{"id":"https://openalex.org/W2900970566","doi":"https://doi.org/10.1109/igarss.2018.8518860","title":"Crops Classification from Sentinel-2A Multi-spectral Remote Sensing Images Based on Convolutional Neural Networks","display_name":"Crops Classification from Sentinel-2A Multi-spectral Remote Sensing Images Based on Convolutional Neural Networks","publication_year":2018,"publication_date":"2018-07-01","ids":{"openalex":"https://openalex.org/W2900970566","doi":"https://doi.org/10.1109/igarss.2018.8518860","mag":"2900970566"},"language":"en","primary_location":{"id":"doi:10.1109/igarss.2018.8518860","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2018.8518860","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2018 - 2018 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/A5031912762","display_name":"Zhuang Zhou","orcid":"https://orcid.org/0000-0001-6544-4858"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210086028","display_name":"Technology and Engineering Center for Space Utilization","ror":"https://ror.org/00cn03n83","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210086028"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhuang Zhou","raw_affiliation_strings":["Key Laboratory of Space Utilization, Chinese Academy of Sciences, Beijing"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Key Laboratory of Space Utilization, Chinese Academy of Sciences, Beijing","institution_ids":["https://openalex.org/I4210086028","https://openalex.org/I19820366"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111003697","display_name":"Shengyang Li","orcid":"https://orcid.org/0000-0002-9888-9869"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210086028","display_name":"Technology and Engineering Center for Space Utilization","ror":"https://ror.org/00cn03n83","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210086028"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shengyang Li","raw_affiliation_strings":["Key Laboratory of Space Utilization, Chinese Academy of Sciences, Beijing"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Key Laboratory of Space Utilization, Chinese Academy of Sciences, Beijing","institution_ids":["https://openalex.org/I4210086028","https://openalex.org/I19820366"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103167139","display_name":"Yuyang Shao","orcid":"https://orcid.org/0000-0001-7632-072X"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210086028","display_name":"Technology and Engineering Center for Space Utilization","ror":"https://ror.org/00cn03n83","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210086028"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuyang Shao","raw_affiliation_strings":["Key Laboratory of Space Utilization, Chinese Academy of Sciences, Beijing"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Key Laboratory of Space Utilization, Chinese Academy of Sciences, Beijing","institution_ids":["https://openalex.org/I4210086028","https://openalex.org/I19820366"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.9182,"has_fulltext":false,"cited_by_count":28,"citation_normalized_percentile":{"value":0.84994466,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"5300","last_page":"5303"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13890","display_name":"Remote Sensing and Land Use","score":0.9968000054359436,"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"}},"topics":[{"id":"https://openalex.org/T13890","display_name":"Remote Sensing and Land Use","score":0.9968000054359436,"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"}},{"id":"https://openalex.org/T10111","display_name":"Remote Sensing in Agriculture","score":0.9948999881744385,"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":0.9911999702453613,"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/convolutional-neural-network","display_name":"Convolutional neural network","score":0.757793664932251},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.7193189263343811},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6997561454772949},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6596254706382751},{"id":"https://openalex.org/keywords/land-cover","display_name":"Land cover","score":0.5441426634788513},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5399169921875},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.5349394679069519},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5265299081802368},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.4963303208351135},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.4827965497970581},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4659149944782257},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.43165484070777893},{"id":"https://openalex.org/keywords/remote-sensing-application","display_name":"Remote sensing application","score":0.41786572337150574},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4144083559513092},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.19238966703414917},{"id":"https://openalex.org/keywords/land-use","display_name":"Land use","score":0.15171504020690918},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.14756563305854797},{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.1365036964416504},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.11485838890075684},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.06107121706008911}],"concepts":[{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.757793664932251},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.7193189263343811},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6997561454772949},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6596254706382751},{"id":"https://openalex.org/C2780648208","wikidata":"https://www.wikidata.org/wiki/Q3001793","display_name":"Land cover","level":3,"score":0.5441426634788513},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5399169921875},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.5349394679069519},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5265299081802368},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.4963303208351135},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.4827965497970581},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4659149944782257},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.43165484070777893},{"id":"https://openalex.org/C183365957","wikidata":"https://www.wikidata.org/wiki/Q17140402","display_name":"Remote sensing application","level":3,"score":0.41786572337150574},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4144083559513092},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.19238966703414917},{"id":"https://openalex.org/C4792198","wikidata":"https://www.wikidata.org/wiki/Q1165944","display_name":"Land use","level":2,"score":0.15171504020690918},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.14756563305854797},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.1365036964416504},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.11485838890075684},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.06107121706008911},{"id":"https://openalex.org/C147176958","wikidata":"https://www.wikidata.org/wiki/Q77590","display_name":"Civil engineering","level":1,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss.2018.8518860","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2018.8518860","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7300000190734863,"id":"https://metadata.un.org/sdg/2","display_name":"Zero hunger"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":5,"referenced_works":["https://openalex.org/W2029342456","https://openalex.org/W2133941557","https://openalex.org/W2283002322","https://openalex.org/W2604086375","https://openalex.org/W2760566066"],"related_works":["https://openalex.org/W2952813363","https://openalex.org/W4360783045","https://openalex.org/W2963346891","https://openalex.org/W3176438653","https://openalex.org/W2770149305","https://openalex.org/W2972076240","https://openalex.org/W3167930666","https://openalex.org/W3014952856","https://openalex.org/W2964843961","https://openalex.org/W2043913960"],"abstract_inverted_index":{"Deep":[0],"learning":[1],"technology":[2],"such":[3],"as":[4],"convolutional":[5],"neural":[6],"networks":[7],"(CNN)":[8],"can":[9],"extract":[10],"the":[11,32,57,69,87,100,105],"distinguishable":[12],"and":[13,62],"representative":[14],"features":[15],"of":[16,34,40,60,72,89,97,108],"different":[17,98],"land":[18],"cover":[19],"from":[20,43,76],"remote":[21,45,79],"sensing":[22,46,80],"images":[23,47,81],"in":[24,31,67,82],"a":[25],"hierarchical":[26],"way":[27],"to":[28,115],"classify.":[29],"However,":[30],"field":[33],"agriculture,":[35],"there":[36],"are":[37],"few":[38],"application":[39],"crops":[41,73],"classification":[42,58],"multi-spectral":[44,78],"based":[48],"on":[49],"deep":[50],"learning.":[51],"In":[52],"this":[53],"context,":[54],"we":[55],"compared":[56],"methods":[59,92],"CNN":[61,109],"support":[63],"vector":[64],"machines":[65],"(SVM)":[66],"extracting":[68],"spatial":[70,95],"distribution":[71,96],"planting":[74],"area":[75],"Sentineal-2A":[77],"Yuanyang":[83],"county,":[84],"China.":[85],"For":[86],"region":[88],"study,":[90],"both":[91],"obtained":[93],"reasonable":[94],"crops,":[99],"verification":[101],"results":[102],"show":[103],"that":[104],"overall":[106],"accuracy":[107],"is":[110,113],"95.6%":[111],"which":[112],"superior":[114],"SVM.":[116]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":12},{"year":2020,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}