{"id":"https://openalex.org/W2784049706","doi":"https://doi.org/10.1109/icsai.2017.8248497","title":"Peanut planting area change monitoring from remote sensing images based on deep learning","display_name":"Peanut planting area change monitoring from remote sensing images based on deep learning","publication_year":2017,"publication_date":"2017-11-01","ids":{"openalex":"https://openalex.org/W2784049706","doi":"https://doi.org/10.1109/icsai.2017.8248497","mag":"2784049706"},"language":"en","primary_location":{"id":"doi:10.1109/icsai.2017.8248497","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icsai.2017.8248497","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 4th International Conference on Systems and Informatics (ICSAI)","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":null},"institutions":[{"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"]},{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"funder","lineage":["https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zhuang Zhou","raw_affiliation_strings":["Technology and Engineering Center for Space Utilization, Chinese Academy of Sciences, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Technology and Engineering Center for Space Utilization, Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210086028","https://openalex.org/I19820366"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5111003697","display_name":"Shengyang Li","orcid":"https://orcid.org/0000-0002-9888-9869"},"institutions":[{"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"]},{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"funder","lineage":["https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shengyang Li","raw_affiliation_strings":["Technology and Engineering Center for Space Utilization, Chinese Academy of Sciences, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Technology and Engineering Center for Space Utilization, Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210086028","https://openalex.org/I19820366"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5031912762"],"corresponding_institution_ids":["https://openalex.org/I19820366","https://openalex.org/I4210086028"],"apc_list":null,"apc_paid":null,"fwci":0.8395,"has_fulltext":false,"cited_by_count":13,"citation_normalized_percentile":{"value":0.72898163,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1358","last_page":"1362"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13890","display_name":"Remote Sensing and Land Use","score":0.9962000250816345,"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":0.9962000250816345,"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/T10689","display_name":"Remote-Sensing Image Classification","score":0.992900013923645,"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":0.968999981880188,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/multispectral-image","display_name":"Multispectral image","score":0.759084165096283},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6998673677444458},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6717734336853027},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6417844295501709},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.5799587965011597},{"id":"https://openalex.org/keywords/multispectral-pattern-recognition","display_name":"Multispectral pattern recognition","score":0.5697413682937622},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.5082425475120544},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.504597544670105},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.5040492415428162},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4698347747325897},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.46442118287086487},{"id":"https://openalex.org/keywords/cohens-kappa","display_name":"Cohen's kappa","score":0.4512178897857666},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.41869229078292847},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3354571461677551},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.2059139907360077},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.20335212349891663},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.16366329789161682}],"concepts":[{"id":"https://openalex.org/C173163844","wikidata":"https://www.wikidata.org/wiki/Q1761440","display_name":"Multispectral image","level":2,"score":0.759084165096283},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6998673677444458},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6717734336853027},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6417844295501709},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.5799587965011597},{"id":"https://openalex.org/C104541649","wikidata":"https://www.wikidata.org/wiki/Q6935090","display_name":"Multispectral pattern recognition","level":3,"score":0.5697413682937622},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.5082425475120544},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.504597544670105},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.5040492415428162},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4698347747325897},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.46442118287086487},{"id":"https://openalex.org/C163864269","wikidata":"https://www.wikidata.org/wiki/Q1107106","display_name":"Cohen's kappa","level":2,"score":0.4512178897857666},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.41869229078292847},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3354571461677551},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2059139907360077},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.20335212349891663},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.16366329789161682}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icsai.2017.8248497","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icsai.2017.8248497","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 4th International Conference on Systems and Informatics (ICSAI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/2","score":0.75,"display_name":"Zero hunger"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W1984670836","https://openalex.org/W2029316659","https://openalex.org/W2029342456","https://openalex.org/W2068470708","https://openalex.org/W2112796928","https://openalex.org/W2133941557","https://openalex.org/W2156163116","https://openalex.org/W2267317359","https://openalex.org/W2283002322","https://openalex.org/W2404854973","https://openalex.org/W2494585756","https://openalex.org/W2604086375","https://openalex.org/W2760566066"],"related_works":["https://openalex.org/W2128126485","https://openalex.org/W1995889410","https://openalex.org/W4382563209","https://openalex.org/W2124952510","https://openalex.org/W1752760603","https://openalex.org/W2777937183","https://openalex.org/W2108633818","https://openalex.org/W1517037119","https://openalex.org/W2126922921","https://openalex.org/W1634492240"],"abstract_inverted_index":{"As":[0],"a":[1,19,42,56],"powerful":[2],"image":[3],"processing":[4],"technology,":[5],"deep":[6],"learning":[7],"can":[8,37],"extract":[9],"representative":[10],"and":[11,75,145],"distinguishing":[12],"features":[13,33],"from":[14,34,69,139],"remote":[15,72,150],"sensing":[16,73,151],"images":[17,74,141],"in":[18,77,80,118,148],"hierarchical":[20],"way.":[21],"Considering":[22],"the":[23,27,30,35,47,88,95,108,126],"spectral":[24,32],"information":[25],"as":[26],"network":[28,36],"input,":[29],"output":[31],"be":[38],"directly":[39],"fed":[40],"into":[41],"subsequent":[43],"classifier":[44],"to":[45],"realize":[46],"classification":[48],"based":[49,58],"on":[50,59],"pixel":[51],"level.":[52],"This":[53],"paper":[54],"presents":[55],"method":[57],"convolutional":[60],"neural":[61],"networks":[62],"(CNN)":[63],"for":[64,136],"peanut":[65,109],"planting":[66,110],"area":[67,111],"extracting":[68],"Landsat-8":[70],"multispectral":[71,140],"applied":[76],"Zhenyang":[78],"county":[79,114],"Henan":[81],"province.":[82],"The":[83,131],"experimental":[84],"results":[85],"show":[86,106],"that":[87,107],"architecture":[89],"of":[90,112,133],"CNN":[91,135],"achieve":[92],"good":[93],"performance,":[94],"overall":[96],"accuracy":[97],"is":[98,102],"96.42%,":[99],"kappa":[100],"efficient":[101],"0.944.":[103],"Statistical":[104],"calculations":[105],"Zhengyang":[113],"increased":[115],"by":[116],"12.1%":[117],"2017":[119],"compared":[120],"with":[121,125],"2015":[122],"which":[123],"consistent":[124],"current":[127],"agricultural":[128,149],"subsidy":[129],"policy.":[130],"exploration":[132],"using":[134],"crop":[137],"recognition":[138],"has":[142],"excellent":[143],"performance":[144],"great":[146],"potential":[147],"classification.":[152]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":4},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}