{"id":"https://openalex.org/W2984113590","doi":"https://doi.org/10.1109/igarss.2019.8900467","title":"A Deep Learning Architecture for Heterogeneous and Irregularly Sampled Remote Sensing Time Series","display_name":"A Deep Learning Architecture for Heterogeneous and Irregularly Sampled Remote Sensing Time Series","publication_year":2019,"publication_date":"2019-07-01","ids":{"openalex":"https://openalex.org/W2984113590","doi":"https://doi.org/10.1109/igarss.2019.8900467","mag":"2984113590"},"language":"en","primary_location":{"id":"doi:10.1109/igarss.2019.8900467","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2019.8900467","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":"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/A5077753514","display_name":"Corrado Avolio","orcid":null},"institutions":[{"id":"https://openalex.org/I4210119941","display_name":"e GEOS (Italy)","ror":"https://ror.org/02jf95y23","country_code":"IT","type":"company","lineage":["https://openalex.org/I4210119941"]},{"id":"https://openalex.org/I2800530175","display_name":"Agenzia Spaziale Italiana","ror":"https://ror.org/034zgem50","country_code":"IT","type":"government","lineage":["https://openalex.org/I2800530175"]}],"countries":["IT"],"is_corresponding":true,"raw_author_name":"Corrado Avolio","raw_affiliation_strings":["e-GEOS \u2013 Italian Space Agency and Telespazio company, Rome, Italy"],"affiliations":[{"raw_affiliation_string":"e-GEOS \u2013 Italian Space Agency and Telespazio company, Rome, Italy","institution_ids":["https://openalex.org/I2800530175","https://openalex.org/I4210119941"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100774114","display_name":"A. Tricomi","orcid":"https://orcid.org/0000-0002-5071-5501"},"institutions":[{"id":"https://openalex.org/I2800530175","display_name":"Agenzia Spaziale Italiana","ror":"https://ror.org/034zgem50","country_code":"IT","type":"government","lineage":["https://openalex.org/I2800530175"]},{"id":"https://openalex.org/I4210119941","display_name":"e GEOS (Italy)","ror":"https://ror.org/02jf95y23","country_code":"IT","type":"company","lineage":["https://openalex.org/I4210119941"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Alessia Tricomi","raw_affiliation_strings":["e-GEOS \u2013 Italian Space Agency and Telespazio company, Rome, Italy"],"affiliations":[{"raw_affiliation_string":"e-GEOS \u2013 Italian Space Agency and Telespazio company, Rome, Italy","institution_ids":["https://openalex.org/I2800530175","https://openalex.org/I4210119941"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056710725","display_name":"Claudio Mammone","orcid":null},"institutions":[{"id":"https://openalex.org/I4210119941","display_name":"e GEOS (Italy)","ror":"https://ror.org/02jf95y23","country_code":"IT","type":"company","lineage":["https://openalex.org/I4210119941"]},{"id":"https://openalex.org/I2800530175","display_name":"Agenzia Spaziale Italiana","ror":"https://ror.org/034zgem50","country_code":"IT","type":"government","lineage":["https://openalex.org/I2800530175"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Claudio Mammone","raw_affiliation_strings":["e-GEOS \u2013 Italian Space Agency and Telespazio company, Rome, Italy"],"affiliations":[{"raw_affiliation_string":"e-GEOS \u2013 Italian Space Agency and Telespazio company, Rome, Italy","institution_ids":["https://openalex.org/I2800530175","https://openalex.org/I4210119941"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011125904","display_name":"Massimo Zavagli","orcid":null},"institutions":[{"id":"https://openalex.org/I2800530175","display_name":"Agenzia Spaziale Italiana","ror":"https://ror.org/034zgem50","country_code":"IT","type":"government","lineage":["https://openalex.org/I2800530175"]},{"id":"https://openalex.org/I4210119941","display_name":"e GEOS (Italy)","ror":"https://ror.org/02jf95y23","country_code":"IT","type":"company","lineage":["https://openalex.org/I4210119941"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Massimo Zavagli","raw_affiliation_strings":["e-GEOS \u2013 Italian Space Agency and Telespazio company, Rome, Italy"],"affiliations":[{"raw_affiliation_string":"e-GEOS \u2013 Italian Space Agency and Telespazio company, Rome, Italy","institution_ids":["https://openalex.org/I2800530175","https://openalex.org/I4210119941"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5086844035","display_name":"E. Costantini","orcid":"https://orcid.org/0000-0001-8470-749X"},"institutions":[{"id":"https://openalex.org/I4210119941","display_name":"e GEOS (Italy)","ror":"https://ror.org/02jf95y23","country_code":"IT","type":"company","lineage":["https://openalex.org/I4210119941"]},{"id":"https://openalex.org/I2800530175","display_name":"Agenzia Spaziale Italiana","ror":"https://ror.org/034zgem50","country_code":"IT","type":"government","lineage":["https://openalex.org/I2800530175"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Mario Costantini","raw_affiliation_strings":["e-GEOS \u2013 Italian Space Agency and Telespazio company, Rome, Italy"],"affiliations":[{"raw_affiliation_string":"e-GEOS \u2013 Italian Space Agency and Telespazio company, Rome, Italy","institution_ids":["https://openalex.org/I2800530175","https://openalex.org/I4210119941"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5077753514"],"corresponding_institution_ids":["https://openalex.org/I2800530175","https://openalex.org/I4210119941"],"apc_list":null,"apc_paid":null,"fwci":0.5109,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.70192185,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10111","display_name":"Remote Sensing in Agriculture","score":0.9969000220298767,"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":0.9969000220298767,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9921000003814697,"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"}},{"id":"https://openalex.org/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9771999716758728,"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/computer-science","display_name":"Computer science","score":0.7085703015327454},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.6895567774772644},{"id":"https://openalex.org/keywords/architecture","display_name":"Architecture","score":0.6194247007369995},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.5434477925300598},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.48695430159568787},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4777190685272217},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.36501649022102356},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.3460710942745209},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.14476847648620605},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.13154810667037964},{"id":"https://openalex.org/keywords/archaeology","display_name":"Archaeology","score":0.07757240533828735}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7085703015327454},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.6895567774772644},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.6194247007369995},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5434477925300598},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.48695430159568787},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4777190685272217},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.36501649022102356},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.3460710942745209},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.14476847648620605},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.13154810667037964},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.07757240533828735},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss.2019.8900467","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2019.8900467","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":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W854541894","https://openalex.org/W1514535095","https://openalex.org/W1799366690","https://openalex.org/W1947481528","https://openalex.org/W1950788856","https://openalex.org/W1963809378","https://openalex.org/W2068094410","https://openalex.org/W2077524583","https://openalex.org/W2117330251","https://openalex.org/W2171599506","https://openalex.org/W2786038065","https://openalex.org/W2803805253","https://openalex.org/W2901791811","https://openalex.org/W2964244673","https://openalex.org/W3104839310","https://openalex.org/W6623517193","https://openalex.org/W6630875275","https://openalex.org/W6638444622","https://openalex.org/W6640754710","https://openalex.org/W6752046673"],"related_works":["https://openalex.org/W4375867731","https://openalex.org/W2611989081","https://openalex.org/W1919101720","https://openalex.org/W4230611425","https://openalex.org/W2731899572","https://openalex.org/W4304166257","https://openalex.org/W4380075502","https://openalex.org/W2622688551","https://openalex.org/W1550175370","https://openalex.org/W1990205660"],"abstract_inverted_index":{"Remote":[0],"sensing":[1,160],"present":[2],"some":[3],"new":[4,66],"challenges":[5],"for":[6],"deep":[7,67,75],"learning,":[8],"because":[9],"(also":[10],"to":[11],"compensate":[12],"the":[13,43,87,91,106,114,118,125,129,141],"scarce":[14],"detail":[15],"level)":[16],"multimodal,":[17],"multisource":[18],"and":[19,54,80,97,103,147,154,174],"multitemporal":[20],"data":[21,38,57,92,126],"should":[22],"be":[23],"jointly":[24,102],"exploited.":[25],"For":[26],"example,":[27],"time":[28],"series":[29],"of":[30,42,74,181],"optical":[31],"multispectral/hyperspectral":[32],"or":[33],"synthetic":[34],"aperture":[35],"radar":[36],"(SAR)":[37],"probe":[39],"different":[40,49],"properties":[41],"observed":[44],"scene,":[45],"based":[46],"on":[47,151],"their":[48,94],"wavelength,":[50],"acquisition":[51],"geometry,":[52],"etc.,":[53],"with":[55,168],"possible":[56],"gaps.":[58],"To":[59],"address":[60],"this":[61],"task,":[62],"we":[63],"propose":[64],"a":[65,72,81],"learning":[68],"architecture":[69],"that":[70,108],"exploits":[71],"sequence":[73],"convolutional":[76],"neural":[77,83],"networks":[78],"(CNN)":[79],"recurrent":[82],"network":[84],"(RNN).":[85],"In":[86],"proposed":[88,130,142],"architecture,":[89,143],"all":[90],"(with":[93],"spectral,":[95],"spatial":[96,177,182],"temporal":[98],"information)":[99],"are":[100,122,165],"used":[101],"optimally":[104],"in":[105],"sense":[107],"no":[109],"imputation":[110],"is":[111],"enforced,":[112],"but":[113],"internal":[115],"weights":[116],"providing":[117],"best":[119],"classification":[120],"results":[121,164],"estimated":[123],"from":[124],"themselves":[127],"(hence":[128],"name":[131],"ODIN":[132],"-":[133],"Optimal":[134],"Data":[135],"Imputation":[136],"Network).":[137],"We":[138],"have":[139],"tested":[140],"using":[144],"Sentinel":[145],"SAR":[146],"multispectral":[148],"image":[149],"series,":[150],"land":[152],"cover":[153],"crop":[155],"classification,":[156],"an":[157,169],"important":[158],"remote":[159],"application.":[161],"The":[162],"obtained":[163],"very":[166],"promising,":[167],"error":[170],"rate":[171],"below":[172],"1%,":[173],"show":[175],"good":[176],"consistency":[178],"without":[179],"loss":[180],"resolution.":[183]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
