{"id":"https://openalex.org/W2897049417","doi":"https://doi.org/10.1109/mmsp.2018.8547095","title":"A Cloud Detection Algorithm for Remote Sensing Images Using Fully Convolutional Neural Networks","display_name":"A Cloud Detection Algorithm for Remote Sensing Images Using Fully Convolutional Neural Networks","publication_year":2018,"publication_date":"2018-08-01","ids":{"openalex":"https://openalex.org/W2897049417","doi":"https://doi.org/10.1109/mmsp.2018.8547095","mag":"2897049417"},"language":"en","primary_location":{"id":"doi:10.1109/mmsp.2018.8547095","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mmsp.2018.8547095","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE 20th International Workshop on Multimedia Signal Processing (MMSP)","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1810.05782","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5049307869","display_name":"Sorour Mohajerani","orcid":"https://orcid.org/0000-0001-6715-5064"},"institutions":[{"id":"https://openalex.org/I18014758","display_name":"Simon Fraser University","ror":"https://ror.org/0213rcc28","country_code":"CA","type":"education","lineage":["https://openalex.org/I18014758"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Sorour Mohajerani","raw_affiliation_strings":["School of Engineering, Science Simon Fraser University, Burnaby, BC, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Engineering, Science Simon Fraser University, Burnaby, BC, Canada","institution_ids":["https://openalex.org/I18014758"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047575491","display_name":"Thomas A. Krammer","orcid":null},"institutions":[{"id":"https://openalex.org/I18014758","display_name":"Simon Fraser University","ror":"https://ror.org/0213rcc28","country_code":"CA","type":"education","lineage":["https://openalex.org/I18014758"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Thomas A. Krammer","raw_affiliation_strings":["School of Engineering, Science Simon Fraser University, Burnaby, BC, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Engineering, Science Simon Fraser University, Burnaby, BC, Canada","institution_ids":["https://openalex.org/I18014758"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5011637244","display_name":"Parvaneh Saeedi","orcid":"https://orcid.org/0000-0002-7507-9986"},"institutions":[{"id":"https://openalex.org/I18014758","display_name":"Simon Fraser University","ror":"https://ror.org/0213rcc28","country_code":"CA","type":"education","lineage":["https://openalex.org/I18014758"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Parvaneh Saeedi","raw_affiliation_strings":["School of Engineering, Science Simon Fraser University, Burnaby, BC, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Engineering, Science Simon Fraser University, Burnaby, BC, Canada","institution_ids":["https://openalex.org/I18014758"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I18014758"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.16596614,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9986000061035156,"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":0.9986000061035156,"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/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9979000091552734,"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":0.9962999820709229,"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/jaccard-index","display_name":"Jaccard index","score":0.796811580657959},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7729207277297974},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7593397498130798},{"id":"https://openalex.org/keywords/cloud-computing","display_name":"Cloud computing","score":0.7292363047599792},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5670276880264282},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5519649982452393},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.5437586903572083},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.5338854193687439},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.48911505937576294},{"id":"https://openalex.org/keywords/precision-and-recall","display_name":"Precision and recall","score":0.4693717956542969},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.45937857031822205},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.45314937829971313},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4426543712615967},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.4164550006389618},{"id":"https://openalex.org/keywords/measure","display_name":"Measure (data warehouse)","score":0.4137084484100342},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.40711838006973267},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.368399053812027},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.0740450918674469}],"concepts":[{"id":"https://openalex.org/C203519979","wikidata":"https://www.wikidata.org/wiki/Q865360","display_name":"Jaccard index","level":3,"score":0.796811580657959},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7729207277297974},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7593397498130798},{"id":"https://openalex.org/C79974875","wikidata":"https://www.wikidata.org/wiki/Q483639","display_name":"Cloud computing","level":2,"score":0.7292363047599792},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5670276880264282},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5519649982452393},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.5437586903572083},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.5338854193687439},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.48911505937576294},{"id":"https://openalex.org/C81669768","wikidata":"https://www.wikidata.org/wiki/Q2359161","display_name":"Precision and recall","level":2,"score":0.4693717956542969},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.45937857031822205},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.45314937829971313},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4426543712615967},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.4164550006389618},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.4137084484100342},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.40711838006973267},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.368399053812027},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0740450918674469},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C59822182","wikidata":"https://www.wikidata.org/wiki/Q441","display_name":"Botany","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1109/mmsp.2018.8547095","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mmsp.2018.8547095","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE 20th International Workshop on Multimedia Signal Processing (MMSP)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:1810.05782","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1810.05782","pdf_url":"https://arxiv.org/pdf/1810.05782","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"},{"id":"mag:2897049417","is_oa":true,"landing_page_url":"http://export.arxiv.org/pdf/1810.05782","pdf_url":null,"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":"arXiv (Cornell University)","raw_type":null},{"id":"doi:10.48550/arxiv.1810.05782","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.1810.05782","pdf_url":null,"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":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1810.05782","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1810.05782","pdf_url":"https://arxiv.org/pdf/1810.05782","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":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2897049417.pdf","grobid_xml":"https://content.openalex.org/works/W2897049417.grobid-xml"},"referenced_works_count":15,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1533693043","https://openalex.org/W1864426711","https://openalex.org/W1901129140","https://openalex.org/W2025745000","https://openalex.org/W2068124105","https://openalex.org/W2166251851","https://openalex.org/W2464708700","https://openalex.org/W2605495192","https://openalex.org/W2607363228","https://openalex.org/W2765940700","https://openalex.org/W2951839332","https://openalex.org/W6631190155","https://openalex.org/W6637242042","https://openalex.org/W6639114710"],"related_works":["https://openalex.org/W2964060775","https://openalex.org/W2910279167","https://openalex.org/W2885322367","https://openalex.org/W2548491386","https://openalex.org/W2914834436","https://openalex.org/W3000843602","https://openalex.org/W2748715875","https://openalex.org/W3183435102","https://openalex.org/W2965921309","https://openalex.org/W3090939278","https://openalex.org/W3147951545","https://openalex.org/W3027738884","https://openalex.org/W3090823604","https://openalex.org/W3062344264","https://openalex.org/W2799078728","https://openalex.org/W3200189320","https://openalex.org/W2990032788","https://openalex.org/W3100667366","https://openalex.org/W2897887361","https://openalex.org/W2977941015"],"abstract_inverted_index":{"This":[0,19],"paper":[1],"presents":[2],"a":[3,23,39,44],"deep-learning":[4],"based":[5],"framework":[6,20],"for":[7],"addressing":[8],"the":[9,58,62,69,72,79,82,87,98],"problem":[10],"of":[11,32,35,55,61,71,81],"accurate":[12],"cloud":[13,36,83],"detection":[14],"in":[15,38,57],"remote":[16],"sensing":[17],"images.":[18],"benefits":[21],"from":[22],"Fully":[24],"Convolutional":[25],"Neural":[26],"Network":[27],"(FCN),":[28],"which":[29],"is":[30,48],"capable":[31],"pixel-level":[33],"labeling":[34],"regions":[37,54],"Landsat":[40],"8":[41],"image.":[42],"Also,":[43],"gradient-based":[45],"identification":[46,84],"approach":[47],"proposed":[49],"to":[50,89],"identify":[51],"and":[52,76,101,108],"exclude":[53],"snow/ice":[56],"ground":[59,94],"truths":[60],"training":[63],"set.":[64],"We":[65],"show":[66],"that":[67],"using":[68],"hybrid":[70],"two":[73],"methods":[74],"(threshold-based":[75],"deep-learning)":[77],"improves":[78],"performance":[80],"process":[85],"without":[86],"need":[88],"manually":[90],"correct":[91],"automatically":[92],"generated":[93],"truths.":[95],"In":[96],"average":[97],"Jaccard":[99],"index":[100],"recall":[102],"measure":[103],"are":[104],"improved":[105],"by":[106],"4.36%":[107],"3.62%,":[109],"respectively.":[110]},"counts_by_year":[],"updated_date":"2026-07-03T08:13:44.112507","created_date":"2022-08-02T00:00:00"}
