.org/sdg/15","display_name":"Life in Land"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":46,"referenced_works":["https://openalex.org/W48298789","https://openalex.org/W129620840","https://openalex.org/W258213556","https://openalex.org/W1492603963","https://openalex.org/W1494131642","https://openalex.org/W1540371141","https://openalex.org/W1575496704","https://openalex.org/W1917380066","https://openalex.org/W1966178163","https://openalex.org/W1973724465","https://openalex.org/W1977023447","https://openalex.org/W1984792953","https://openalex.org/W1993756236","https://openalex.org/W2000179198","https://openalex.org/W2002358109","https://openalex.org/W2003904948","https://openalex.org/W2032729760","https://openalex.org/W2033769372","https://openalex.org/W2039388884","https://openalex.org/W2048929443","https://openalex.org/W2061240006","https://openalex.org/W2063907334","https://openalex.org/W2066015174","https://openalex.org/W2068885053","https://openalex.org/W2081894181","https://openalex.org/W2101051003","https://openalex.org/W2102841720","https://openalex.org/W2105568170","https://openalex.org/W2114487471","https://openalex.org/W2118513112","https://openalex.org/W2119879130","https://openalex.org/W2130121312","https://openalex.org/W2131884039","https://openalex.org/W2133744276","https://openalex.org/W2137891718","https://openalex.org/W2146715065","https://openalex.org/W2151832941","https://openalex.org/W2163763714","https://openalex.org/W2164598857","https://openalex.org/W2164777277","https://openalex.org/W2166424889","https://openalex.org/W2167705549","https://openalex.org/W2168809519","https://openalex.org/W2203777347","https://openalex.org/W2914885528","https://openalex.org/W6632223008"],"related_works":["https://openalex.org/W2291311298","https://openalex.org/W2125652721","https://openalex.org/W1540371141","https://openalex.org/W1549363203","https://openalex.org/W1529840045","https://openalex.org/W4244036394","https://openalex.org/W1842879116","https://openalex.org/W2135107501","https://openalex.org/W2124490386","https://openalex.org/W1822895636"],"abstract_inverted_index":{"A":[0],"huge":[1],"effort":[2],"has":[3],"been":[4],"applied":[5],"in":[6,124],"image":[7,34,117],"classification":[8,35,39],"to":[9,15,46,53,65,69,143,158,163],"create":[10],"high-quality":[11],"thematic":[12],"maps":[13],"and":[14,86,93,104,137,162],"establish":[16],"precise":[17],"inventories":[18],"about":[19],"land":[20],"cover":[21],"use.":[22],"The":[23,110,184],"peculiarities":[24],"of":[25,50,60,83,118,139],"remote":[26],"sensing":[27],"images":[28],"(RSIs)":[29],"combined":[30],"with":[31,173,179],"the":[32,58,84,135,170,188,192],"traditional":[33],"challenges":[36],"made":[37],"RSI":[38],"a":[40,48,115,144,165,174],"hard":[41],"task.":[42],"Our":[43],"aim":[44],"is":[45,64,78,156],"propose":[47],"kind":[49],"boost-classifier":[51],"adapted":[52],"multiscale":[54,154],"segmentation.":[55],"We":[56,90,121,148,168],"use":[57],"paradigm":[59],"boosting,":[61],"whose":[62],"principle":[63],"combine":[66],"weak":[67,76,95],"classifiers":[68,96],"build":[70],"an":[71],"efficient":[72],"global":[73],"one.":[74],"Each":[75],"classifier":[77],"trained":[79],"for":[80],"one":[81,87],"level":[82],"segmentation":[85],"region":[88,105],"descriptor.":[89],"have":[91,122,149],"proposed":[92,171,189],"tested":[94],"based":[97,130,176],"on":[98,114,131,177],"linear":[99],"support":[100],"vector":[101],"machines":[102],"(SVM)":[103],"distances":[106],"provided":[107],"by":[108],"descriptors.":[109],"experiments":[111],"were":[112],"performed":[113],"large":[116],"coffee":[119],"plantations.":[120],"shown":[123,151],"this":[125],"paper":[126],"that":[127,152,187],"our":[128],"approach":[129],"boosting":[132],"can":[133],"detect":[134],"scale":[136],"set":[138],"features":[140],"best":[141],"suited":[142],"particular":[145],"training":[146,160],"set.":[147],"also":[150],"hierarchical":[153],"analysis":[155],"able":[157],"reduce":[159],"time":[161],"produce":[164],"stronger":[166],"classifier.":[167],"compare":[169],"methods":[172,190],"baseline":[175],"SVM":[178],"radial":[180],"basis":[181],"function":[182],"kernel.":[183],"results":[185],"show":[186],"outperform":[191],"baseline.":[193]},"counts_by_year":[{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":5},{"year":2019,"cited_by_count":4},{"year":2018,"cited_by_count":4},{"year":2017,"cited_by_count":12},{"year":2016,"cited_by_count":8},{"year":2015,"cited_by_count":7},{"year":2014,"cited_by_count":12},{"year":2013,"cited_by_count":7},{"year":2012,"cited_by_count":5}],"updated_date":"2026-04-16T08:26:57.006410","created_date":"2025-10-10T00:00:00"}