{"id":"https://openalex.org/W4312876374","doi":"https://doi.org/10.1109/igarss46834.2022.9884910","title":"A Deep Curriculum Learner in an Active Learning Cycle for Polsar Image Classification","display_name":"A Deep Curriculum Learner in an Active Learning Cycle for Polsar Image Classification","publication_year":2022,"publication_date":"2022-07-17","ids":{"openalex":"https://openalex.org/W4312876374","doi":"https://doi.org/10.1109/igarss46834.2022.9884910"},"language":"en","primary_location":{"id":"doi:10.1109/igarss46834.2022.9884910","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss46834.2022.9884910","pdf_url":null,"source":{"id":"https://openalex.org/S4363604196","display_name":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2022 - 2022 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/A5002660872","display_name":"Seyed Hamidreza Mousavi","orcid":"https://orcid.org/0000-0001-9334-9989"},"institutions":[{"id":"https://openalex.org/I84606831","display_name":"Terumo (Germany)","ror":"https://ror.org/04hjayk14","country_code":"DE","type":"company","lineage":["https://openalex.org/I4210119124","https://openalex.org/I84606831"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Seyed Hamidreza Mousavi","raw_affiliation_strings":["TernowAI GmbH,Munich,Germany","TernowAI GmbH, Munich, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"TernowAI GmbH,Munich,Germany","institution_ids":["https://openalex.org/I84606831"]},{"raw_affiliation_string":"TernowAI GmbH, Munich, Germany","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5006333653","display_name":"Seyed Majid Azimi","orcid":"https://orcid.org/0000-0002-6084-2272"},"institutions":[{"id":"https://openalex.org/I2898391981","display_name":"Deutsches Zentrum f\u00fcr Luft- und Raumfahrt e. V. (DLR)","ror":"https://ror.org/04bwf3e34","country_code":"DE","type":"facility","lineage":["https://openalex.org/I1305996414","https://openalex.org/I2898391981"]},{"id":"https://openalex.org/I84606831","display_name":"Terumo (Germany)","ror":"https://ror.org/04hjayk14","country_code":"DE","type":"company","lineage":["https://openalex.org/I4210119124","https://openalex.org/I84606831"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Seyed Majid Azimi","raw_affiliation_strings":["TernowAI GmbH,Munich,Germany","Remote Sensing Technology Institute, German Aerospace Center (DLR), Oberpfaffenhofen, Germany","TernowAI GmbH, Munich, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"TernowAI GmbH,Munich,Germany","institution_ids":["https://openalex.org/I84606831"]},{"raw_affiliation_string":"Remote Sensing Technology Institute, German Aerospace Center (DLR), Oberpfaffenhofen, Germany","institution_ids":["https://openalex.org/I2898391981"]},{"raw_affiliation_string":"TernowAI GmbH, Munich, Germany","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.9376,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.91989313,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"88","last_page":"91"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10801","display_name":"Synthetic Aperture Radar (SAR) Applications and Techniques","score":0.9980999827384949,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"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/T10801","display_name":"Synthetic Aperture Radar (SAR) Applications and Techniques","score":0.9980999827384949,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"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/T11609","display_name":"Geophysical Methods and Applications","score":0.9976999759674072,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean Engineering"},"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/T11038","display_name":"Advanced SAR Imaging Techniques","score":0.996999979019165,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"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":0.7661192417144775},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7605125904083252},{"id":"https://openalex.org/keywords/overfitting","display_name":"Overfitting","score":0.7476767301559448},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.7046946287155151},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.6174653172492981},{"id":"https://openalex.org/keywords/synthetic-aperture-radar","display_name":"Synthetic aperture radar","score":0.6080289483070374},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5932901501655579},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5563837885856628},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5040754079818726},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.476254940032959},{"id":"https://openalex.org/keywords/speckle-pattern","display_name":"Speckle pattern","score":0.44289863109588623},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.428554505109787},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3539459705352783},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.2881714105606079}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7661192417144775},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7605125904083252},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.7476767301559448},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.7046946287155151},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.6174653172492981},{"id":"https://openalex.org/C87360688","wikidata":"https://www.wikidata.org/wiki/Q740686","display_name":"Synthetic aperture radar","level":2,"score":0.6080289483070374},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5932901501655579},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5563837885856628},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5040754079818726},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.476254940032959},{"id":"https://openalex.org/C102290492","wikidata":"https://www.wikidata.org/wiki/Q7575045","display_name":"Speckle pattern","level":2,"score":0.44289863109588623},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.428554505109787},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3539459705352783},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2881714105606079},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/igarss46834.2022.9884910","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss46834.2022.9884910","pdf_url":null,"source":{"id":"https://openalex.org/S4363604196","display_name":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-article"},{"id":"pmh:oai:elib.dlr.de:192701","is_oa":false,"landing_page_url":"https://ieeexplore.ieee.org/document/9884910","pdf_url":null,"source":{"id":"https://openalex.org/S4377196266","display_name":"elib (German Aerospace Center)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I2898391981","host_organization_name":"Deutsches Zentrum f\u00fcr Luft- und Raumfahrt e. V. (DLR)","host_organization_lineage":["https://openalex.org/I2898391981"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Konferenzbeitrag"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7400000095367432,"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":7,"referenced_works":["https://openalex.org/W2133989913","https://openalex.org/W2296073425","https://openalex.org/W2965344373","https://openalex.org/W3003720284","https://openalex.org/W3103333135","https://openalex.org/W4206723194","https://openalex.org/W6785638489"],"related_works":["https://openalex.org/W4362597605","https://openalex.org/W1574414179","https://openalex.org/W4297676672","https://openalex.org/W3009056573","https://openalex.org/W2922073769","https://openalex.org/W3128011703","https://openalex.org/W4210966920","https://openalex.org/W2156233651","https://openalex.org/W2550009779","https://openalex.org/W2565656575"],"abstract_inverted_index":{"The":[0],"integration":[1],"of":[2,36,50,67,75,84,100,142,162],"deep":[3,37,108],"learning":[4,7,28,38,44,86,109],"and":[5,52,145],"active":[6,27],"has":[8],"achieved":[9,152],"great":[10],"success":[11],"in":[12,41],"polarimetric":[13],"synthetic":[14],"aperture":[15],"radar":[16],"(PolSAR)":[17],"image":[18],"classification.":[19],"However,":[20],"the":[21,26,34,42,65,72,98,107,136,153],"training":[22,163],"samples":[23,102],"provided":[24],"by":[25,58,82],"approach":[29],"are":[30,56],"inade-quate":[31],"to":[32,106],"promote":[33],"performance":[35],"methods.":[39],"Also,":[40],"initial":[43],"stages,":[45],"querying":[46],"a":[47,90,114,128,158],"small":[48],"amount":[49,161],"informative":[51,101],"complex":[53],"samples,":[54],"which":[55],"plagued":[57],"significant":[59],"speckle":[60],"noise,":[61],"not":[62],"only":[63],"increases":[64],"risk":[66],"overfitting,":[68],"but":[69],"also":[70],"makes":[71],"further":[73],"annotations":[74],"less":[76],"importance.":[77],"To":[78],"alleviate":[79],"these":[80],"problems,":[81],"utilization":[83],"curriculum":[85],"(CL),":[87],"we":[88,112],"propose":[89],"novel":[91],"classification":[92,155],"method":[93,151],"for":[94],"PolSAR":[95,138],"images,":[96],"considering":[97],"complexity":[99],"before":[103],"applying":[104],"them":[105],"model.":[110],"Furthermore,":[111],"develop":[113],"new":[115],"lightweight":[116],"3D":[117],"convolutional":[118],"neural":[119],"network":[120],"with":[121,135,157],"high-level":[122],"feature":[123],"extraction":[124],"ability":[125],"while":[126],"having":[127],"very":[129],"low":[130],"computational":[131],"cost.":[132],"Experimental":[133],"results":[134,156],"two":[137],"benchmark":[139],"data":[140],"sets":[141],"AIRSAR":[143],"Flevoland":[144],"ESAR":[146],"Oberpfaffenhofen":[147],"indicate":[148],"our":[149],"proposed":[150],"state-of-the-art":[154],"significantly":[159],"smaller":[160],"data.":[164]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":2}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}