{"id":"https://openalex.org/W4387829178","doi":"https://doi.org/10.1109/igarss52108.2023.10282285","title":"Active Learning Guided Fine-Tuning for Enhancing Self-Supervised based Multi-Label Classification of Remote Sensing Images","display_name":"Active Learning Guided Fine-Tuning for Enhancing Self-Supervised based Multi-Label Classification of Remote Sensing Images","publication_year":2023,"publication_date":"2023-07-16","ids":{"openalex":"https://openalex.org/W4387829178","doi":"https://doi.org/10.1109/igarss52108.2023.10282285"},"language":"en","primary_location":{"id":"doi:10.1109/igarss52108.2023.10282285","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss52108.2023.10282285","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2023 - 2023 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/A5089478924","display_name":"Lars M\u00f6llenbrok","orcid":null},"institutions":[{"id":"https://openalex.org/I4577782","display_name":"Technische Universit\u00e4t Berlin","ror":"https://ror.org/03v4gjf40","country_code":"DE","type":"education","lineage":["https://openalex.org/I4577782"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Lars M\u00f6llenbrok","raw_affiliation_strings":["Technische Universit&#x00E4;t,Faculty of Electrical Engineering and Computer Science,Berlin,Germany"],"affiliations":[{"raw_affiliation_string":"Technische Universit&#x00E4;t,Faculty of Electrical Engineering and Computer Science,Berlin,Germany","institution_ids":["https://openalex.org/I4577782"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5087126293","display_name":"Beg\u00fcm Demir","orcid":"https://orcid.org/0000-0003-2175-7072"},"institutions":[{"id":"https://openalex.org/I4577782","display_name":"Technische Universit\u00e4t Berlin","ror":"https://ror.org/03v4gjf40","country_code":"DE","type":"education","lineage":["https://openalex.org/I4577782"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Beg\u00fcm Demir","raw_affiliation_strings":["Technische Universit&#x00E4;t,Faculty of Electrical Engineering and Computer Science,Berlin,Germany"],"affiliations":[{"raw_affiliation_string":"Technische Universit&#x00E4;t,Faculty of Electrical Engineering and Computer Science,Berlin,Germany","institution_ids":["https://openalex.org/I4577782"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5089478924"],"corresponding_institution_ids":["https://openalex.org/I4577782"],"apc_list":null,"apc_paid":null,"fwci":0.65,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.7329196,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"4986","last_page":"4989"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9908000230789185,"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":0.9908000230789185,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.9768000245094299,"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"}},{"id":"https://openalex.org/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9764000177383423,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7976288795471191},{"id":"https://openalex.org/keywords/fine-tuning","display_name":"Fine-tuning","score":0.737533450126648},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.6410059332847595},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6292763948440552},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.6067517399787903},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5898767113685608},{"id":"https://openalex.org/keywords/annotation","display_name":"Annotation","score":0.586898148059845},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.46331122517585754},{"id":"https://openalex.org/keywords/limit","display_name":"Limit (mathematics)","score":0.451425701379776},{"id":"https://openalex.org/keywords/labeled-data","display_name":"Labeled data","score":0.41781049966812134},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.41323134303092957},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3782426416873932},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.06082791090011597}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7976288795471191},{"id":"https://openalex.org/C157524613","wikidata":"https://www.wikidata.org/wiki/Q2828883","display_name":"Fine-tuning","level":2,"score":0.737533450126648},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.6410059332847595},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6292763948440552},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.6067517399787903},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5898767113685608},{"id":"https://openalex.org/C2776321320","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Annotation","level":2,"score":0.586898148059845},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.46331122517585754},{"id":"https://openalex.org/C151201525","wikidata":"https://www.wikidata.org/wiki/Q177239","display_name":"Limit (mathematics)","level":2,"score":0.451425701379776},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.41781049966812134},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.41323134303092957},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3782426416873932},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.06082791090011597},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss52108.2023.10282285","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss52108.2023.10282285","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.44999998807907104}],"awards":[],"funders":[{"id":"https://openalex.org/F4320334678","display_name":"European Research Council","ror":"https://ror.org/0472cxd90"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W1989316905","https://openalex.org/W2073459066","https://openalex.org/W2766938848","https://openalex.org/W2897372286","https://openalex.org/W2901576309","https://openalex.org/W2914311543","https://openalex.org/W2948367246","https://openalex.org/W3027532550","https://openalex.org/W3035060554","https://openalex.org/W3134652006","https://openalex.org/W4283697304","https://openalex.org/W4310745604","https://openalex.org/W4385832256","https://openalex.org/W6668990524","https://openalex.org/W6763607942","https://openalex.org/W6779326418","https://openalex.org/W6791742336"],"related_works":["https://openalex.org/W4317548404","https://openalex.org/W4380558612","https://openalex.org/W3022007134","https://openalex.org/W2130553454","https://openalex.org/W4387829178","https://openalex.org/W2033364610","https://openalex.org/W2797776314","https://openalex.org/W2153927146","https://openalex.org/W3104108945","https://openalex.org/W3163689946"],"abstract_inverted_index":{"In":[0],"recent":[1],"years,":[2],"deep":[3],"neural":[4],"networks":[5],"(DNNs)":[6],"have":[7],"been":[8],"found":[9],"very":[10],"successful":[11],"for":[12,112],"multi-label":[13],"classification":[14],"(MLC)":[15],"of":[16,42,66,70,87,107,127],"remote":[17],"sensing":[18],"(RS)":[19],"images.":[20],"Self-supervised":[21],"pre-training":[22,72],"combined":[23],"with":[24,73],"fine-tuning":[25,86,110,128],"on":[26,47],"a":[27,35,48,88,130],"randomly":[28,131],"selected":[29],"small":[30,49,133],"training":[31,52,94,134],"set":[32,53],"has":[33],"become":[34],"popular":[36],"approach":[37],"to":[38,96,124],"minimize":[39],"annotation":[40],"efforts":[41],"data-demanding":[43],"DNNs.":[44],"However,":[45],"finetuning":[46],"and":[50],"biased":[51],"may":[54],"limit":[55],"model":[56,90],"performance.":[57],"To":[58],"address":[59],"this":[60],"issue,":[61],"we":[62],"investigate":[63],"the":[64,67,84,105,113,125],"effectiveness":[65,106],"joint":[68],"use":[69],"self-supervised":[71,89],"active":[74],"learning":[75],"(AL).":[76],"The":[77],"considered":[78],"AL":[79],"strategy":[80],"aims":[81],"at":[82],"guiding":[83],"MLC":[85,121],"by":[91],"selecting":[92],"informative":[93],"samples":[95],"annotate":[97],"in":[98,120],"an":[99],"iterative":[100],"manner.":[101],"Experimental":[102],"results":[103],"show":[104],"applying":[108],"AL-guided":[109],"(particularly":[111],"case":[114],"where":[115],"strong":[116],"class-imbalance":[117],"is":[118],"present":[119],"problems)":[122],"compared":[123],"application":[126],"using":[129],"constructed":[132],"set.":[135]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
