{"id":"https://openalex.org/W4292313826","doi":"https://doi.org/10.3390/rs14163972","title":"Reshaping Hyperspectral Data into a Two-Dimensional Image for a CNN Model to Classify Plant Species from Reflectance","display_name":"Reshaping Hyperspectral Data into a Two-Dimensional Image for a CNN Model to Classify Plant Species from Reflectance","publication_year":2022,"publication_date":"2022-08-16","ids":{"openalex":"https://openalex.org/W4292313826","doi":"https://doi.org/10.3390/rs14163972"},"language":"en","primary_location":{"id":"doi:10.3390/rs14163972","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs14163972","pdf_url":"https://www.mdpi.com/2072-4292/14/16/3972/pdf?version=1660650669","source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Remote Sensing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2072-4292/14/16/3972/pdf?version=1660650669","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101703196","display_name":"Shaoxiong Yuan","orcid":"https://orcid.org/0000-0002-5702-3372"},"institutions":[{"id":"https://openalex.org/I4210103524","display_name":"Guangdong Academy of Sciences","ror":"https://ror.org/01g9hkj35","country_code":"CN","type":"government","lineage":["https://openalex.org/I4210103524"]},{"id":"https://openalex.org/I4210147158","display_name":"Guangzhou Institute of Geography","ror":"https://ror.org/03tbxt129","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210147158"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Shaoxiong Yuan","raw_affiliation_strings":["Guangdong Provincial Public Laboratory of Geospatial Information Technology and Application, Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou 510070, China"],"affiliations":[{"raw_affiliation_string":"Guangdong Provincial Public Laboratory of Geospatial Information Technology and Application, Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou 510070, China","institution_ids":["https://openalex.org/I4210147158","https://openalex.org/I4210103524"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002126800","display_name":"Guangman Song","orcid":"https://orcid.org/0000-0002-2896-048X"},"institutions":[{"id":"https://openalex.org/I1298590031","display_name":"Shizuoka University","ror":"https://ror.org/01w6wtk13","country_code":"JP","type":"education","lineage":["https://openalex.org/I1298590031"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Guangman Song","raw_affiliation_strings":["Faculty of Agriculture, Shizuoka University, Shizuoka 422-8529, Japan"],"affiliations":[{"raw_affiliation_string":"Faculty of Agriculture, Shizuoka University, Shizuoka 422-8529, Japan","institution_ids":["https://openalex.org/I1298590031"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110357729","display_name":"Guangqing Huang","orcid":null},"institutions":[{"id":"https://openalex.org/I4210147158","display_name":"Guangzhou Institute of Geography","ror":"https://ror.org/03tbxt129","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210147158"]},{"id":"https://openalex.org/I4210103524","display_name":"Guangdong Academy of Sciences","ror":"https://ror.org/01g9hkj35","country_code":"CN","type":"government","lineage":["https://openalex.org/I4210103524"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guangqing Huang","raw_affiliation_strings":["Guangdong Provincial Public Laboratory of Geospatial Information Technology and Application, Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou 510070, China"],"affiliations":[{"raw_affiliation_string":"Guangdong Provincial Public Laboratory of Geospatial Information Technology and Application, Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou 510070, China","institution_ids":["https://openalex.org/I4210147158","https://openalex.org/I4210103524"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5108047863","display_name":"Quan Wang","orcid":"https://orcid.org/0000-0001-5483-0243"},"institutions":[{"id":"https://openalex.org/I1298590031","display_name":"Shizuoka University","ror":"https://ror.org/01w6wtk13","country_code":"JP","type":"education","lineage":["https://openalex.org/I1298590031"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Quan Wang","raw_affiliation_strings":["Faculty of Agriculture, Shizuoka University, Shizuoka 422-8529, Japan"],"affiliations":[{"raw_affiliation_string":"Faculty of Agriculture, Shizuoka University, Shizuoka 422-8529, Japan","institution_ids":["https://openalex.org/I1298590031"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5101703196"],"corresponding_institution_ids":["https://openalex.org/I4210103524","https://openalex.org/I4210147158"],"apc_list":{"value":2500,"currency":"CHF","value_usd":2707},"apc_paid":{"value":2500,"currency":"CHF","value_usd":2707},"fwci":2.5464,"has_fulltext":true,"cited_by_count":15,"citation_normalized_percentile":{"value":0.89071648,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":97,"max":98},"biblio":{"volume":"14","issue":"16","first_page":"3972","last_page":"3972"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10111","display_name":"Remote Sensing in Agriculture","score":0.9987999796867371,"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"}},"topics":[{"id":"https://openalex.org/T10111","display_name":"Remote Sensing in Agriculture","score":0.9987999796867371,"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/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.9930999875068665,"subfield":{"id":"https://openalex.org/subfields/1602","display_name":"Analytical Chemistry"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T14365","display_name":"Leaf Properties and Growth Measurement","score":0.9878000020980835,"subfield":{"id":"https://openalex.org/subfields/1110","display_name":"Plant Science"},"field":{"id":"https://openalex.org/fields/11","display_name":"Agricultural and Biological Sciences"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.9110202789306641},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6730590462684631},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6629269123077393},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.633270263671875},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6056993007659912},{"id":"https://openalex.org/keywords/principal-component-analysis","display_name":"Principal component analysis","score":0.5927045941352844},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5627704858779907},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.48496460914611816},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.4224483370780945},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.07560393214225769}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.9110202789306641},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6730590462684631},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6629269123077393},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.633270263671875},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6056993007659912},{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.5927045941352844},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5627704858779907},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.48496460914611816},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.4224483370780945},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.07560393214225769}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.3390/rs14163972","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs14163972","pdf_url":"https://www.mdpi.com/2072-4292/14/16/3972/pdf?version=1660650669","source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Remote Sensing","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:b1937e97d10e484bafbea0dca5b2dab2","is_oa":true,"landing_page_url":"https://doaj.org/article/b1937e97d10e484bafbea0dca5b2dab2","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Remote Sensing, Vol 14, Iss 16, p 3972 (2022)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/2072-4292/14/16/3972/","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/rs14163972","pdf_url":null,"source":{"id":"https://openalex.org/S4306400947","display_name":"MDPI (MDPI AG)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210097602","host_organization_name":"Multidisciplinary Digital Publishing Institute (Switzerland)","host_organization_lineage":["https://openalex.org/I4210097602"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Remote Sensing","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/rs14163972","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs14163972","pdf_url":"https://www.mdpi.com/2072-4292/14/16/3972/pdf?version=1660650669","source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Remote Sensing","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Life in Land","id":"https://metadata.un.org/sdg/15","score":0.75}],"awards":[{"id":"https://openalex.org/G1571256415","display_name":null,"funder_award_id":"2020B121201013","funder_id":"https://openalex.org/F4320335795","funder_display_name":"Science and Technology Planning Project of Guangdong Province"},{"id":"https://openalex.org/G3516498560","display_name":null,"funder_award_id":"2020GDASYL-20200301003","funder_id":"https://openalex.org/F4320335795","funder_display_name":"Science and Technology Planning Project of Guangdong Province"},{"id":"https://openalex.org/G5048721785","display_name":null,"funder_award_id":"2020B121201013","funder_id":"https://openalex.org/F4320335357","funder_display_name":"Guangdong Academy of Sciences"},{"id":"https://openalex.org/G5724592921","display_name":null,"funder_award_id":"2020GDA","funder_id":"https://openalex.org/F4320335357","funder_display_name":"Guangdong Academy of Sciences"},{"id":"https://openalex.org/G859055919","display_name":null,"funder_award_id":"2020GDASYL-20200301003","funder_id":"https://openalex.org/F4320335357","funder_display_name":"Guangdong Academy of Sciences"}],"funders":[{"id":"https://openalex.org/F4320335357","display_name":"Guangdong Academy of Sciences","ror":"https://ror.org/01g9hkj35"},{"id":"https://openalex.org/F4320335795","display_name":"Science and Technology Planning Project of Guangdong Province","ror":null}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4292313826.pdf","grobid_xml":"https://content.openalex.org/works/W4292313826.grobid-xml"},"referenced_works_count":52,"referenced_works":["https://openalex.org/W1967320999","https://openalex.org/W2012688023","https://openalex.org/W2024039740","https://openalex.org/W2030432742","https://openalex.org/W2036003376","https://openalex.org/W2037295424","https://openalex.org/W2063907334","https://openalex.org/W2088794999","https://openalex.org/W2089882657","https://openalex.org/W2095705004","https://openalex.org/W2101234009","https://openalex.org/W2109191549","https://openalex.org/W2118823101","https://openalex.org/W2130774035","https://openalex.org/W2145862305","https://openalex.org/W2163605009","https://openalex.org/W2176623756","https://openalex.org/W2197299173","https://openalex.org/W2329061269","https://openalex.org/W2515306179","https://openalex.org/W2568155635","https://openalex.org/W2590489721","https://openalex.org/W2616728375","https://openalex.org/W2791303772","https://openalex.org/W2795866616","https://openalex.org/W2888214130","https://openalex.org/W2900713360","https://openalex.org/W2902505403","https://openalex.org/W2919115771","https://openalex.org/W2963755276","https://openalex.org/W2963801405","https://openalex.org/W2964182926","https://openalex.org/W2964318286","https://openalex.org/W2976141963","https://openalex.org/W2979348177","https://openalex.org/W2998198695","https://openalex.org/W3008511810","https://openalex.org/W3036016333","https://openalex.org/W3037775055","https://openalex.org/W3041018999","https://openalex.org/W3085588412","https://openalex.org/W3093945404","https://openalex.org/W3118829545","https://openalex.org/W3132859298","https://openalex.org/W3150095517","https://openalex.org/W3153687269","https://openalex.org/W6631190155","https://openalex.org/W6674330103","https://openalex.org/W6675354045","https://openalex.org/W6768390318","https://openalex.org/W6788350531","https://openalex.org/W6963572241"],"related_works":["https://openalex.org/W2072166414","https://openalex.org/W3209970181","https://openalex.org/W2060875994","https://openalex.org/W3034375524","https://openalex.org/W4230131218","https://openalex.org/W4396689146","https://openalex.org/W4200112873","https://openalex.org/W2955796858","https://openalex.org/W2004826645","https://openalex.org/W4372048956"],"abstract_inverted_index":{"Leaf-level":[0],"hyperspectral-based":[1],"species":[2,13,98,193,219],"identification":[3,48],"has":[4],"a":[5,189,192,203,211],"long":[6],"research":[7,32],"history.":[8],"However,":[9],"unlike":[10],"hyperspectral":[11,35,57,102,198],"image-based":[12],"classification":[14,99,194],"models,":[15,171],"convolutional":[16],"neural":[17,81],"network":[18,82],"(CNN)":[19],"models":[20,111],"are":[21],"rarely":[22],"used":[23],"for":[24,128,191,227],"the":[25,43,50,72,78,106,110,125,149,169,215],"one-dimensional":[26],"(1D)":[27],"structured":[28],"leaf-level":[29,101],"spectrum.":[30],"Our":[31,186],"focuses":[33],"on":[34,197],"data":[36,58,118,127,199,209],"from":[37,100,119],"five":[38],"laboratories":[39],"worldwide":[40],"to":[41,148,202,217],"test":[42],"general":[44,107],"use":[45],"of":[46,49,74,109,137,141,145,206],"effective":[47],"CNN":[51,132],"model":[52,133],"by":[53,112],"reshaping":[54,207],"1D":[55,208],"structure":[56],"into":[59,210],"two-dimensional":[60,75,212],"greyscale":[61],"images":[62],"without":[63],"principal":[64],"component":[65],"analysis":[66],"(PCA)":[67],"or":[68,122],"downscaling.":[69],"We":[70,104],"compared":[71,147],"performance":[73,108],"CNNs":[76,172],"with":[77,153,161,168],"deep":[79],"cross":[80],"(DCN),":[83],"support":[84,150],"vector":[85,151],"machine,":[86,91,152],"random":[87],"forest,":[88],"gradient":[89],"boosting":[90],"and":[92,143,156,159,164,178,200],"decision":[93],"tree":[94,97],"in":[95,182],"individual":[96],"data.":[103],"tested":[105],"simulating":[113],"an":[114],"application":[115],"phase":[116],"using":[117],"different":[120],"labs":[121],"years":[123],"as":[124,214],"unseen":[126],"prediction.":[129,220],"The":[130],"best-performing":[131],"had":[134,179],"validation":[135],"accuracy":[136,140,181],"98.6%,":[138,154],"prediction":[139],"91.6%,":[142],"precision":[144],"74.9%,":[146],"88.8%,":[155],"66.4%,":[157],"respectively,":[158],"DCN,":[160],"94.0%,":[162],"85.7%,":[163],"57.1%,":[165],"respectively.":[166],"Compared":[167],"reference":[170],"more":[173],"efficiently":[174],"recognized":[175],"Fagus":[176],"crenata,":[177],"high":[180],"Quercus":[183],"rubra":[184],"identification.":[185],"results":[187],"provide":[188],"template":[190],"method":[195,222],"based":[196],"point":[201],"new":[204],"way":[205],"image,":[213],"key":[216],"better":[218],"This":[221],"may":[223],"also":[224],"be":[225],"helpful":[226],"foliar":[228],"trait":[229],"estimation.":[230]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":5}],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2025-10-10T00:00:00"}