{"id":"https://openalex.org/W3186592876","doi":"https://doi.org/10.1109/eit51626.2021.9491878","title":"Comparison of data mining algorithms in remote sensing using Lidar data fusion and feature selection","display_name":"Comparison of data mining algorithms in remote sensing using Lidar data fusion and feature selection","publication_year":2021,"publication_date":"2021-05-14","ids":{"openalex":"https://openalex.org/W3186592876","doi":"https://doi.org/10.1109/eit51626.2021.9491878","mag":"3186592876"},"language":"en","primary_location":{"id":"doi:10.1109/eit51626.2021.9491878","is_oa":false,"landing_page_url":"https://doi.org/10.1109/eit51626.2021.9491878","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Electro Information Technology (EIT)","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/A5047996569","display_name":"Papia F. Rozario","orcid":"https://orcid.org/0000-0002-2066-9479"},"institutions":[{"id":"https://openalex.org/I124727415","display_name":"University of Wisconsin\u2013Eau Claire","ror":"https://ror.org/03mnm0t94","country_code":"US","type":"education","lineage":["https://openalex.org/I124727415"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Papia Rozario","raw_affiliation_strings":["University of Wisconsin-Eau Claire,Dept. of Geography and Anthropology,Eau Claire,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Wisconsin-Eau Claire,Dept. of Geography and Anthropology,Eau Claire,USA","institution_ids":["https://openalex.org/I124727415"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5034845580","display_name":"Rahul Gomes","orcid":"https://orcid.org/0000-0002-5377-8196"},"institutions":[{"id":"https://openalex.org/I124727415","display_name":"University of Wisconsin\u2013Eau Claire","ror":"https://ror.org/03mnm0t94","country_code":"US","type":"education","lineage":["https://openalex.org/I124727415"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Rahul Gomes","raw_affiliation_strings":["University of Wisconsin-Eau Claire,Dept. of Computer Science,Eau Claire,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Wisconsin-Eau Claire,Dept. of Computer Science,Eau Claire,USA","institution_ids":["https://openalex.org/I124727415"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I124727415"],"apc_list":null,"apc_paid":null,"fwci":0.2748,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.52143191,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":"5","issue":null,"first_page":"236","last_page":"243"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"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/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"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":0.9997000098228455,"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/T10895","display_name":"Species Distribution and Climate Change","score":0.9962000250816345,"subfield":{"id":"https://openalex.org/subfields/2302","display_name":"Ecological Modeling"},"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/multispectral-image","display_name":"Multispectral image","score":0.7644100189208984},{"id":"https://openalex.org/keywords/lidar","display_name":"Lidar","score":0.6523491144180298},{"id":"https://openalex.org/keywords/multispectral-pattern-recognition","display_name":"Multispectral pattern recognition","score":0.6393545866012573},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5626015663146973},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.5606781244277954},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.5595036745071411},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.5481770038604736},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.5329042077064514},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.5282750725746155},{"id":"https://openalex.org/keywords/sensor-fusion","display_name":"Sensor fusion","score":0.5193077921867371},{"id":"https://openalex.org/keywords/ranging","display_name":"Ranging","score":0.46424946188926697},{"id":"https://openalex.org/keywords/statistical-classification","display_name":"Statistical classification","score":0.4635469317436218},{"id":"https://openalex.org/keywords/land-cover","display_name":"Land cover","score":0.4614397883415222},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4006243348121643},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3794650733470917},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3571721911430359},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.1863919198513031},{"id":"https://openalex.org/keywords/land-use","display_name":"Land use","score":0.15517911314964294}],"concepts":[{"id":"https://openalex.org/C173163844","wikidata":"https://www.wikidata.org/wiki/Q1761440","display_name":"Multispectral image","level":2,"score":0.7644100189208984},{"id":"https://openalex.org/C51399673","wikidata":"https://www.wikidata.org/wiki/Q504027","display_name":"Lidar","level":2,"score":0.6523491144180298},{"id":"https://openalex.org/C104541649","wikidata":"https://www.wikidata.org/wiki/Q6935090","display_name":"Multispectral pattern recognition","level":3,"score":0.6393545866012573},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5626015663146973},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.5606781244277954},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.5595036745071411},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.5481770038604736},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.5329042077064514},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.5282750725746155},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.5193077921867371},{"id":"https://openalex.org/C115051666","wikidata":"https://www.wikidata.org/wiki/Q6522493","display_name":"Ranging","level":2,"score":0.46424946188926697},{"id":"https://openalex.org/C110083411","wikidata":"https://www.wikidata.org/wiki/Q1744628","display_name":"Statistical classification","level":2,"score":0.4635469317436218},{"id":"https://openalex.org/C2780648208","wikidata":"https://www.wikidata.org/wiki/Q3001793","display_name":"Land cover","level":3,"score":0.4614397883415222},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4006243348121643},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3794650733470917},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3571721911430359},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.1863919198513031},{"id":"https://openalex.org/C4792198","wikidata":"https://www.wikidata.org/wiki/Q1165944","display_name":"Land use","level":2,"score":0.15517911314964294},{"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/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/eit51626.2021.9491878","is_oa":false,"landing_page_url":"https://doi.org/10.1109/eit51626.2021.9491878","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Electro Information Technology (EIT)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Life in Land","id":"https://metadata.un.org/sdg/15","score":0.7300000190734863}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W41607323","https://openalex.org/W75876269","https://openalex.org/W91932901","https://openalex.org/W1497089125","https://openalex.org/W1965825034","https://openalex.org/W1987821097","https://openalex.org/W2004492626","https://openalex.org/W2022792380","https://openalex.org/W2032013744","https://openalex.org/W2042031583","https://openalex.org/W2073056054","https://openalex.org/W2081543911","https://openalex.org/W2092120745","https://openalex.org/W2094153102","https://openalex.org/W2119412805","https://openalex.org/W2119718161","https://openalex.org/W2141206308","https://openalex.org/W2148143831","https://openalex.org/W2148645123","https://openalex.org/W2156909104","https://openalex.org/W2157151912","https://openalex.org/W2167711035","https://openalex.org/W2172025119","https://openalex.org/W2273297058","https://openalex.org/W2557799930","https://openalex.org/W2599919765","https://openalex.org/W2899147610","https://openalex.org/W2899487481","https://openalex.org/W2911964244","https://openalex.org/W2941023688","https://openalex.org/W4239944110","https://openalex.org/W6601649133","https://openalex.org/W6603746958","https://openalex.org/W6677631928","https://openalex.org/W6683581212","https://openalex.org/W6685093538"],"related_works":["https://openalex.org/W2128126485","https://openalex.org/W1995889410","https://openalex.org/W1752760603","https://openalex.org/W4382563209","https://openalex.org/W2124952510","https://openalex.org/W2777937183","https://openalex.org/W2108633818","https://openalex.org/W2124951708","https://openalex.org/W1634492240","https://openalex.org/W2126922921"],"abstract_inverted_index":{"Application":[0],"of":[1,8,52,63,96],"data":[2,87,112,156],"mining":[3],"techniques":[4],"defines":[5],"the":[6,93,117,139,146,163],"basis":[7],"land":[9,22],"use":[10],"classification.":[11],"Even":[12],"though":[13],"multispectral":[14],"images":[15],"can":[16],"be":[17],"very":[18],"accurate":[19],"in":[20,92,145],"classifying":[21],"cover":[23],"categories,":[24],"using":[25],"spectral":[26,39,79,118],"reflectivity":[27,80,119],"alone":[28,82],"sometimes":[29],"fails":[30],"to":[31,50,115,133,141],"distinguish":[32],"between":[33],"landcover":[34],"types":[35],"that":[36,107],"share":[37],"similar":[38],"signatures":[40],"such":[41],"as":[42],"forest":[43],"and":[44,66,69,83,124,138,158],"wetlands.":[45],"The":[46],"problem":[47],"aggravates":[48],"owing":[49],"interpolation":[51],"neighbourhood":[53],"pixel":[54],"values.":[55,120],"In":[56],"this":[57],"paper,":[58],"we":[59],"present":[60],"a":[61],"comparison":[62],"four":[64],"classification":[65],"clustering":[67,160],"algorithms":[68,74],"analyze":[70],"their":[71,134],"performance.":[72],"These":[73],"are":[75],"applied":[76],"both":[77],"on":[78],"values":[81],"along":[84],"with":[85,154],"Lidar":[86,111,155],"fusion.":[88],"Experiments":[89,105],"were":[90],"performed":[91],"Carlton":[94],"County":[95],"Minnesota.":[97],"Accuracy":[98],"estimation":[99],"was":[100],"conducted":[101],"for":[102],"all":[103],"models.":[104],"indicate":[106],"accuracy":[108,165],"increases":[109],"when":[110],"is":[113],"used":[114],"complement":[116],"Random":[121],"Forest":[122],"Classification":[123],"Support":[125],"Vector":[126],"Machines":[127],"yield":[128],"good":[129],"results":[130],"consistently":[131],"due":[132],"ensemble":[135],"learning":[136],"methods":[137],"ability":[140],"represent":[142],"non-linear":[143],"relationship":[144],"dataset,":[147],"respectively.":[148],"Maximum":[149],"likelihood":[150],"shows":[151],"significant":[152],"improvement":[153],"fusion":[157],"ISODATA":[159],"approach":[161],"has":[162],"lowest":[164],"rate.":[166]},"counts_by_year":[{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1}],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}
