{"id":"https://openalex.org/W3048128316","doi":"https://doi.org/10.1109/lgrs.2020.3020098","title":"Learning CNN Filters From User-Drawn Image Markers for Coconut-Tree Image Classification","display_name":"Learning CNN Filters From User-Drawn Image Markers for Coconut-Tree Image Classification","publication_year":2020,"publication_date":"2020-09-11","ids":{"openalex":"https://openalex.org/W3048128316","doi":"https://doi.org/10.1109/lgrs.2020.3020098","mag":"3048128316"},"language":"en","primary_location":{"id":"doi:10.1109/lgrs.2020.3020098","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2020.3020098","pdf_url":null,"source":{"id":"https://openalex.org/S126920919","display_name":"IEEE Geoscience and Remote Sensing Letters","issn_l":"1545-598X","issn":["1545-598X","1558-0571"],"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 Geoscience and Remote Sensing Letters","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2008.03549","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5049489283","display_name":"Italos Estilon de Souza","orcid":"https://orcid.org/0000-0002-1088-0289"},"institutions":[{"id":"https://openalex.org/I181391015","display_name":"Universidade Estadual de Campinas (UNICAMP)","ror":"https://ror.org/04wffgt70","country_code":"BR","type":"education","lineage":["https://openalex.org/I181391015"]}],"countries":["BR"],"is_corresponding":true,"raw_author_name":"Italos Estilon de Souza","raw_affiliation_strings":["Institute of Computing, State University of Campinas, Campinas, Brazil"],"affiliations":[{"raw_affiliation_string":"Institute of Computing, State University of Campinas, Campinas, Brazil","institution_ids":["https://openalex.org/I181391015"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5015267493","display_name":"Alexandre X. Falc\u00e3o","orcid":"https://orcid.org/0000-0002-2914-5380"},"institutions":[{"id":"https://openalex.org/I181391015","display_name":"Universidade Estadual de Campinas (UNICAMP)","ror":"https://ror.org/04wffgt70","country_code":"BR","type":"education","lineage":["https://openalex.org/I181391015"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Alexandre Xavier Falcao","raw_affiliation_strings":["Institute of Computing, State University of Campinas, Campinas, Brazil"],"affiliations":[{"raw_affiliation_string":"Institute of Computing, State University of Campinas, Campinas, Brazil","institution_ids":["https://openalex.org/I181391015"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5049489283"],"corresponding_institution_ids":["https://openalex.org/I181391015"],"apc_list":null,"apc_paid":null,"fwci":2.233,"has_fulltext":false,"cited_by_count":31,"citation_normalized_percentile":{"value":0.8660737,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":"19","issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.998199999332428,"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"}},"topics":[{"id":"https://openalex.org/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.998199999332428,"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"}},{"id":"https://openalex.org/T10616","display_name":"Smart Agriculture and AI","score":0.992900013923645,"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/T12703","display_name":"Oil Palm Production and Sustainability","score":0.9894000291824341,"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/convolutional-neural-network","display_name":"Convolutional neural network","score":0.8065343499183655},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.797580361366272},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.747639536857605},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6750991344451904},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.6535705327987671},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.6343256235122681},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5519464015960693},{"id":"https://openalex.org/keywords/tree","display_name":"Tree (set theory)","score":0.5169667601585388},{"id":"https://openalex.org/keywords/aerial-image","display_name":"Aerial image","score":0.46520501375198364},{"id":"https://openalex.org/keywords/backpropagation","display_name":"Backpropagation","score":0.43601712584495544},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4279511272907257},{"id":"https://openalex.org/keywords/image-processing","display_name":"Image processing","score":0.4110095202922821},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3490472137928009},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.32469090819358826},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.2929086685180664},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.09672549366950989}],"concepts":[{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.8065343499183655},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.797580361366272},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.747639536857605},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6750991344451904},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.6535705327987671},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.6343256235122681},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5519464015960693},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.5169667601585388},{"id":"https://openalex.org/C2776429412","wikidata":"https://www.wikidata.org/wiki/Q4688011","display_name":"Aerial image","level":3,"score":0.46520501375198364},{"id":"https://openalex.org/C155032097","wikidata":"https://www.wikidata.org/wiki/Q798503","display_name":"Backpropagation","level":3,"score":0.43601712584495544},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4279511272907257},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.4110095202922821},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3490472137928009},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.32469090819358826},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2929086685180664},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.09672549366950989},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/lgrs.2020.3020098","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2020.3020098","pdf_url":null,"source":{"id":"https://openalex.org/S126920919","display_name":"IEEE Geoscience and Remote Sensing Letters","issn_l":"1545-598X","issn":["1545-598X","1558-0571"],"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 Geoscience and Remote Sensing Letters","raw_type":"journal-article"},{"id":"pmh:oai:arXiv.org:2008.03549","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2008.03549","pdf_url":"https://arxiv.org/pdf/2008.03549","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2008.03549","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2008.03549","pdf_url":"https://arxiv.org/pdf/2008.03549","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2314765024","display_name":null,"funder_award_id":"4600583791","funder_id":"https://openalex.org/F4320323909","funder_display_name":"Ag\u00eancia Nacional do Petr\u00f3leo, G\u00e1s Natural e Biocombust\u00edveis"},{"id":"https://openalex.org/G4312984384","display_name":null,"funder_award_id":"2014/12236-1","funder_id":"https://openalex.org/F4320320997","funder_display_name":"Funda\u00e7\u00e3o de Amparo \u00e0 Pesquisa do Estado de S\u00e3o Paulo"},{"id":"https://openalex.org/G6309692610","display_name":null,"funder_award_id":"4600556376","funder_id":"https://openalex.org/F4320323909","funder_display_name":"Ag\u00eancia Nacional do Petr\u00f3leo, G\u00e1s Natural e Biocombust\u00edveis"},{"id":"https://openalex.org/G8901163871","display_name":null,"funder_award_id":"303808/2018-7","funder_id":"https://openalex.org/F4320322025","funder_display_name":"Conselho Nacional de Desenvolvimento Cient\u00edfico e Tecnol\u00f3gico"}],"funders":[{"id":"https://openalex.org/F4320320997","display_name":"Funda\u00e7\u00e3o de Amparo \u00e0 Pesquisa do Estado de S\u00e3o Paulo","ror":"https://ror.org/02ddkpn78"},{"id":"https://openalex.org/F4320322025","display_name":"Conselho Nacional de Desenvolvimento Cient\u00edfico e Tecnol\u00f3gico","ror":"https://ror.org/03swz6y49"},{"id":"https://openalex.org/F4320323909","display_name":"Ag\u00eancia Nacional do Petr\u00f3leo, G\u00e1s Natural e Biocombust\u00edveis","ror":"https://ror.org/00phthq42"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W1686810756","https://openalex.org/W2060300932","https://openalex.org/W2101234009","https://openalex.org/W2118585731","https://openalex.org/W2187089797","https://openalex.org/W2480078828","https://openalex.org/W2512304460","https://openalex.org/W2515306179","https://openalex.org/W2557283755","https://openalex.org/W2732036031","https://openalex.org/W2760340275","https://openalex.org/W2773356293","https://openalex.org/W2787614951","https://openalex.org/W2794021703","https://openalex.org/W2887636778","https://openalex.org/W2890072312","https://openalex.org/W2890554472","https://openalex.org/W2965446359","https://openalex.org/W2978741124","https://openalex.org/W2981731882","https://openalex.org/W2989460518","https://openalex.org/W3034278117","https://openalex.org/W3041378136","https://openalex.org/W6637373629","https://openalex.org/W6675354045","https://openalex.org/W6677656871"],"related_works":["https://openalex.org/W4239286941","https://openalex.org/W2088845016","https://openalex.org/W589102260","https://openalex.org/W1966421350","https://openalex.org/W1868434454","https://openalex.org/W4366985237","https://openalex.org/W2810569973","https://openalex.org/W2128396103","https://openalex.org/W3147207884","https://openalex.org/W1836423264"],"abstract_inverted_index":{"Identifying":[0],"species":[1],"of":[2,17,23,98,110,124,143,165,171],"trees":[3,24],"in":[4,25,47,131],"aerial":[5,26,167],"images":[6,27,100,112,168],"is":[7,28],"essential":[8],"for":[9,76,83],"land-use":[10],"classification,":[11],"plantation":[12],"monitoring,":[13],"and":[14,31,79,87,141,156],"impact":[15],"assessment":[16],"natural":[18],"disasters.":[19],"The":[20,119],"manual":[21,60],"identification":[22],"tedious,":[29],"costly,":[30],"error-prone,":[32],"so":[33],"automatic":[34],"classification":[35,49,164],"methods":[36],"are":[37],"necessary.":[38],"Convolutional":[39],"neural":[40],"network":[41],"(CNN)":[42],"models":[43,56],"have":[44],"well":[45],"succeeded":[46],"image":[48,132],"applications":[50],"from":[51,128],"different":[52],"domains.":[53],"However,":[54],"CNN":[55,72,175],"usually":[57],"require":[58],"intensive":[59],"annotation":[61],"to":[62,101,113],"create":[63],"large":[64],"training":[65,145],"sets.":[66],"One":[67],"may":[68],"conceptually":[69],"divide":[70],"a":[71,91,95],"into":[73],"convolutional":[74,126],"layers":[75,82],"feature":[77,84,105],"extraction":[78],"fully":[80,116],"connected":[81,117],"space":[85],"reduction":[86],"classification.":[88],"We":[89],"present":[90],"method":[92,120],"that":[93,134],"needs":[94],"minimal":[96],"set":[97],"user-selected":[99],"train":[102,114],"the":[103,108,115,122,144,162,172],"CNN\u2019s":[104],"extractor,":[106],"reducing":[107],"number":[109],"required":[111],"layers.":[118],"learns":[121],"filters":[123],"each":[125],"layer":[127],"user-drawn":[129],"markers":[130],"regions":[133],"discriminate":[135],"classes,":[136],"allowing":[137],"better":[138],"user":[139],"control":[140],"understanding":[142],"process.":[146],"It":[147],"does":[148],"not":[149],"rely":[150],"on":[151,154,161],"optimization":[152],"based":[153],"backpropagation,":[155],"we":[157],"demonstrate":[158],"its":[159],"advantages":[160],"binary":[163],"coconut-tree":[166],"against":[169],"one":[170],"most":[173],"popular":[174],"models.":[176]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":13},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":5},{"year":2020,"cited_by_count":2}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}
