{"id":"https://openalex.org/W2169500530","doi":"https://doi.org/10.1109/tgrs.2010.2041784","title":"Sensitivity of Support Vector Machines to Random Feature Selection in Classification of Hyperspectral Data","display_name":"Sensitivity of Support Vector Machines to Random Feature Selection in Classification of Hyperspectral Data","publication_year":2010,"publication_date":"2010-03-30","ids":{"openalex":"https://openalex.org/W2169500530","doi":"https://doi.org/10.1109/tgrs.2010.2041784","mag":"2169500530"},"language":"en","primary_location":{"id":"doi:10.1109/tgrs.2010.2041784","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2010.2041784","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/A5018040226","display_name":"Bj\u00f6rn Waske","orcid":"https://orcid.org/0000-0002-2586-3748"},"institutions":[{"id":"https://openalex.org/I135140700","display_name":"University of Bonn","ror":"https://ror.org/041nas322","country_code":"DE","type":"education","lineage":["https://openalex.org/I135140700"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Bj\u00f6rn Waske","raw_affiliation_strings":["Faculty of Agricultural, University of Bonn, Bonn, Germany"],"affiliations":[{"raw_affiliation_string":"Faculty of Agricultural, University of Bonn, Bonn, Germany","institution_ids":["https://openalex.org/I135140700"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015469539","display_name":"Sebastian van der Linden","orcid":"https://orcid.org/0000-0001-6576-8377"},"institutions":[{"id":"https://openalex.org/I39343248","display_name":"Humboldt-Universit\u00e4t zu Berlin","ror":"https://ror.org/01hcx6992","country_code":"DE","type":"education","lineage":["https://openalex.org/I39343248"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Sebastian van der Linden","raw_affiliation_strings":["Geography Department, Humboldt-Universit\u00e4t zu Berlin, Berlin, Germany","Geomatics Laboratory Geography Department, Huiyin Institute of Technology, Berlin, Germany"],"affiliations":[{"raw_affiliation_string":"Geography Department, Humboldt-Universit\u00e4t zu Berlin, Berlin, Germany","institution_ids":["https://openalex.org/I39343248"]},{"raw_affiliation_string":"Geomatics Laboratory Geography Department, Huiyin Institute of Technology, Berlin, Germany","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035508615","display_name":"J\u00f3n Atli Benediktsson","orcid":"https://orcid.org/0000-0003-0621-9647"},"institutions":[{"id":"https://openalex.org/I165368041","display_name":"University of Iceland","ror":"https://ror.org/01db6h964","country_code":"IS","type":"education","lineage":["https://openalex.org/I165368041"]}],"countries":["IS"],"is_corresponding":false,"raw_author_name":"J\u00f3n Atli Benediktsson","raw_affiliation_strings":["Faculty of Electrical and Computer Engineering, University of Iceland, Reykjavik, Iceland"],"affiliations":[{"raw_affiliation_string":"Faculty of Electrical and Computer Engineering, University of Iceland, Reykjavik, Iceland","institution_ids":["https://openalex.org/I165368041"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5108536470","display_name":"Andreas Rabe","orcid":null},"institutions":[{"id":"https://openalex.org/I39343248","display_name":"Humboldt-Universit\u00e4t zu Berlin","ror":"https://ror.org/01hcx6992","country_code":"DE","type":"education","lineage":["https://openalex.org/I39343248"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Andreas Rabe","raw_affiliation_strings":["Geography Department, Humboldt-Universit\u00e4t zu Berlin, Berlin, Germany","Geomatics Laboratory Geography Department, Huiyin Institute of Technology, Berlin, Germany"],"affiliations":[{"raw_affiliation_string":"Geography Department, Humboldt-Universit\u00e4t zu Berlin, Berlin, Germany","institution_ids":["https://openalex.org/I39343248"]},{"raw_affiliation_string":"Geomatics Laboratory Geography Department, Huiyin Institute of Technology, Berlin, Germany","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5041171844","display_name":"Patrick Hostert","orcid":"https://orcid.org/0000-0002-5730-5484"},"institutions":[{"id":"https://openalex.org/I39343248","display_name":"Humboldt-Universit\u00e4t zu Berlin","ror":"https://ror.org/01hcx6992","country_code":"DE","type":"education","lineage":["https://openalex.org/I39343248"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Patrick Hostert","raw_affiliation_strings":["Geography Department, Humboldt-Universit\u00e4t zu Berlin, Berlin, Germany","Geomatics Laboratory Geography Department, Huiyin Institute of Technology, Berlin, Germany"],"affiliations":[{"raw_affiliation_string":"Geography Department, Humboldt-Universit\u00e4t zu Berlin, Berlin, Germany","institution_ids":["https://openalex.org/I39343248"]},{"raw_affiliation_string":"Geomatics Laboratory Geography Department, Huiyin Institute of Technology, Berlin, Germany","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5018040226"],"corresponding_institution_ids":["https://openalex.org/I135140700"],"apc_list":null,"apc_paid":null,"fwci":26.2688,"has_fulltext":false,"cited_by_count":318,"citation_normalized_percentile":{"value":0.99711621,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":100},"biblio":{"volume":"48","issue":"7","first_page":"2880","last_page":"2889"},"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/T13890","display_name":"Remote Sensing and Land Use","score":0.9918000102043152,"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":0.9908999800682068,"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/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.8686379790306091},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.8354445695877075},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.741381049156189},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6911137104034424},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6618178486824036},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.647464394569397},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6206413507461548},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5375307202339172},{"id":"https://openalex.org/keywords/land-cover","display_name":"Land cover","score":0.5335192084312439},{"id":"https://openalex.org/keywords/statistical-classification","display_name":"Statistical classification","score":0.470316082239151},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.4540754556655884},{"id":"https://openalex.org/keywords/data-classification","display_name":"Data classification","score":0.43951475620269775},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4331167936325073},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.403589129447937},{"id":"https://openalex.org/keywords/land-use","display_name":"Land use","score":0.09252464771270752}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.8686379790306091},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.8354445695877075},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.741381049156189},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6911137104034424},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6618178486824036},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.647464394569397},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6206413507461548},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5375307202339172},{"id":"https://openalex.org/C2780648208","wikidata":"https://www.wikidata.org/wiki/Q3001793","display_name":"Land cover","level":3,"score":0.5335192084312439},{"id":"https://openalex.org/C110083411","wikidata":"https://www.wikidata.org/wiki/Q1744628","display_name":"Statistical classification","level":2,"score":0.470316082239151},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.4540754556655884},{"id":"https://openalex.org/C2780724565","wikidata":"https://www.wikidata.org/wiki/Q5227256","display_name":"Data classification","level":2,"score":0.43951475620269775},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4331167936325073},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.403589129447937},{"id":"https://openalex.org/C4792198","wikidata":"https://www.wikidata.org/wiki/Q1165944","display_name":"Land use","level":2,"score":0.09252464771270752},{"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}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tgrs.2010.2041784","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2010.2041784","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:10.1.1.470.471","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.470.471","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"https://notendur.hi.is/benedikt/Waske_et_al_TGRS_2010.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7400000095367432,"display_name":"Life in Land","id":"https://metadata.un.org/sdg/15"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320323700","display_name":"Humboldt-Universit\u00e4t zu Berlin","ror":"https://ror.org/01hcx6992"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":50,"referenced_works":["https://openalex.org/W26816478","https://openalex.org/W651241572","https://openalex.org/W1570448133","https://openalex.org/W1906766771","https://openalex.org/W1968612609","https://openalex.org/W1977066218","https://openalex.org/W1990653740","https://openalex.org/W2017050504","https://openalex.org/W2022369455","https://openalex.org/W2043665634","https://openalex.org/W2078619499","https://openalex.org/W2082880010","https://openalex.org/W2090425484","https://openalex.org/W2098057602","https://openalex.org/W2101711129","https://openalex.org/W2103699041","https://openalex.org/W2109094355","https://openalex.org/W2111787810","https://openalex.org/W2112076978","https://openalex.org/W2112803241","https://openalex.org/W2113242816","https://openalex.org/W2113458744","https://openalex.org/W2113621698","https://openalex.org/W2124706543","https://openalex.org/W2129211905","https://openalex.org/W2131697388","https://openalex.org/W2135293965","https://openalex.org/W2136251662","https://openalex.org/W2137641062","https://openalex.org/W2139212933","https://openalex.org/W2145862305","https://openalex.org/W2151773573","https://openalex.org/W2154874087","https://openalex.org/W2156909104","https://openalex.org/W2157559031","https://openalex.org/W2165049595","https://openalex.org/W2165491713","https://openalex.org/W2165796970","https://openalex.org/W2167917621","https://openalex.org/W2169482235","https://openalex.org/W2911964244","https://openalex.org/W2912934387","https://openalex.org/W2966207845","https://openalex.org/W3120421331","https://openalex.org/W4212883601","https://openalex.org/W4230674625","https://openalex.org/W4320339642","https://openalex.org/W6601080436","https://openalex.org/W6639916179","https://openalex.org/W6676769703"],"related_works":["https://openalex.org/W2901444342","https://openalex.org/W2596054022","https://openalex.org/W2144337949","https://openalex.org/W2371570177","https://openalex.org/W2740479052","https://openalex.org/W1529156857","https://openalex.org/W2168920113","https://openalex.org/W3202881146","https://openalex.org/W3199023014","https://openalex.org/W2357721021"],"abstract_inverted_index":{"The":[0],"accuracy":[1,145,194],"of":[2,26,85,99,121,125,134,140,164,183],"supervised":[3],"land":[4,39],"cover":[5,40],"classifications":[6],"depends":[7],"on":[8,37,108],"factors":[9],"such":[10],"as":[11,195,197],"the":[12,19,24,38,47,103,119,126,132,138,141,181,204,222],"chosen":[13],"classification":[14,57,144,193,213],"algorithm,":[15],"adequate":[16],"training":[17,100,165],"data,":[18,92],"input":[20],"data":[21,149,231],"characteristics,":[22],"and":[23,34,52,95,110,137,173,199],"selection":[25,113],"features.":[27],"Hyperspectral":[28],"imaging":[29],"provides":[30],"more":[31,63,211],"detailed":[32],"spectral":[33],"spatial":[35],"information":[36],"than":[41,215],"other":[42],"remote":[43],"sensing":[44],"resources.":[45],"Over":[46],"past":[48],"ten":[49],"years,":[50],"traditional":[51],"formerly":[53],"widely":[54],"accepted":[55],"statistical":[56,86],"methods":[58],"have":[59],"been":[60],"superseded":[61],"by":[62,74,83],"recent":[64],"machine":[65],"learning":[66],"algorithms,":[67],"e.g.,":[68],"support":[69],"vector":[70],"machines":[71],"(SVMs),":[72],"or":[73],"multiple":[75],"classifier":[76,186],"systems":[77],"(MCS).":[78],"This":[79],"can":[80],"be":[81],"explained":[82],"limitations":[84],"approaches":[87],"with":[88,160,170,188],"regard":[89],"to":[90,117],"high-dimensional":[91],"multimodal":[93],"classes,":[94],"often":[96],"limited":[97],"availability":[98],"data.":[101],"In":[102,155,202],"presented":[104],"study,":[105],"MCSs":[106],"based":[107],"SVM":[109,172],"random":[111,174],"feature":[112],"(RFS)":[114],"are":[115,168],"applied":[116],"explore":[118],"potential":[120],"a":[122,161],"synergetic":[123],"use":[124],"two":[127,147],"concepts.":[128],"We":[129],"investigated":[130],"how":[131],"number":[133,163],"selected":[135],"features":[136],"size":[139],"MCS":[142],"influence":[143],"using":[146],"hyperspectral":[148,230],"sets,":[150],"from":[151,217,221],"different":[152],"environmental":[153],"settings.":[154],"addition,":[156,203],"experiments":[157,223],"were":[158,224],"conducted":[159],"varying":[162],"samples.":[166],"Accuracies":[167],"compared":[169],"regular":[171],"forests.":[175],"Experimental":[176],"results":[177,207],"clearly":[178],"demonstrate":[179],"that":[180],"generation":[182],"an":[184,228],"SVM-based":[185],"system":[187],"RFS":[189],"significantly":[190],"improves":[191],"overall":[192],"well":[196],"producer's":[198],"user's":[200],"accuracies.":[201],"ensemble":[205],"strategy":[206],"in":[208],"smoother,":[209],"i.e.,":[210],"realistic,":[212],"maps":[214],"those":[216],"stand-alone":[218],"SVM.":[219],"Findings":[220],"successfully":[225],"transferred":[226],"onto":[227],"additional":[229],"set.":[232]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":23},{"year":2023,"cited_by_count":31},{"year":2022,"cited_by_count":20},{"year":2021,"cited_by_count":23},{"year":2020,"cited_by_count":31},{"year":2019,"cited_by_count":29},{"year":2018,"cited_by_count":19},{"year":2017,"cited_by_count":25},{"year":2016,"cited_by_count":19},{"year":2015,"cited_by_count":17},{"year":2014,"cited_by_count":24},{"year":2013,"cited_by_count":27},{"year":2012,"cited_by_count":11}],"updated_date":"2026-04-07T14:57:38.498316","created_date":"2025-10-10T00:00:00"}
