{"id":"https://openalex.org/W3088971399","doi":"https://doi.org/10.1109/tgrs.2020.3027387","title":"RS-MetaNet: Deep Metametric Learning for Few-Shot Remote Sensing Scene Classification","display_name":"RS-MetaNet: Deep Metametric Learning for Few-Shot Remote Sensing Scene Classification","publication_year":2020,"publication_date":"2020-10-29","ids":{"openalex":"https://openalex.org/W3088971399","doi":"https://doi.org/10.1109/tgrs.2020.3027387","mag":"3088971399"},"language":"en","primary_location":{"id":"doi:10.1109/tgrs.2020.3027387","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2020.3027387","pdf_url":null,"source":{"id":"https://openalex.org/S111326731","display_name":"IEEE Transactions on Geoscience and Remote Sensing","issn_l":"0196-2892","issn":["0196-2892","1558-0644"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Geoscience and Remote Sensing","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2009.13364","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Haifeng Li","orcid":"https://orcid.org/0000-0003-1173-6593"},"institutions":[{"id":"https://openalex.org/I139660479","display_name":"Central South University","ror":"https://ror.org/00f1zfq44","country_code":"CN","type":"education","lineage":["https://openalex.org/I139660479"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Haifeng Li","raw_affiliation_strings":["School of Geosciences and Info-Physics, Central South University, Changsha, China"],"affiliations":[{"raw_affiliation_string":"School of Geosciences and Info-Physics, Central South University, Changsha, China","institution_ids":["https://openalex.org/I139660479"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Zhenqi Cui","orcid":null},"institutions":[{"id":"https://openalex.org/I139660479","display_name":"Central South University","ror":"https://ror.org/00f1zfq44","country_code":"CN","type":"education","lineage":["https://openalex.org/I139660479"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhenqi Cui","raw_affiliation_strings":["School of Geosciences and Info-Physics, Central South University, Changsha, China"],"affiliations":[{"raw_affiliation_string":"School of Geosciences and Info-Physics, Central South University, Changsha, China","institution_ids":["https://openalex.org/I139660479"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Zhiqiang Zhu","orcid":null},"institutions":[{"id":"https://openalex.org/I139660479","display_name":"Central South University","ror":"https://ror.org/00f1zfq44","country_code":"CN","type":"education","lineage":["https://openalex.org/I139660479"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhiqiang Zhu","raw_affiliation_strings":["School of Geosciences and Info-Physics, Central South University, Changsha, China"],"affiliations":[{"raw_affiliation_string":"School of Geosciences and Info-Physics, Central South University, Changsha, China","institution_ids":["https://openalex.org/I139660479"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Li Chen","orcid":null},"institutions":[{"id":"https://openalex.org/I139660479","display_name":"Central South University","ror":"https://ror.org/00f1zfq44","country_code":"CN","type":"education","lineage":["https://openalex.org/I139660479"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Li Chen","raw_affiliation_strings":["School of Geosciences and Info-Physics, Central South University, Changsha, China"],"affiliations":[{"raw_affiliation_string":"School of Geosciences and Info-Physics, Central South University, Changsha, China","institution_ids":["https://openalex.org/I139660479"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Jiawei Zhu","orcid":"https://orcid.org/0000-0002-2961-1302"},"institutions":[{"id":"https://openalex.org/I139660479","display_name":"Central South University","ror":"https://ror.org/00f1zfq44","country_code":"CN","type":"education","lineage":["https://openalex.org/I139660479"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiawei Zhu","raw_affiliation_strings":["School of Geosciences and Info-Physics, Central South University, Changsha, China"],"affiliations":[{"raw_affiliation_string":"School of Geosciences and Info-Physics, Central South University, Changsha, China","institution_ids":["https://openalex.org/I139660479"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Haozhe Huang","orcid":"https://orcid.org/0000-0003-1675-2170"},"institutions":[{"id":"https://openalex.org/I139660479","display_name":"Central South University","ror":"https://ror.org/00f1zfq44","country_code":"CN","type":"education","lineage":["https://openalex.org/I139660479"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haozhe Huang","raw_affiliation_strings":["School of Geosciences and Info-Physics, Central South University, Changsha, China"],"affiliations":[{"raw_affiliation_string":"School of Geosciences and Info-Physics, Central South University, Changsha, China","institution_ids":["https://openalex.org/I139660479"]}]},{"author_position":"last","author":{"id":null,"display_name":"Chao Tao","orcid":"https://orcid.org/0000-0003-0071-310X"},"institutions":[{"id":"https://openalex.org/I139660479","display_name":"Central South University","ror":"https://ror.org/00f1zfq44","country_code":"CN","type":"education","lineage":["https://openalex.org/I139660479"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chao Tao","raw_affiliation_strings":["School of Geosciences and Info-Physics, Central South University, Changsha, China"],"affiliations":[{"raw_affiliation_string":"School of Geosciences and Info-Physics, Central South University, Changsha, China","institution_ids":["https://openalex.org/I139660479"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":7,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I139660479"],"apc_list":null,"apc_paid":null,"fwci":10.896,"has_fulltext":false,"cited_by_count":95,"citation_normalized_percentile":{"value":0.98434869,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":100},"biblio":{"volume":"59","issue":"8","first_page":"6983","last_page":"6994"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.8187999725341797,"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":0.8187999725341797,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.12600000202655792,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.013100000098347664,"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/overfitting","display_name":"Overfitting","score":0.828499972820282},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.6851999759674072},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.60589998960495},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5806000232696533},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5091999769210815},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.5062000155448914},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.4934000074863434},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4814999997615814},{"id":"https://openalex.org/keywords/remote-sensing-application","display_name":"Remote sensing application","score":0.47360000014305115}],"concepts":[{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.828499972820282},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8245999813079834},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6973999738693237},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.6851999759674072},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.60589998960495},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5806000232696533},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5091999769210815},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.5062000155448914},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.4934000074863434},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4814999997615814},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.4772000014781952},{"id":"https://openalex.org/C183365957","wikidata":"https://www.wikidata.org/wiki/Q17140402","display_name":"Remote sensing application","level":3,"score":0.47360000014305115},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.46939998865127563},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.43299999833106995},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.37279999256134033},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.36250001192092896},{"id":"https://openalex.org/C28006648","wikidata":"https://www.wikidata.org/wiki/Q6934509","display_name":"Multi-task learning","level":3,"score":0.36090001463890076},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.3447999954223633},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.3402999937534332},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3310999870300293},{"id":"https://openalex.org/C2776429412","wikidata":"https://www.wikidata.org/wiki/Q4688011","display_name":"Aerial image","level":3,"score":0.32749998569488525},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.3237999975681305},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.2946999967098236},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.28450000286102295},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2775999903678894},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.2766000032424927},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.2630000114440918},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.25999999046325684},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.257099986076355},{"id":"https://openalex.org/C2778572836","wikidata":"https://www.wikidata.org/wiki/Q380933","display_name":"Space (punctuation)","level":2,"score":0.25690001249313354}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tgrs.2020.3027387","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2020.3027387","pdf_url":null,"source":{"id":"https://openalex.org/S111326731","display_name":"IEEE Transactions on Geoscience and Remote Sensing","issn_l":"0196-2892","issn":["0196-2892","1558-0644"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Geoscience and Remote Sensing","raw_type":"journal-article"},{"id":"pmh:oai:arXiv.org:2009.13364","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2009.13364","pdf_url":"https://arxiv.org/pdf/2009.13364","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2009.13364","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2009.13364","pdf_url":"https://arxiv.org/pdf/2009.13364","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1053941327","display_name":null,"funder_award_id":"41871364","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G1259905161","display_name":null,"funder_award_id":"41871276","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G1274608491","display_name":null,"funder_award_id":"41671357","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G1408904890","display_name":null,"funder_award_id":"41871302","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G2025850787","display_name":null,"funder_award_id":"41571397","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320321514","display_name":"Central South University","ror":"https://ror.org/00f1zfq44"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":61,"referenced_works":["https://openalex.org/W99485931","https://openalex.org/W1980038761","https://openalex.org/W2005368619","https://openalex.org/W2024106491","https://openalex.org/W2068042582","https://openalex.org/W2089468765","https://openalex.org/W2090424610","https://openalex.org/W2097117768","https://openalex.org/W2157364932","https://openalex.org/W2169495281","https://openalex.org/W2183341477","https://openalex.org/W2194775991","https://openalex.org/W2267317359","https://openalex.org/W2338459354","https://openalex.org/W2470786530","https://openalex.org/W2515866431","https://openalex.org/W2522698497","https://openalex.org/W2592962403","https://openalex.org/W2617536493","https://openalex.org/W2620429297","https://openalex.org/W2620858446","https://openalex.org/W2621526417","https://openalex.org/W2660018007","https://openalex.org/W2744582969","https://openalex.org/W2770429219","https://openalex.org/W2780334404","https://openalex.org/W2783165089","https://openalex.org/W2804452283","https://openalex.org/W2883732261","https://openalex.org/W2891755487","https://openalex.org/W2898204262","https://openalex.org/W2908046763","https://openalex.org/W2921468651","https://openalex.org/W2921700058","https://openalex.org/W2944029760","https://openalex.org/W2962933664","https://openalex.org/W2963943197","https://openalex.org/W2966617434","https://openalex.org/W2986799142","https://openalex.org/W2999304331","https://openalex.org/W3022140654","https://openalex.org/W4210880854","https://openalex.org/W6635862820","https://openalex.org/W6637373629","https://openalex.org/W6638667902","https://openalex.org/W6680300913","https://openalex.org/W6682778277","https://openalex.org/W6682889407","https://openalex.org/W6684191040","https://openalex.org/W6717697761","https://openalex.org/W6720057410","https://openalex.org/W6731982132","https://openalex.org/W6735236233","https://openalex.org/W6736057607","https://openalex.org/W6743661861","https://openalex.org/W6748331328","https://openalex.org/W6748816842","https://openalex.org/W6750254146","https://openalex.org/W6755095635","https://openalex.org/W6763094690","https://openalex.org/W6777402597"],"related_works":[],"abstract_inverted_index":{"Training":[0],"a":[1,27,32,37,46,89,110,152,159,170,177],"modern":[2],"deep":[3],"neural":[4],"network":[5],"on":[6,85,100,220],"massive":[7],"labeled":[8,253],"samples":[9,58,194],"is":[10],"the":[11,16,76,81,119,129,133,138,143,146,186,190,197,203],"main":[12],"paradigm":[13],"in":[14,45,50,104,128,151,205,244],"solving":[15],"scene":[17,41,126],"classification":[18,42,127],"problem":[19],"for":[20,36],"remote":[21,39,124,166,225],"sensing,":[22],"but":[23,112],"learning":[24,71,95,141],"from":[25,88,142,169],"only":[26,249],"few":[28],"data":[29,227],"points":[30],"remains":[31],"challenge.":[33],"Existing":[34],"methods":[35],"few-shot":[38,123],"sensing":[40,125,167,226],"are":[43,248],"performed":[44],"sample-level":[47],"manner,":[48],"resulting":[49],"easy":[51],"overfitting":[52],"of":[53,62,140,172,189],"learned":[54,63],"features":[55],"to":[56,97,117,122,145,157,192],"individual":[57],"and":[59,154,223,231],"inadequate":[60],"generalization":[61,187],"category":[64],"segmentation":[65,211],"surfaces.":[66],"To":[67],"solve":[68],"this":[69],"problem,":[70],"should":[72],"be":[73],"organized":[74],"at":[75],"task":[77,90,147],"level":[78,139],"rather":[79],"than":[80],"sample":[82,144],"level.":[83],"Learning":[84],"tasks":[86,102],"sampled":[87,103],"family":[91],"can":[92,163],"help":[93],"tune":[94],"algorithms":[96],"perform":[98],"well":[99,164],"new":[101,178,193],"that":[105,162,236],"family.":[106],"Therefore,":[107],"we":[108],"propose":[109,176],"simple":[111],"effective":[113],"method,":[114],"called":[115,181],"RS-MetaNet,":[116],"resolve":[118],"issues":[120],"related":[121],"real":[130],"world.":[131],"On":[132],"one":[134],"hand,":[135],"RS-MetaNet":[136,239],"raises":[137],"by":[148,195],"organizing":[149],"training":[150],"metaway,":[153],"it":[155],"learns":[156],"learn":[158],"metric":[160],"space":[161],"classify":[165],"scenes":[168,204],"series":[171],"tasks.":[173],"We":[174],"also":[175],"loss":[179],"function,":[180],"balance":[182],"loss,":[183],"which":[184],"maximizes":[185],"ability":[188],"model":[191,215],"maximizing":[196],"distance":[198],"between":[199],"different":[200,206],"categories,":[201],"providing":[202],"categories":[207],"with":[208],"better":[209],"linear":[210],"planes":[212],"while":[213],"ensuring":[214],"fit.":[216],"The":[217],"experimental":[218],"results":[219,243],"three":[221],"open":[222],"challenging":[224],"sets,":[228],"UCMerced_LandUse,":[229],"NWPU-RESISC45,":[230],"Aerial":[232],"Image":[233],"Data,":[234],"demonstrate":[235],"our":[237],"proposed":[238],"method":[240],"achieves":[241],"state-of-the-art":[242],"cases":[245],"where":[246],"there":[247],"1":[250],"~":[251],"20":[252],"samples.":[254]},"counts_by_year":[{"year":2026,"cited_by_count":6},{"year":2025,"cited_by_count":14},{"year":2024,"cited_by_count":18},{"year":2023,"cited_by_count":20},{"year":2022,"cited_by_count":21},{"year":2021,"cited_by_count":14},{"year":2020,"cited_by_count":2}],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2020-10-01T00:00:00"}
