{"id":"https://openalex.org/W4387803654","doi":"https://doi.org/10.1109/igarss52108.2023.10282991","title":"Evaluation of a Meta-Transfer Approach for Few-Shot Remote Sensing Scene Classification","display_name":"Evaluation of a Meta-Transfer Approach for Few-Shot Remote Sensing Scene Classification","publication_year":2023,"publication_date":"2023-07-16","ids":{"openalex":"https://openalex.org/W4387803654","doi":"https://doi.org/10.1109/igarss52108.2023.10282991"},"language":"en","primary_location":{"id":"doi:10.1109/igarss52108.2023.10282991","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/igarss52108.2023.10282991","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":"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/A5011777499","display_name":"Keli Cheng","orcid":"https://orcid.org/0000-0003-0424-1306"},"institutions":[{"id":"https://openalex.org/I76835614","display_name":"University of Missouri","ror":"https://ror.org/02ymw8z06","country_code":"US","type":"education","lineage":["https://openalex.org/I76835614"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Keli Cheng","raw_affiliation_strings":["University of Missouri,MO,USA","University of Missouri, MO, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Missouri,MO,USA","institution_ids":["https://openalex.org/I76835614"]},{"raw_affiliation_string":"University of Missouri, MO, USA","institution_ids":["https://openalex.org/I76835614"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5051712346","display_name":"Grant J. Scott","orcid":"https://orcid.org/0000-0001-5870-9387"},"institutions":[{"id":"https://openalex.org/I76835614","display_name":"University of Missouri","ror":"https://ror.org/02ymw8z06","country_code":"US","type":"education","lineage":["https://openalex.org/I76835614"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Grant J. Scott","raw_affiliation_strings":["University of Missouri,MO,USA","University of Missouri, MO, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Missouri,MO,USA","institution_ids":["https://openalex.org/I76835614"]},{"raw_affiliation_string":"University of Missouri, MO, USA","institution_ids":["https://openalex.org/I76835614"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I76835614"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"5002","last_page":"5005"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9933000206947327,"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"}},"topics":[{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9933000206947327,"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/T10689","display_name":"Remote-Sensing Image Classification","score":0.9851999878883362,"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/T10644","display_name":"Cryospheric studies and observations","score":0.9240000247955322,"subfield":{"id":"https://openalex.org/subfields/1902","display_name":"Atmospheric Science"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.825965404510498},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.7744544744491577},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.707568883895874},{"id":"https://openalex.org/keywords/shot","display_name":"Shot (pellet)","score":0.6828255653381348},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6341137290000916},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.5474423170089722},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.514984667301178},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.5008761882781982},{"id":"https://openalex.org/keywords/meta-learning","display_name":"Meta learning (computer science)","score":0.46446317434310913},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.461044043302536},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.4305691123008728},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3456074595451355},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.3025822639465332},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.28880977630615234}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.825965404510498},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.7744544744491577},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.707568883895874},{"id":"https://openalex.org/C2778344882","wikidata":"https://www.wikidata.org/wiki/Q278938","display_name":"Shot (pellet)","level":2,"score":0.6828255653381348},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6341137290000916},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.5474423170089722},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.514984667301178},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.5008761882781982},{"id":"https://openalex.org/C2781002164","wikidata":"https://www.wikidata.org/wiki/Q6822311","display_name":"Meta learning (computer science)","level":3,"score":0.46446317434310913},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.461044043302536},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.4305691123008728},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3456074595451355},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3025822639465332},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.28880977630615234},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","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/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss52108.2023.10282991","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/igarss52108.2023.10282991","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":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W2601450892","https://openalex.org/W2963943197","https://openalex.org/W3096675569","https://openalex.org/W3098643429","https://openalex.org/W3131937156","https://openalex.org/W3132437036","https://openalex.org/W3159481202","https://openalex.org/W3163623915","https://openalex.org/W3174822832","https://openalex.org/W3188824417","https://openalex.org/W3205363943","https://openalex.org/W3205981334","https://openalex.org/W4293661094","https://openalex.org/W4297797918","https://openalex.org/W4312359569","https://openalex.org/W6735236233","https://openalex.org/W6756810460","https://openalex.org/W6779525158","https://openalex.org/W6785249291","https://openalex.org/W6792690848","https://openalex.org/W6842211612"],"related_works":["https://openalex.org/W2074502265","https://openalex.org/W4214877189","https://openalex.org/W2773965352","https://openalex.org/W2381179799","https://openalex.org/W2980279061","https://openalex.org/W2334685461","https://openalex.org/W2366718574","https://openalex.org/W2359774528","https://openalex.org/W4298312966","https://openalex.org/W4401571341"],"abstract_inverted_index":{"Large":[0],"numbers":[1],"of":[2,10,18,34,109,130],"labeled":[3],"data":[4,21,72,91,100],"are":[5,28],"necessary":[6],"for":[7,41,73],"the":[8,32,35,98,104,107,114,120,127],"success":[9],"modern":[11],"deep-learning":[12],"techniques.":[13],"Despite":[14],"a":[15,59],"large":[16],"amount":[17],"satellite":[19],"image":[20],"now":[22],"available,":[23],"their":[24],"ground":[25],"truth":[26],"labels":[27],"inadequate":[29],"due":[30],"to":[31,94],"complexity":[33],"real":[36],"world.":[37],"It":[38],"is":[39],"true":[40],"many":[42],"remote":[43,75],"sensing":[44,76],"tasks":[45,112,125],"including":[46],"scene":[47,77],"classification,":[48,50],"target":[49,52],"and":[51,57,66,89,117],"detection.":[53],"This":[54],"study":[55,105],"explores":[56,106],"evaluates":[58],"state-of-the-art":[60],"pipeline":[61],"that":[62,83,119],"combines":[63],"transfer":[64],"learning":[65],"meta-learning":[67],"methods":[68],"with":[69,123],"voluminous":[70],"external":[71],"few-shot":[74],"classification.":[78],"The":[79],"experimental":[80],"findings":[81],"demonstrate":[82],"using":[84],"this":[85],"pipeline,":[86],"both":[87],"in-domain":[88],"out-of-domain":[90],"can":[92],"lead":[93],"equivalent":[95],"performance":[96],"as":[97],"base":[99],"during":[101],"training.":[102],"Additionally,":[103],"impact":[108],"various":[110],"N-way-K-shot":[111],"in":[113],"meta-training":[115],"stage":[116],"finds":[118],"model":[121],"trained":[122],"5-way-5-shot":[124],"achieves":[126],"highest":[128],"level":[129],"performance.":[131]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":1}],"updated_date":"2026-07-15T18:14:33.161393","created_date":"2025-10-10T00:00:00"}