{"id":"https://openalex.org/W4312510344","doi":"https://doi.org/10.1109/igarss46834.2022.9884876","title":"Convolutional Transformersl for Aerial Image Classification: a General to Specific Learning Curve","display_name":"Convolutional Transformersl for Aerial Image Classification: a General to Specific Learning Curve","publication_year":2022,"publication_date":"2022-07-17","ids":{"openalex":"https://openalex.org/W4312510344","doi":"https://doi.org/10.1109/igarss46834.2022.9884876"},"language":"en","primary_location":{"id":"doi:10.1109/igarss46834.2022.9884876","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss46834.2022.9884876","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":"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/A5017130210","display_name":"Antonio Chirtu","orcid":null},"institutions":[{"id":"https://openalex.org/I61641377","display_name":"Universitatea Na\u021bional\u0103 de \u0218tiin\u021b\u0103 \u0219i Tehnologie Politehnica Bucure\u0219ti","ror":"https://ror.org/0558j5q12","country_code":"RO","type":"education","lineage":["https://openalex.org/I61641377"]}],"countries":["RO"],"is_corresponding":true,"raw_author_name":"Antonio Chirtu","raw_affiliation_strings":["University Politehnica of Bucharest"],"affiliations":[{"raw_affiliation_string":"University Politehnica of Bucharest","institution_ids":["https://openalex.org/I61641377"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083932459","display_name":"Nicolae-C\u0103t\u0103lin Ristea","orcid":"https://orcid.org/0000-0002-7880-9307"},"institutions":[{"id":"https://openalex.org/I61641377","display_name":"Universitatea Na\u021bional\u0103 de \u0218tiin\u021b\u0103 \u0219i Tehnologie Politehnica Bucure\u0219ti","ror":"https://ror.org/0558j5q12","country_code":"RO","type":"education","lineage":["https://openalex.org/I61641377"]}],"countries":["RO"],"is_corresponding":false,"raw_author_name":"Nicolae-Catalin Ristea","raw_affiliation_strings":["University Politehnica of Bucharest"],"affiliations":[{"raw_affiliation_string":"University Politehnica of Bucharest","institution_ids":["https://openalex.org/I61641377"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5091483225","display_name":"Anamaria R\u0103doi","orcid":"https://orcid.org/0000-0002-7577-1067"},"institutions":[{"id":"https://openalex.org/I61641377","display_name":"Universitatea Na\u021bional\u0103 de \u0218tiin\u021b\u0103 \u0219i Tehnologie Politehnica Bucure\u0219ti","ror":"https://ror.org/0558j5q12","country_code":"RO","type":"education","lineage":["https://openalex.org/I61641377"]}],"countries":["RO"],"is_corresponding":false,"raw_author_name":"Anamaria Radoi","raw_affiliation_strings":["University Politehnica of Bucharest"],"affiliations":[{"raw_affiliation_string":"University Politehnica of Bucharest","institution_ids":["https://openalex.org/I61641377"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5017130210"],"corresponding_institution_ids":["https://openalex.org/I61641377"],"apc_list":null,"apc_paid":null,"fwci":0.2398,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.49650023,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"33","issue":null,"first_page":"659","last_page":"662"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9994999766349792,"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":0.9994999766349792,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9976000189781189,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9868999719619751,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/computer-science","display_name":"Computer science","score":0.749390184879303},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.6677550077438354},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6585924625396729},{"id":"https://openalex.org/keywords/aerial-image","display_name":"Aerial image","score":0.5775388479232788},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.5532792210578918},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5080485343933105},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.4834643006324768},{"id":"https://openalex.org/keywords/high-resolution","display_name":"High resolution","score":0.42987340688705444},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.36012130975723267},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.32657065987586975},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.3199993968009949},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.1057557761669159},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.07930707931518555}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.749390184879303},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.6677550077438354},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6585924625396729},{"id":"https://openalex.org/C2776429412","wikidata":"https://www.wikidata.org/wiki/Q4688011","display_name":"Aerial image","level":3,"score":0.5775388479232788},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.5532792210578918},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5080485343933105},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.4834643006324768},{"id":"https://openalex.org/C3020199158","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"High resolution","level":2,"score":0.42987340688705444},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.36012130975723267},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.32657065987586975},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3199993968009949},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.1057557761669159},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.07930707931518555},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss46834.2022.9884876","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss46834.2022.9884876","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"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.8299999833106995,"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":19,"referenced_works":["https://openalex.org/W1686810756","https://openalex.org/W1912954554","https://openalex.org/W2077689834","https://openalex.org/W2086866337","https://openalex.org/W2091280333","https://openalex.org/W2097117768","https://openalex.org/W2190186811","https://openalex.org/W2515866431","https://openalex.org/W2592962403","https://openalex.org/W2620179114","https://openalex.org/W2621526417","https://openalex.org/W2897086142","https://openalex.org/W3105577662","https://openalex.org/W3205131452","https://openalex.org/W4214493665","https://openalex.org/W6637373629","https://openalex.org/W6687157873","https://openalex.org/W6780028523","https://openalex.org/W6802164488"],"related_works":["https://openalex.org/W2378211422","https://openalex.org/W4321353415","https://openalex.org/W2745001401","https://openalex.org/W2130974462","https://openalex.org/W2028665553","https://openalex.org/W2086519370","https://openalex.org/W972276598","https://openalex.org/W3147207884","https://openalex.org/W2076075893","https://openalex.org/W1836423264"],"abstract_inverted_index":{"Remote":[0],"sensing":[1,14,57,91,122],"image":[2,44,58],"classification":[3,59],"is":[4,82,108],"at":[5],"the":[6,12,17,28,89,94,125,128,136],"center":[7],"of":[8,22,71,101,118,127],"many":[9],"tasks":[10],"in":[11,135],"remote":[13,56,90,121],"domain.":[15],"However,":[16],"complexity":[18],"and":[19,43],"content":[20],"variety":[21],"aerial":[23],"images":[24,123],"contribute":[25],"to":[26,84,132],"making":[27],"task":[29],"still":[30],"challenging.":[31],"Transformers":[32,73],"have":[33],"recently":[34],"achieved":[35,66],"state-of-the-art":[36],"performances":[37],"for":[38],"numerous":[39],"natural":[40],"language":[41],"processing":[42,45],"tasks.":[46],"In":[47],"this":[48],"paper,":[49],"we":[50],"pro-pose":[51],"a":[52,62,68,76,102],"novel":[53],"solution":[54],"towards":[55],"based":[60],"on":[61],"general-to-specific":[63],"learning":[64],"curve":[65],"through":[67],"cascaded":[69],"chain":[70],"Vision":[72,79,104],"(Vit).":[74],"First,":[75],"standard":[77],"pre-trained":[78],"Transformer":[80,105],"(ViT)":[81],"used":[83],"provide":[85],"general":[86],"information":[87],"regarding":[88],"scenes,":[92],"whereas":[93],"specific":[95],"details":[96],"are":[97,138],"learned":[98],"by":[99],"means":[100],"Convolutional":[103],"(CvT)":[106],"which":[107],"trained":[109],"end-to-end.":[110],"The":[111],"experiments":[112],"conducted":[113],"over":[114],"two":[115],"benchmark":[116],"datasets":[117],"high":[119],"resolution":[120],"show":[124],"effectiveness":[126],"proposed":[129],"technique.":[130],"Comparisons":[131],"other":[133],"methods":[134],"literature":[137],"also":[139],"provided.":[140]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}