{"id":"https://openalex.org/W3016244469","doi":"https://doi.org/10.1109/tgrs.2020.2982064","title":"Joint Classification of Hyperspectral and LiDAR Data Using Hierarchical Random Walk and Deep CNN Architecture","display_name":"Joint Classification of Hyperspectral and LiDAR Data Using Hierarchical Random Walk and Deep CNN Architecture","publication_year":2020,"publication_date":"2020-04-07","ids":{"openalex":"https://openalex.org/W3016244469","doi":"https://doi.org/10.1109/tgrs.2020.2982064","mag":"3016244469"},"language":"en","primary_location":{"id":"doi:10.1109/tgrs.2020.2982064","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2020.2982064","pdf_url":null,"source":{"id":"https://openalex.org/S111326731","display_name":"IEEE Transactions on Geoscience and Remote Sensing","issn_l":"0196-2892","issn":["0196-2892","1558-0644"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Geoscience and Remote Sensing","raw_type":"journal-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/A5001489670","display_name":"Xudong Zhao","orcid":"https://orcid.org/0000-0002-6942-136X"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]},{"id":"https://openalex.org/I32597200","display_name":"Ghent University","ror":"https://ror.org/00cv9y106","country_code":"BE","type":"education","lineage":["https://openalex.org/I32597200"]}],"countries":["BE","CN"],"is_corresponding":true,"raw_author_name":"Xudong Zhao","raw_affiliation_strings":["Image Processing and Interpretation, IMEC Research Group, Ghent University, Ghent, Belgium","School of Information and Electronics, Beijing Institute of Technology, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Image Processing and Interpretation, IMEC Research Group, Ghent University, Ghent, Belgium","institution_ids":["https://openalex.org/I32597200"]},{"raw_affiliation_string":"School of Information and Electronics, Beijing Institute of Technology, Beijing, China","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067803447","display_name":"Ran Tao","orcid":"https://orcid.org/0000-0002-5243-7189"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ran Tao","raw_affiliation_strings":["School of Information and Electronics, Beijing Institute of Technology, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Information and Electronics, Beijing Institute of Technology, Beijing, China","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100317994","display_name":"Wei Li","orcid":"https://orcid.org/0000-0001-7015-7335"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Li","raw_affiliation_strings":["School of Information and Electronics, Beijing Institute of Technology, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Information and Electronics, Beijing Institute of Technology, Beijing, China","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015155189","display_name":"Heng-Chao Li","orcid":"https://orcid.org/0000-0002-9735-570X"},"institutions":[{"id":"https://openalex.org/I4800084","display_name":"Southwest Jiaotong University","ror":"https://ror.org/00hn7w693","country_code":"CN","type":"education","lineage":["https://openalex.org/I4800084"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Heng-Chao Li","raw_affiliation_strings":["School of Information Science and Technology, Southwest Jiaotong University, Chengdu, China"],"affiliations":[{"raw_affiliation_string":"School of Information Science and Technology, Southwest Jiaotong University, Chengdu, China","institution_ids":["https://openalex.org/I4800084"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033017179","display_name":"Qian Du","orcid":"https://orcid.org/0000-0001-8354-7500"},"institutions":[{"id":"https://openalex.org/I99041443","display_name":"Mississippi State University","ror":"https://ror.org/0432jq872","country_code":"US","type":"education","lineage":["https://openalex.org/I4210141039","https://openalex.org/I99041443"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Qian Du","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Mississippi State University, Starkville, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Mississippi State University, Starkville, USA","institution_ids":["https://openalex.org/I99041443"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015582924","display_name":"Wenzhi Liao","orcid":"https://orcid.org/0000-0002-2183-0324"},"institutions":[{"id":"https://openalex.org/I32597200","display_name":"Ghent University","ror":"https://ror.org/00cv9y106","country_code":"BE","type":"education","lineage":["https://openalex.org/I32597200"]},{"id":"https://openalex.org/I68522396","display_name":"Flemish Institute for Technological Research","ror":"https://ror.org/04gq0w522","country_code":"BE","type":"facility","lineage":["https://openalex.org/I68522396"]}],"countries":["BE"],"is_corresponding":false,"raw_author_name":"Wenzhi Liao","raw_affiliation_strings":["Image Processing and Interpretation, IMEC Research Group, Ghent University, Ghent, Belgium","Sustainable Materials Management, Flemish Institute for Technological Research (VITO), Mol, Belgium"],"affiliations":[{"raw_affiliation_string":"Image Processing and Interpretation, IMEC Research Group, Ghent University, Ghent, Belgium","institution_ids":["https://openalex.org/I32597200"]},{"raw_affiliation_string":"Sustainable Materials Management, Flemish Institute for Technological Research (VITO), Mol, Belgium","institution_ids":["https://openalex.org/I68522396"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5071483672","display_name":"Wilfried Philips","orcid":"https://orcid.org/0000-0003-4456-4353"},"institutions":[{"id":"https://openalex.org/I32597200","display_name":"Ghent University","ror":"https://ror.org/00cv9y106","country_code":"BE","type":"education","lineage":["https://openalex.org/I32597200"]}],"countries":["BE"],"is_corresponding":false,"raw_author_name":"Wilfried Philips","raw_affiliation_strings":["Image Processing and Interpretation, IMEC Research Group, Ghent University, Ghent, Belgium"],"affiliations":[{"raw_affiliation_string":"Image Processing and Interpretation, IMEC Research Group, Ghent University, Ghent, Belgium","institution_ids":["https://openalex.org/I32597200"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5001489670"],"corresponding_institution_ids":["https://openalex.org/I125839683","https://openalex.org/I32597200"],"apc_list":null,"apc_paid":null,"fwci":26.3802,"has_fulltext":false,"cited_by_count":227,"citation_normalized_percentile":{"value":0.99655608,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":100},"biblio":{"volume":"58","issue":"10","first_page":"7355","last_page":"7370"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":1.0,"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"}},"topics":[{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":1.0,"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"}},{"id":"https://openalex.org/T10111","display_name":"Remote Sensing in Agriculture","score":0.9927999973297119,"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/T13890","display_name":"Remote Sensing and Land Use","score":0.9886000156402588,"subfield":{"id":"https://openalex.org/subfields/1902","display_name":"Atmospheric Science"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/lidar","display_name":"Lidar","score":0.8110605478286743},{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.7660635113716125},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7461720108985901},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.653192400932312},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5864396095275879},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.5748164653778076},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5372270345687866},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.535143256187439},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.5185468792915344},{"id":"https://openalex.org/keywords/ranging","display_name":"Ranging","score":0.4522439241409302},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.4401703178882599},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.4396836757659912},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.4118748605251312},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.10648471117019653},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.09268110990524292}],"concepts":[{"id":"https://openalex.org/C51399673","wikidata":"https://www.wikidata.org/wiki/Q504027","display_name":"Lidar","level":2,"score":0.8110605478286743},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.7660635113716125},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7461720108985901},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.653192400932312},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5864396095275879},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.5748164653778076},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5372270345687866},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.535143256187439},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.5185468792915344},{"id":"https://openalex.org/C115051666","wikidata":"https://www.wikidata.org/wiki/Q6522493","display_name":"Ranging","level":2,"score":0.4522439241409302},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.4401703178882599},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.4396836757659912},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.4118748605251312},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.10648471117019653},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.09268110990524292},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tgrs.2020.2982064","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2020.2982064","pdf_url":null,"source":{"id":"https://openalex.org/S111326731","display_name":"IEEE Transactions on Geoscience and Remote Sensing","issn_l":"0196-2892","issn":["0196-2892","1558-0644"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Geoscience and Remote Sensing","raw_type":"journal-article"},{"id":"pmh:oai:archive.ugent.be:8658091","is_oa":false,"landing_page_url":"http://hdl.handle.net/1854/LU-8658091","pdf_url":null,"source":{"id":"https://openalex.org/S4306400478","display_name":"Ghent University Academic Bibliography (Ghent University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I32597200","host_organization_name":"Ghent University","host_organization_lineage":["https://openalex.org/I32597200"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"ISSN: 1558-0644","raw_type":"info:eu-repo/semantics/publishedVersion"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities","score":0.7799999713897705}],"awards":[{"id":"https://openalex.org/G1023919524","display_name":null,"funder_award_id":", Grant","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G1231421488","display_name":null,"funder_award_id":"under","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3317480652","display_name":null,"funder_award_id":"Science","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G37568934","display_name":null,"funder_award_id":"Grant","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G391238517","display_name":null,"funder_award_id":", and","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3935756157","display_name":null,"funder_award_id":"142100","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G4417359500","display_name":null,"funder_award_id":"U1833203","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G4639895821","display_name":null,"funder_award_id":"61421001","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5994120800","display_name":null,"funder_award_id":"Natural","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6501339799","display_name":null,"funder_award_id":"61571033","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7394983629","display_name":null,"funder_award_id":"61922013","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G83685938","display_name":null,"funder_award_id":"1833203","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8589651859","display_name":null,"funder_award_id":"China","funder_id":"https://openalex.org/F4320322725","funder_display_name":"China Scholarship Council"},{"id":"https://openalex.org/G8898889987","display_name":null,"funder_award_id":"91638201","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320322725","display_name":"China Scholarship Council","ror":"https://ror.org/04atp4p48"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":58,"referenced_works":["https://openalex.org/W1494131642","https://openalex.org/W1497089125","https://openalex.org/W1522301498","https://openalex.org/W1625021374","https://openalex.org/W1654063000","https://openalex.org/W1836465849","https://openalex.org/W1990077509","https://openalex.org/W1997565609","https://openalex.org/W2008056655","https://openalex.org/W2011085793","https://openalex.org/W2029992428","https://openalex.org/W2059438067","https://openalex.org/W2067874135","https://openalex.org/W2092869901","https://openalex.org/W2103094532","https://openalex.org/W2104269704","https://openalex.org/W2111072639","https://openalex.org/W2119897980","https://openalex.org/W2125637308","https://openalex.org/W2127152713","https://openalex.org/W2131864940","https://openalex.org/W2157853947","https://openalex.org/W2220312492","https://openalex.org/W2257036858","https://openalex.org/W2320846209","https://openalex.org/W2412782625","https://openalex.org/W2519307493","https://openalex.org/W2548791488","https://openalex.org/W2565258258","https://openalex.org/W2589453516","https://openalex.org/W2600746131","https://openalex.org/W2606929568","https://openalex.org/W2611452721","https://openalex.org/W2614256707","https://openalex.org/W2719511702","https://openalex.org/W2745252301","https://openalex.org/W2751033422","https://openalex.org/W2765739551","https://openalex.org/W2792083654","https://openalex.org/W2888119354","https://openalex.org/W2889637463","https://openalex.org/W2890022946","https://openalex.org/W2890133123","https://openalex.org/W2896847173","https://openalex.org/W2898381489","https://openalex.org/W2901461790","https://openalex.org/W2940513807","https://openalex.org/W2949117887","https://openalex.org/W2963342403","https://openalex.org/W2963446712","https://openalex.org/W2964121744","https://openalex.org/W3004480865","https://openalex.org/W3098551073","https://openalex.org/W3103753223","https://openalex.org/W3104795559","https://openalex.org/W6631190155","https://openalex.org/W6638667902","https://openalex.org/W6657934682"],"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/W2594043982","https://openalex.org/W3036493597"],"abstract_inverted_index":{"Earth":[0],"observation":[1],"using":[2,39],"multisensor":[3,148],"data":[4,20,36,85,151,180],"is":[5,37,58,71,135],"drawing":[6],"increasing":[7],"attention.":[8],"Fusing":[9],"remotely":[10],"sensed":[11],"hyperspectral":[12,32],"imagery":[13,33],"and":[14,17,34,64,86],"light":[15],"detection":[16],"ranging":[18],"(LiDAR)":[19],"helps":[21],"to":[22,61,73],"increase":[23],"application":[24],"performance.":[25],"In":[26,47],"this":[27],"article,":[28],"joint":[29],"classification":[30,133],"of":[31,91,104,118,129,172,177,190,196],"LiDAR":[35,84],"investigated":[38],"an":[40,170,188,194],"effective":[41],"hierarchical":[42,97],"random":[43,98],"walk":[44,99],"network":[45,55],"(HRWN).":[46],"the":[48,75,88,95,101,115,126,139,154,164,175,183],"proposed":[49,72,155,184],"HRWN,":[50],"a":[51,132],"dual-tunnel":[52,105],"convolutional":[53],"neural":[54],"(CNN)":[56],"architecture":[57],"first":[59],"developed":[60],"capture":[62,74],"spectral":[63],"spatial":[65,89,123],"features.":[66],"A":[67],"pixelwise":[68,112],"affinity":[69,113],"branch":[70],"relationships":[76],"between":[77],"classes":[78],"with":[79,145],"different":[80],"elevation":[81],"information":[82],"from":[83],"confirm":[87],"contrast":[90],"classification.":[92],"Then":[93],"in":[94,125,193],"designed":[96],"layer,":[100],"predicted":[102],"distribution":[103],"CNN":[106,167],"serves":[107],"as":[108],"global":[109],"prior":[110],"while":[111,182],"reflects":[114],"local":[116],"similarity":[117],"pixel":[119],"pairs,":[120],"which":[121],"enforce":[122],"consistency":[124],"deeper":[127],"layers":[128],"networks.":[130],"Finally,":[131],"map":[134],"obtained":[136],"by":[137],"calculating":[138],"probability":[140],"distribution.":[141],"Experimental":[142],"results":[143],"validated":[144],"three":[146],"real":[147],"remote":[149],"sensing":[150],"demonstrate":[152],"that":[153],"HRWN":[156,185],"significantly":[157],"outperforms":[158],"other":[159],"state-of-the-art":[160],"methods.":[161],"For":[162],"example,":[163],"two":[165],"branches":[166],"classifier":[168,186],"achieves":[169],"accuracy":[171,189],"88.91%":[173],"on":[174],"University":[176],"Houston":[178],"campus":[179],"set,":[181],"obtains":[187],"93.61%,":[191],"resulting":[192],"improvement":[195],"approximately":[197],"5%.":[198]},"counts_by_year":[{"year":2026,"cited_by_count":7},{"year":2025,"cited_by_count":33},{"year":2024,"cited_by_count":49},{"year":2023,"cited_by_count":32},{"year":2022,"cited_by_count":50},{"year":2021,"cited_by_count":49},{"year":2020,"cited_by_count":7}],"updated_date":"2026-04-16T08:26:57.006410","created_date":"2025-10-10T00:00:00"}
