{"id":"https://openalex.org/W4312940336","doi":"https://doi.org/10.1109/igarss46834.2022.9884777","title":"Leaf Area Index Estimation from Hemisphere Image Based on GhostNet","display_name":"Leaf Area Index Estimation from Hemisphere Image Based on GhostNet","publication_year":2022,"publication_date":"2022-07-17","ids":{"openalex":"https://openalex.org/W4312940336","doi":"https://doi.org/10.1109/igarss46834.2022.9884777"},"language":"en","primary_location":{"id":"doi:10.1109/igarss46834.2022.9884777","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss46834.2022.9884777","pdf_url":null,"source":{"id":"https://openalex.org/S4363604196","display_name":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2022 - 2022 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/A5113134265","display_name":"Yuanlei Cheng","orcid":null},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yuanlei Cheng","raw_affiliation_strings":["School of Automation Engineering, University of Electronic Science and Technology of China,Chengdu,China,611731"],"affiliations":[{"raw_affiliation_string":"School of Automation Engineering, University of Electronic Science and Technology of China,Chengdu,China,611731","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102008231","display_name":"Yunping Chen","orcid":"https://orcid.org/0000-0002-7821-2308"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yunping Chen","raw_affiliation_strings":["School of Automation Engineering, University of Electronic Science and Technology of China,Chengdu,China,611731"],"affiliations":[{"raw_affiliation_string":"School of Automation Engineering, University of Electronic Science and Technology of China,Chengdu,China,611731","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010111657","display_name":"Shuaifeng Jiao","orcid":null},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shuaifeng Jiao","raw_affiliation_strings":["School of Automation Engineering, University of Electronic Science and Technology of China,Chengdu,China,611731"],"affiliations":[{"raw_affiliation_string":"School of Automation Engineering, University of Electronic Science and Technology of China,Chengdu,China,611731","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059559050","display_name":"Haichang Wei","orcid":null},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haichang Wei","raw_affiliation_strings":["School of Automation Engineering, University of Electronic Science and Technology of China,Chengdu,China,611731"],"affiliations":[{"raw_affiliation_string":"School of Automation Engineering, University of Electronic Science and Technology of China,Chengdu,China,611731","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5069949630","display_name":"Wangyao Shen","orcid":null},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wangyao Shen","raw_affiliation_strings":["School of Automation Engineering, University of Electronic Science and Technology of China,Chengdu,China,611731"],"affiliations":[{"raw_affiliation_string":"School of Automation Engineering, University of Electronic Science and Technology of China,Chengdu,China,611731","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100378075","display_name":"Yan Chen","orcid":"https://orcid.org/0000-0002-3227-4562"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yan Chen","raw_affiliation_strings":["School of Automation Engineering, University of Electronic Science and Technology of China,Chengdu,China,611731"],"affiliations":[{"raw_affiliation_string":"School of Automation Engineering, University of Electronic Science and Technology of China,Chengdu,China,611731","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100725230","display_name":"Shilong Li","orcid":"https://orcid.org/0000-0002-6044-4733"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shilong Li","raw_affiliation_strings":["State Grid Sichuan Electric Power Research Institute,Chengdu,Sichuan,China,610041"],"affiliations":[{"raw_affiliation_string":"State Grid Sichuan Electric Power Research Institute,Chengdu,Sichuan,China,610041","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5104100512","display_name":"Hua Zhan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hua Zhan","raw_affiliation_strings":["State Grid Sichuan Electric Power Research Institute,Chengdu,Sichuan,China,610041"],"affiliations":[{"raw_affiliation_string":"State Grid Sichuan Electric Power Research Institute,Chengdu,Sichuan,China,610041","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":8,"corresponding_author_ids":["https://openalex.org/A5113134265"],"corresponding_institution_ids":["https://openalex.org/I150229711"],"apc_list":null,"apc_paid":null,"fwci":0.7277,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.72208228,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"1400","last_page":"1403"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10111","display_name":"Remote Sensing in Agriculture","score":0.9994000196456909,"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":0.9994000196456909,"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/T14365","display_name":"Leaf Properties and Growth Measurement","score":0.9994000196456909,"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"}},{"id":"https://openalex.org/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.9969000220298767,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/leaf-area-index","display_name":"Leaf area index","score":0.7979519367218018},{"id":"https://openalex.org/keywords/vegetation","display_name":"Vegetation (pathology)","score":0.6558928489685059},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.5694255232810974},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.5680594444274902},{"id":"https://openalex.org/keywords/vegetation-index","display_name":"Vegetation Index","score":0.513925313949585},{"id":"https://openalex.org/keywords/index","display_name":"Index (typography)","score":0.513218879699707},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.47662532329559326},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.4458155632019043},{"id":"https://openalex.org/keywords/vegetation-types","display_name":"Vegetation types","score":0.4335118532180786},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.42311811447143555},{"id":"https://openalex.org/keywords/estimation","display_name":"Estimation","score":0.4138326644897461},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.41313305497169495},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.3774508237838745},{"id":"https://openalex.org/keywords/normalized-difference-vegetation-index","display_name":"Normalized Difference Vegetation Index","score":0.30603083968162537},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.28735870122909546},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.24109727144241333},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.15735319256782532}],"concepts":[{"id":"https://openalex.org/C25989453","wikidata":"https://www.wikidata.org/wiki/Q446746","display_name":"Leaf area index","level":2,"score":0.7979519367218018},{"id":"https://openalex.org/C2776133958","wikidata":"https://www.wikidata.org/wiki/Q7918366","display_name":"Vegetation (pathology)","level":2,"score":0.6558928489685059},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.5694255232810974},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.5680594444274902},{"id":"https://openalex.org/C2780376076","wikidata":"https://www.wikidata.org/wiki/Q1499458","display_name":"Vegetation Index","level":4,"score":0.513925313949585},{"id":"https://openalex.org/C2777382242","wikidata":"https://www.wikidata.org/wiki/Q6017816","display_name":"Index (typography)","level":2,"score":0.513218879699707},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.47662532329559326},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.4458155632019043},{"id":"https://openalex.org/C2994341026","wikidata":"https://www.wikidata.org/wiki/Q18648871","display_name":"Vegetation types","level":3,"score":0.4335118532180786},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.42311811447143555},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.4138326644897461},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.41313305497169495},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3774508237838745},{"id":"https://openalex.org/C1549246","wikidata":"https://www.wikidata.org/wiki/Q718775","display_name":"Normalized Difference Vegetation Index","level":3,"score":0.30603083968162537},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.28735870122909546},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.24109727144241333},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.15735319256782532},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C18903297","wikidata":"https://www.wikidata.org/wiki/Q7150","display_name":"Ecology","level":1,"score":0.0},{"id":"https://openalex.org/C185933670","wikidata":"https://www.wikidata.org/wiki/Q52105","display_name":"Habitat","level":2,"score":0.0},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.0},{"id":"https://openalex.org/C142724271","wikidata":"https://www.wikidata.org/wiki/Q7208","display_name":"Pathology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss46834.2022.9884777","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss46834.2022.9884777","pdf_url":null,"source":{"id":"https://openalex.org/S4363604196","display_name":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6899999976158142,"id":"https://metadata.un.org/sdg/15","display_name":"Life in Land"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":9,"referenced_works":["https://openalex.org/W2127723859","https://openalex.org/W2151647593","https://openalex.org/W2194775991","https://openalex.org/W2752782242","https://openalex.org/W3003034928","https://openalex.org/W3035414587","https://openalex.org/W3097767678","https://openalex.org/W3130934138","https://openalex.org/W3202992230"],"related_works":["https://openalex.org/W4319317911","https://openalex.org/W3207384893","https://openalex.org/W4396668917","https://openalex.org/W3207046288","https://openalex.org/W4372264653","https://openalex.org/W4310842702","https://openalex.org/W4327662653","https://openalex.org/W2012686349","https://openalex.org/W2096239959","https://openalex.org/W4285330809"],"abstract_inverted_index":{"Hemispherical":[0,54],"photography":[1],"is":[2,94,125],"an":[3,30],"important":[4],"method":[5],"of":[6,56,120,132,136,139],"leaf":[7],"area":[8],"index":[9,22],"(LAI)":[10],"measurement,":[11],"but":[12],"the":[13,51,69,77,81,85,90,109,118,121,133],"intermediate":[14,52],"processes":[15],"such":[16],"as":[17,76],"image":[18,42,123],"segmentation":[19],"and":[20,58,65,68,124,129],"clumping":[21],"estimation":[23,110,131],"will":[24],"introduce":[25],"errors.":[26],"In":[27],"this":[28],"paper,":[29],"end-to-end":[31],"model":[32,111],"was":[33],"proposed":[34],"to":[35],"directly":[36],"estimate":[37,117],"LAI":[38,119,134],"from":[39,62],"a":[40],"hemispheric":[41,122],"based":[43,112],"on":[44,113],"GhostNet,":[45],"which":[46],"avoids":[47],"errors":[48],"introduced":[49],"by":[50],"processing.":[53],"images":[55],"crops":[57],"forest":[59],"vegetation":[60,140],"obtained":[61],"Shihezi,":[63],"Xinjiang":[64],"Xiong'an,":[66],"Hebei,":[67],"measured":[70],"values":[71],"using":[72],"LAI-2200":[73,82],"were":[74],"utilized":[75],"dataset.":[78],"Compared":[79],"with":[80,97],"measurement":[83],"results,":[84],"analysis":[86],"results":[87,106],"show":[88,107],"that":[89,108],"correlation":[91],"between":[92],"them":[93],"extremely":[95],"significant,":[96],"<tex":[98],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[99],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">$\\mathrm{R}^{2}=0.80,\\":[100],"\\text{RMSE}=0.65,\\":[101],"\\text{MAE}=0.46$</tex>":[102],".":[103],"The":[104],"experimental":[105],"GhostNet":[114],"can":[115],"accurately":[116],"suitable":[126],"for":[127],"timely":[128],"accurate":[130],"value":[135],"various":[137],"types":[138],"through":[141],"edge":[142],"devices.":[143]},"counts_by_year":[{"year":2023,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}