{"id":"https://openalex.org/W2994088486","doi":"https://doi.org/10.1109/whispers.2019.8921016","title":"Individual Tree Species Classification Using Airborne Hyperspectral Imagery And Lidar Data","display_name":"Individual Tree Species Classification Using Airborne Hyperspectral Imagery And Lidar Data","publication_year":2019,"publication_date":"2019-09-01","ids":{"openalex":"https://openalex.org/W2994088486","doi":"https://doi.org/10.1109/whispers.2019.8921016","mag":"2994088486"},"language":"en","primary_location":{"id":"doi:10.1109/whispers.2019.8921016","is_oa":false,"landing_page_url":"https://doi.org/10.1109/whispers.2019.8921016","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS)","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/A5008170866","display_name":"P\u00e9ter Burai","orcid":"https://orcid.org/0000-0001-5989-5521"},"institutions":[{"id":"https://openalex.org/I132735039","display_name":"University of Debrecen","ror":"https://ror.org/02xf66n48","country_code":"HU","type":"education","lineage":["https://openalex.org/I132735039"]}],"countries":["HU"],"is_corresponding":false,"raw_author_name":"Peter Burai","raw_affiliation_strings":["Remote Sensing Centre, University of Debrecen, Hungary"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Remote Sensing Centre, University of Debrecen, Hungary","institution_ids":["https://openalex.org/I132735039"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5048646070","display_name":"L\u00e1szl\u00f3 Bek\u0151","orcid":"https://orcid.org/0000-0002-2956-6193"},"institutions":[{"id":"https://openalex.org/I132735039","display_name":"University of Debrecen","ror":"https://ror.org/02xf66n48","country_code":"HU","type":"education","lineage":["https://openalex.org/I132735039"]}],"countries":["HU"],"is_corresponding":false,"raw_author_name":"Laszlo Beko","raw_affiliation_strings":["Remote Sensing Centre, University of Debrecen, Hungary"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Remote Sensing Centre, University of Debrecen, Hungary","institution_ids":["https://openalex.org/I132735039"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033847196","display_name":"Csaba L\u00e9n\u00e1rt","orcid":"https://orcid.org/0000-0002-1483-8723"},"institutions":[{"id":"https://openalex.org/I132735039","display_name":"University of Debrecen","ror":"https://ror.org/02xf66n48","country_code":"HU","type":"education","lineage":["https://openalex.org/I132735039"]}],"countries":["HU"],"is_corresponding":false,"raw_author_name":"Csaba Lenart","raw_affiliation_strings":["Remote Sensing Centre, University of Debrecen, Hungary"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Remote Sensing Centre, University of Debrecen, Hungary","institution_ids":["https://openalex.org/I132735039"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078908447","display_name":"Tam\u00e1s Tomor","orcid":"https://orcid.org/0000-0002-4154-5088"},"institutions":[{"id":"https://openalex.org/I132735039","display_name":"University of Debrecen","ror":"https://ror.org/02xf66n48","country_code":"HU","type":"education","lineage":["https://openalex.org/I132735039"]}],"countries":["HU"],"is_corresponding":false,"raw_author_name":"Tamas Tomor","raw_affiliation_strings":["Remote Sensing Centre, University of Debrecen, Hungary"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Remote Sensing Centre, University of Debrecen, Hungary","institution_ids":["https://openalex.org/I132735039"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5054602927","display_name":"Zolt\u00e1n Kov\u00e1cs","orcid":"https://orcid.org/0000-0003-0641-8830"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zoltan Kovacs","raw_affiliation_strings":["Envirosense Hungary Ltd, Hungary"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Envirosense Hungary Ltd, Hungary","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.1454,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.76165139,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":"70","issue":null,"first_page":"1","last_page":"4"},"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/T10689","display_name":"Remote-Sensing Image Classification","score":0.9901000261306763,"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/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.8217359781265259},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.7350943684577942},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6904596090316772},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6551563143730164},{"id":"https://openalex.org/keywords/lidar","display_name":"Lidar","score":0.5652408599853516},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5432873368263245},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.5183409452438354},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.49362456798553467},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.46633362770080566},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.46399328112602234},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.4497787356376648},{"id":"https://openalex.org/keywords/tree","display_name":"Tree (set theory)","score":0.43720319867134094},{"id":"https://openalex.org/keywords/watershed","display_name":"Watershed","score":0.4242994785308838},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3173485994338989},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.2659672498703003},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.2254767119884491},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.15149086713790894}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.8217359781265259},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.7350943684577942},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6904596090316772},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6551563143730164},{"id":"https://openalex.org/C51399673","wikidata":"https://www.wikidata.org/wiki/Q504027","display_name":"Lidar","level":2,"score":0.5652408599853516},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5432873368263245},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.5183409452438354},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.49362456798553467},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.46633362770080566},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.46399328112602234},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.4497787356376648},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.43720319867134094},{"id":"https://openalex.org/C150547873","wikidata":"https://www.wikidata.org/wiki/Q947851","display_name":"Watershed","level":2,"score":0.4242994785308838},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3173485994338989},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2659672498703003},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.2254767119884491},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.15149086713790894},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/whispers.2019.8921016","is_oa":false,"landing_page_url":"https://doi.org/10.1109/whispers.2019.8921016","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6000000238418579,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W1984232434","https://openalex.org/W2006262525","https://openalex.org/W2054780659","https://openalex.org/W2063396028","https://openalex.org/W2098919237","https://openalex.org/W2108517747","https://openalex.org/W2131697388","https://openalex.org/W2136625467","https://openalex.org/W2270715058","https://openalex.org/W2405365025","https://openalex.org/W2904280379","https://openalex.org/W6676376460"],"related_works":["https://openalex.org/W4319317934","https://openalex.org/W2901265155","https://openalex.org/W2072166414","https://openalex.org/W2956374172","https://openalex.org/W3209970181","https://openalex.org/W2060875994","https://openalex.org/W4319837668","https://openalex.org/W4308071650","https://openalex.org/W2005234362","https://openalex.org/W1997235926"],"abstract_inverted_index":{"In":[0],"this":[1,32],"study":[2,33],"high":[3],"spatial":[4],"resolution":[5],"(1":[6],"m)":[7],"hyperspectral":[8],"images":[9],"and":[10,71],"LiDAR":[11],"data":[12],"(8p/m":[13],"<sup":[14],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[15],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">2</sup>":[16],")":[17],"was":[18,34,50,64,80],"applied":[19,65,109],"to":[20,35,52],"discriminate":[21],"among":[22],"tree":[23,77],"species":[24],"of":[25,31,87,96,115,125],"mixed":[26],"forest.":[27],"The":[28,61,99,113],"main":[29],"objective":[30],"apply":[36],"machine":[37],"learning":[38],"methods":[39],"using":[40],"crown":[41,116],"segments":[42,117],"for":[43],"image":[44,62],"classification.":[45],"A":[46,75],"watershed":[47],"segmentation":[48],"algorithm":[49],"used":[51],"delineate":[53],"individual":[54],"crowns":[55],"from":[56],"a":[57],"filtered":[58],"CHM":[59],"model.":[60],"classification":[63,123,131],"on":[66,90,101],"the":[67,85,91],"original":[68],"spectral":[69],"bands":[70],"transformed":[72],"(MNF)":[73],"dataset.":[74],"binary":[76],"SVM":[78,111],"classifier":[79],"developed":[81],"in":[82,119,122],"accordance":[83],"with":[84],"principle":[86],"SVM,":[88],"based":[89],"Jeffries-Matusita":[92],"(JM)":[93],"separability":[94],"measure":[95],"selected":[97],"classes.":[98],"ABTSVM":[100],"MNF-transformed":[102],"dataset":[103],"provided":[104],"more":[105],"accurate":[106],"results":[107],"than":[108],"multiclass":[110],"methods.":[112],"addition":[114],"resulted":[118],"an":[120],"increase":[121],"accuracy":[124],"14.51":[126],"percentage":[127],"points":[128],"over":[129],"pixel-based":[130],"alone.":[132]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":5},{"year":2020,"cited_by_count":3}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}