{"id":"https://openalex.org/W4387803606","doi":"https://doi.org/10.1109/igarss52108.2023.10281687","title":"Maxent: An Optimal Neighbor Selection for Multispectral Airborne Lidar Point Cloud Classification","display_name":"Maxent: An Optimal Neighbor Selection for Multispectral Airborne Lidar Point Cloud Classification","publication_year":2023,"publication_date":"2023-07-16","ids":{"openalex":"https://openalex.org/W4387803606","doi":"https://doi.org/10.1109/igarss52108.2023.10281687"},"language":"en","primary_location":{"id":"doi:10.1109/igarss52108.2023.10281687","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/igarss52108.2023.10281687","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium","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/A5100765362","display_name":"Ge Jiang","orcid":"https://orcid.org/0000-0001-9192-5444"},"institutions":[{"id":"https://openalex.org/I168635309","display_name":"University of Calgary","ror":"https://ror.org/03yjb2x39","country_code":"CA","type":"education","lineage":["https://openalex.org/I168635309"]},{"id":"https://openalex.org/I14243506","display_name":"Hong Kong Polytechnic University","ror":"https://ror.org/0030zas98","country_code":"HK","type":"education","lineage":["https://openalex.org/I14243506"]}],"countries":["CA","HK"],"is_corresponding":true,"raw_author_name":"Ge Jiang","raw_affiliation_strings":["The Hong Kong Polytechnic University,Department of Land Surveying and Geo-Informatics,Hong Kong","Department of Geomatics Engineering, University of Calgary, Calgary, Canada","Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong"],"affiliations":[{"raw_affiliation_string":"The Hong Kong Polytechnic University,Department of Land Surveying and Geo-Informatics,Hong Kong","institution_ids":["https://openalex.org/I14243506"]},{"raw_affiliation_string":"Department of Geomatics Engineering, University of Calgary, Calgary, Canada","institution_ids":["https://openalex.org/I168635309"]},{"raw_affiliation_string":"Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong","institution_ids":["https://openalex.org/I14243506"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070456429","display_name":"Wai Yeung Yan","orcid":"https://orcid.org/0000-0001-7813-1666"},"institutions":[{"id":"https://openalex.org/I14243506","display_name":"Hong Kong Polytechnic University","ror":"https://ror.org/0030zas98","country_code":"HK","type":"education","lineage":["https://openalex.org/I14243506"]},{"id":"https://openalex.org/I530967","display_name":"Toronto Metropolitan University","ror":"https://ror.org/05g13zd79","country_code":"CA","type":"education","lineage":["https://openalex.org/I530967"]}],"countries":["CA","HK"],"is_corresponding":false,"raw_author_name":"Wai Yeung Yan","raw_affiliation_strings":["The Hong Kong Polytechnic University,Department of Land Surveying and Geo-Informatics,Hong Kong","Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong","Department of Civil Engineering, Toronto Metropolitan University, Toronto, Canada"],"affiliations":[{"raw_affiliation_string":"The Hong Kong Polytechnic University,Department of Land Surveying and Geo-Informatics,Hong Kong","institution_ids":["https://openalex.org/I14243506"]},{"raw_affiliation_string":"Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong","institution_ids":["https://openalex.org/I14243506"]},{"raw_affiliation_string":"Department of Civil Engineering, Toronto Metropolitan University, Toronto, Canada","institution_ids":["https://openalex.org/I530967"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5010635267","display_name":"Derek D. Lichti","orcid":"https://orcid.org/0000-0001-7038-073X"},"institutions":[{"id":"https://openalex.org/I168635309","display_name":"University of Calgary","ror":"https://ror.org/03yjb2x39","country_code":"CA","type":"education","lineage":["https://openalex.org/I168635309"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Derek D. Lichti","raw_affiliation_strings":["University of Calgary,Department of Geomatics Engineering,Calgary,Canada","Department of Geomatics Engineering, University of Calgary, Calgary, Canada"],"affiliations":[{"raw_affiliation_string":"University of Calgary,Department of Geomatics Engineering,Calgary,Canada","institution_ids":["https://openalex.org/I168635309"]},{"raw_affiliation_string":"Department of Geomatics Engineering, University of Calgary, Calgary, Canada","institution_ids":["https://openalex.org/I168635309"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5100765362"],"corresponding_institution_ids":["https://openalex.org/I14243506","https://openalex.org/I168635309"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.13001565,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"36","issue":null,"first_page":"4250","last_page":"4253"},"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/T11211","display_name":"3D Surveying and Cultural Heritage","score":0.996999979019165,"subfield":{"id":"https://openalex.org/subfields/1907","display_name":"Geology"},"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":0.9958000183105469,"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/multispectral-image","display_name":"Multispectral image","score":0.8167769908905029},{"id":"https://openalex.org/keywords/lidar","display_name":"Lidar","score":0.7472580671310425},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7297805547714233},{"id":"https://openalex.org/keywords/point-cloud","display_name":"Point cloud","score":0.6835628747940063},{"id":"https://openalex.org/keywords/ranging","display_name":"Ranging","score":0.5564461946487427},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.5189361572265625},{"id":"https://openalex.org/keywords/k-nearest-neighbors-algorithm","display_name":"k-nearest neighbors algorithm","score":0.5112924575805664},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.48926159739494324},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4818117022514343},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.47797200083732605},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.4707411229610443},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.44596824049949646},{"id":"https://openalex.org/keywords/principle-of-maximum-entropy","display_name":"Principle of maximum entropy","score":0.43037179112434387},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.4101601839065552},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3904944658279419},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.16948369145393372},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.07053244113922119}],"concepts":[{"id":"https://openalex.org/C173163844","wikidata":"https://www.wikidata.org/wiki/Q1761440","display_name":"Multispectral image","level":2,"score":0.8167769908905029},{"id":"https://openalex.org/C51399673","wikidata":"https://www.wikidata.org/wiki/Q504027","display_name":"Lidar","level":2,"score":0.7472580671310425},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7297805547714233},{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.6835628747940063},{"id":"https://openalex.org/C115051666","wikidata":"https://www.wikidata.org/wiki/Q6522493","display_name":"Ranging","level":2,"score":0.5564461946487427},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.5189361572265625},{"id":"https://openalex.org/C113238511","wikidata":"https://www.wikidata.org/wiki/Q1071612","display_name":"k-nearest neighbors algorithm","level":2,"score":0.5112924575805664},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.48926159739494324},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4818117022514343},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.47797200083732605},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.4707411229610443},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.44596824049949646},{"id":"https://openalex.org/C9679016","wikidata":"https://www.wikidata.org/wiki/Q1417473","display_name":"Principle of maximum entropy","level":2,"score":0.43037179112434387},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.4101601839065552},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3904944658279419},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.16948369145393372},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.07053244113922119},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"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/igarss52108.2023.10281687","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/igarss52108.2023.10281687","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5899999737739563,"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":7,"referenced_works":["https://openalex.org/W1977271893","https://openalex.org/W2046736943","https://openalex.org/W2099346911","https://openalex.org/W2149887512","https://openalex.org/W2620775722","https://openalex.org/W2887121482","https://openalex.org/W2921362487"],"related_works":["https://openalex.org/W2783354812","https://openalex.org/W4384112194","https://openalex.org/W2103009189","https://openalex.org/W4312958259","https://openalex.org/W4308259661","https://openalex.org/W4390813131","https://openalex.org/W2349383066","https://openalex.org/W4328132048","https://openalex.org/W1969901537","https://openalex.org/W2119768059"],"abstract_inverted_index":{"Multispectral":[0],"Airborne":[1],"Light":[2],"Detection":[3],"And":[4],"Ranging":[5],"(LiDAR)":[6],"technology":[7],"is":[8],"recently":[9],"invented":[10],"to":[11,78,112],"improve":[12],"the":[13,50,59,73,80,114],"capability":[14],"of":[15,28,36,61,83,116,144],"classification":[16,131],"through":[17],"incorporating":[18],"fruitful":[19],"spectral":[20],"information.":[21],"Some":[22],"existing":[23],"methods":[24],"construct":[25],"contextual":[26,87],"features":[27,88],"point":[29,105],"clouds":[30],"by":[31,139,148],"querying":[32],"a":[33],"fixed":[34],"scale":[35],"neighbor":[37,56,68,121],"points":[38],"or":[39,53],"selecting":[40],"an":[41,66],"optimal":[42,67,92],"neighborhood.":[43],"However,":[44],"they":[45],"may":[46],"introduce":[47],"heterogeneity":[48],"from":[49,90],"local":[51],"neighborhood":[52,82,93],"select":[54],"insufficient":[55],"points,":[57],"hindering":[58],"performance":[60,115],"classification.":[62,107],"Therefore,":[63],"we":[64],"propose":[65],"selection":[69,122],"method":[70],"based":[71],"on":[72,133],"maximum":[74],"entropy":[75],"(MaxEnt)":[76],"principle":[77],"determine":[79],"homogeneous":[81],"each":[84],"point.":[85],"The":[86],"extracted":[89],"selected":[91],"are":[94,110],"then":[95],"served":[96],"as":[97],"input":[98],"into":[99],"various":[100],"machine":[101],"learning":[102],"classifiers":[103],"for":[104],"cloud":[106],"Extensive":[108],"experiments":[109],"conducted":[111],"compare":[113],"MaxEnt":[117,127],"against":[118],"six":[119],"other":[120],"methods,":[123],"which":[124],"demonstrate":[125],"that":[126],"can":[128],"achieve":[129],"better":[130],"results":[132],"multispectral":[134],"airborne":[135],"LiDAR":[136],"data":[137],"collected":[138],"Optech":[140],"Titan":[141],"in":[142],"terms":[143],"overall":[145],"accuracy":[146],"improvement":[147],"7.3-19.1%.":[149]},"counts_by_year":[],"updated_date":"2025-12-22T23:10:17.713674","created_date":"2025-10-10T00:00:00"}