{"id":"https://openalex.org/W2986013500","doi":"https://doi.org/10.1109/igarss.2019.8900123","title":"Deep Learning for the Classification of Sentinel-2 Image Time Series","display_name":"Deep Learning for the Classification of Sentinel-2 Image Time Series","publication_year":2019,"publication_date":"2019-07-01","ids":{"openalex":"https://openalex.org/W2986013500","doi":"https://doi.org/10.1109/igarss.2019.8900123","mag":"2986013500"},"language":"en","primary_location":{"id":"doi:10.1109/igarss.2019.8900123","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2019.8900123","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-article"},"type":"conference-paper","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/A5032732048","display_name":"Charlotte Pelletier","orcid":"https://orcid.org/0000-0002-4652-7778"},"institutions":[{"id":"https://openalex.org/I56590836","display_name":"Monash University","ror":"https://ror.org/02bfwt286","country_code":"AU","type":"education","lineage":["https://openalex.org/I56590836"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Charlotte Pelletier","raw_affiliation_strings":["Faculty of Information Technology, Monash University, Melbourne, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Faculty of Information Technology, Monash University, Melbourne, Australia","institution_ids":["https://openalex.org/I56590836"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058054791","display_name":"Geoffrey I. Webb","orcid":"https://orcid.org/0000-0001-9963-5169"},"institutions":[{"id":"https://openalex.org/I56590836","display_name":"Monash University","ror":"https://ror.org/02bfwt286","country_code":"AU","type":"education","lineage":["https://openalex.org/I56590836"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Geoffrey I. Webb","raw_affiliation_strings":["Faculty of Information Technology, Monash University, Melbourne, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Faculty of Information Technology, Monash University, Melbourne, Australia","institution_ids":["https://openalex.org/I56590836"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5005781957","display_name":"Fran\u00e7ois Petitjean","orcid":"https://orcid.org/0000-0001-5334-3574"},"institutions":[{"id":"https://openalex.org/I56590836","display_name":"Monash University","ror":"https://ror.org/02bfwt286","country_code":"AU","type":"education","lineage":["https://openalex.org/I56590836"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Francois Petitjean","raw_affiliation_strings":["Faculty of Information Technology, Monash University, Melbourne, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Faculty of Information Technology, Monash University, Melbourne, Australia","institution_ids":["https://openalex.org/I56590836"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I56590836"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":34,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"461","last_page":"464"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10111","display_name":"Remote Sensing in Agriculture","score":0.9958999752998352,"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"}},"topics":[{"id":"https://openalex.org/T10111","display_name":"Remote Sensing in Agriculture","score":0.9958999752998352,"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":0.9861000180244446,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9742000102996826,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7234647870063782},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7198244333267212},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6745606660842896},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6561836004257202},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5995718836784363},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.5824054479598999},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.5707563757896423},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.559291660785675},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.5194315910339355},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.5062064528465271},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.43855881690979004},{"id":"https://openalex.org/keywords/satellite","display_name":"Satellite","score":0.4231109023094177},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3576183617115021},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.1036171019077301},{"id":"https://openalex.org/keywords/cartography","display_name":"Cartography","score":0.07722514867782593}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7234647870063782},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7198244333267212},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6745606660842896},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6561836004257202},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5995718836784363},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.5824054479598999},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.5707563757896423},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.559291660785675},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.5194315910339355},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.5062064528465271},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.43855881690979004},{"id":"https://openalex.org/C19269812","wikidata":"https://www.wikidata.org/wiki/Q26540","display_name":"Satellite","level":2,"score":0.4231109023094177},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3576183617115021},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.1036171019077301},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.07722514867782593},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss.2019.8900123","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2019.8900123","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Life in Land","id":"https://metadata.un.org/sdg/15","score":0.75}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W1974289021","https://openalex.org/W2056435747","https://openalex.org/W2098676252","https://openalex.org/W2163605009","https://openalex.org/W2261059368","https://openalex.org/W2307094448","https://openalex.org/W2402144811","https://openalex.org/W2531168480","https://openalex.org/W2551393996","https://openalex.org/W2581906016","https://openalex.org/W2589453516","https://openalex.org/W2737391801","https://openalex.org/W2742878349","https://openalex.org/W2886493749","https://openalex.org/W2896998982","https://openalex.org/W2911964244","https://openalex.org/W2953384591","https://openalex.org/W2963131120","https://openalex.org/W6684191040","https://openalex.org/W6713134421","https://openalex.org/W6755893616","https://openalex.org/W6756169136"],"related_works":["https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W4372048956","https://openalex.org/W3193565141","https://openalex.org/W3133861977","https://openalex.org/W4206989953","https://openalex.org/W4283776244","https://openalex.org/W3008584592","https://openalex.org/W3167935049","https://openalex.org/W3029198973"],"abstract_inverted_index":{"Satellite":[0],"image":[1],"time":[2,71,102],"series":[3],"(SITS)":[4],"have":[5,24,41,148],"proven":[6],"to":[7,73,111],"be":[8,139],"essential":[9],"for":[10,45,50,68,80,142],"accurate":[11],"and":[12,88,108],"up-to-date":[13],"land":[14],"cover":[15],"mapping":[16],"over":[17,118],"large":[18,98,143],"areas.":[19],"Most":[20],"works":[21],"about":[22],"SITS":[23],"focused":[25],"on":[26],"the":[27,69,74,123,130],"use":[28],"of":[29,126],"traditional":[30],"classification":[31],"algorithms":[32,40,79],"such":[33,58],"as":[34,59,146],"Random":[35],"Forests":[36],"(RFs).":[37],"Deep":[38],"learning":[39,78],"been":[42],"very":[43],"successful":[44],"supervised":[46],"tasks,":[47],"in":[48],"particular":[49],"data":[51],"that":[52,128,136],"exhibit":[53],"a":[54,97],"structure":[55],"between":[56],"attributes,":[57],"space":[60],"or":[61],"time.":[62],"In":[63],"this":[64],"work,":[65],"we":[66],"compare":[67,105],"first":[70],"RFs":[72],"two":[75],"leading":[76],"deep":[77],"handling":[81],"temporal":[82,89],"data:":[83],"Recurrent":[84],"Neural":[85,91],"Networks":[86,92],"(RNNs)":[87],"Convolutional":[90],"(TempCNNs).":[93],"We":[94,104],"carry":[95],"out":[96],"experiment":[99],"using":[100],"Sentinel-2":[101],"series.":[103],"both":[106],"accuracy":[107],"computational":[109],"times":[110],"classify":[112],"10,980":[113],"km":[114],"<sup":[115],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[116],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">2</sup>":[117],"Australia.":[119],"The":[120],"results":[121],"highlights":[122],"good":[124],"performance":[125],"TemCNNs":[127],"obtain":[129],"highest":[131],"accuracy.":[132],"They":[133],"also":[134],"show":[135],"RNNs":[137],"might":[138],"less":[140],"suited":[141],"scale":[144],"study":[145],"they":[147],"higher":[149],"runtime":[150],"complexity.":[151]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":9},{"year":2023,"cited_by_count":7},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":5},{"year":2020,"cited_by_count":5}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
