{"id":"https://openalex.org/W4387129614","doi":"https://doi.org/10.1109/lgrs.2023.3320406","title":"Few-Shot Learning Using Residual Channel Attention and Prototype Domain Adaptation for Hyperspectral Image Classification","display_name":"Few-Shot Learning Using Residual Channel Attention and Prototype Domain Adaptation for Hyperspectral Image Classification","publication_year":2023,"publication_date":"2023-01-01","ids":{"openalex":"https://openalex.org/W4387129614","doi":"https://doi.org/10.1109/lgrs.2023.3320406"},"language":"en","primary_location":{"id":"doi:10.1109/lgrs.2023.3320406","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2023.3320406","pdf_url":null,"source":{"id":"https://openalex.org/S126920919","display_name":"IEEE Geoscience and Remote Sensing Letters","issn_l":"1545-598X","issn":["1545-598X","1558-0571"],"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 Geoscience and Remote Sensing Letters","raw_type":"journal-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/A5100731629","display_name":"Zhen Ye","orcid":"https://orcid.org/0000-0001-5410-863X"},"institutions":[{"id":"https://openalex.org/I25355098","display_name":"Chang'an University","ror":"https://ror.org/05mxya461","country_code":"CN","type":"education","lineage":["https://openalex.org/I25355098"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zhen Ye","raw_affiliation_strings":["School of Electronics and Control Engineering, Chang&#x2019;an University, Xi&#x2019;an, China"],"affiliations":[{"raw_affiliation_string":"School of Electronics and Control Engineering, Chang&#x2019;an University, Xi&#x2019;an, China","institution_ids":["https://openalex.org/I25355098"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101492559","display_name":"Tao Sun","orcid":"https://orcid.org/0000-0002-8938-3858"},"institutions":[{"id":"https://openalex.org/I25355098","display_name":"Chang'an University","ror":"https://ror.org/05mxya461","country_code":"CN","type":"education","lineage":["https://openalex.org/I25355098"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tao Sun","raw_affiliation_strings":["School of Electronics and Control Engineering, Chang&#x2019;an University, Xi&#x2019;an, China"],"affiliations":[{"raw_affiliation_string":"School of Electronics and Control Engineering, Chang&#x2019;an University, Xi&#x2019;an, China","institution_ids":["https://openalex.org/I25355098"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101771120","display_name":"Zhan Cao","orcid":"https://orcid.org/0000-0001-7480-7913"},"institutions":[{"id":"https://openalex.org/I25355098","display_name":"Chang'an University","ror":"https://ror.org/05mxya461","country_code":"CN","type":"education","lineage":["https://openalex.org/I25355098"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhan Cao","raw_affiliation_strings":["School of Electronics and Control Engineering, Chang&#x2019;an University, Xi&#x2019;an, China"],"affiliations":[{"raw_affiliation_string":"School of Electronics and Control Engineering, Chang&#x2019;an University, Xi&#x2019;an, China","institution_ids":["https://openalex.org/I25355098"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058293888","display_name":"Lin Bai","orcid":"https://orcid.org/0000-0002-9910-7742"},"institutions":[{"id":"https://openalex.org/I25355098","display_name":"Chang'an University","ror":"https://ror.org/05mxya461","country_code":"CN","type":"education","lineage":["https://openalex.org/I25355098"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lin Bai","raw_affiliation_strings":["School of Electronics and Control Engineering, Chang&#x2019;an University, Xi&#x2019;an, China"],"affiliations":[{"raw_affiliation_string":"School of Electronics and Control Engineering, Chang&#x2019;an University, Xi&#x2019;an, China","institution_ids":["https://openalex.org/I25355098"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5078795882","display_name":"James E. Fowler","orcid":"https://orcid.org/0000-0003-2005-405X"},"institutions":[{"id":"https://openalex.org/I99041443","display_name":"Mississippi State University","ror":"https://ror.org/0432jq872","country_code":"US","type":"education","lineage":["https://openalex.org/I4210141039","https://openalex.org/I99041443"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"James E. Fowler","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Mississippi State University, Mississippi State, MS, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Mississippi State University, Mississippi State, MS, USA","institution_ids":["https://openalex.org/I99041443"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5100731629"],"corresponding_institution_ids":["https://openalex.org/I25355098"],"apc_list":null,"apc_paid":null,"fwci":0.9526,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.78764586,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":98},"biblio":{"volume":"20","issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9997000098228455,"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":0.9997000098228455,"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":0.9952999949455261,"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/T12676","display_name":"Machine Learning and ELM","score":0.980400025844574,"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":0.796189546585083},{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.7836382985115051},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7777398228645325},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6043922901153564},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.512904167175293},{"id":"https://openalex.org/keywords/domain-adaptation","display_name":"Domain adaptation","score":0.4890502393245697},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.47208353877067566},{"id":"https://openalex.org/keywords/extractor","display_name":"Extractor","score":0.46359604597091675},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.45021113753318787},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4453766644001007},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.41062402725219727},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.17565155029296875},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.10440558195114136},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.09527289867401123}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.796189546585083},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.7836382985115051},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7777398228645325},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6043922901153564},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.512904167175293},{"id":"https://openalex.org/C2776434776","wikidata":"https://www.wikidata.org/wiki/Q19246213","display_name":"Domain adaptation","level":3,"score":0.4890502393245697},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.47208353877067566},{"id":"https://openalex.org/C117978034","wikidata":"https://www.wikidata.org/wiki/Q5422192","display_name":"Extractor","level":2,"score":0.46359604597091675},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.45021113753318787},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4453766644001007},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.41062402725219727},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.17565155029296875},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.10440558195114136},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.09527289867401123},{"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/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C21880701","wikidata":"https://www.wikidata.org/wiki/Q2144042","display_name":"Process engineering","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lgrs.2023.3320406","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2023.3320406","pdf_url":null,"source":{"id":"https://openalex.org/S126920919","display_name":"IEEE Geoscience and Remote Sensing Letters","issn_l":"1545-598X","issn":["1545-598X","1558-0571"],"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 Geoscience and Remote Sensing Letters","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1292681430","display_name":null,"funder_award_id":"2020YFC1512002","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"},{"id":"https://openalex.org/G3192453023","display_name":null,"funder_award_id":"GYH2023033","funder_id":"https://openalex.org/F4320329332","funder_display_name":"Central Military Commission"}],"funders":[{"id":"https://openalex.org/F4320329332","display_name":"Central Military Commission","ror":null},{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W2036842392","https://openalex.org/W2117507204","https://openalex.org/W2120184245","https://openalex.org/W2572303978","https://openalex.org/W2601450892","https://openalex.org/W2752782242","https://openalex.org/W2764276316","https://openalex.org/W2795155917","https://openalex.org/W2898204262","https://openalex.org/W2942454403","https://openalex.org/W2964105864","https://openalex.org/W3005956490","https://openalex.org/W3012405452","https://openalex.org/W3031015423","https://openalex.org/W3034552520","https://openalex.org/W3114720220","https://openalex.org/W3125860323","https://openalex.org/W3132867842","https://openalex.org/W3133055443","https://openalex.org/W3170347305","https://openalex.org/W4380451135","https://openalex.org/W6735236233","https://openalex.org/W6750109254"],"related_works":["https://openalex.org/W2072166414","https://openalex.org/W3209970181","https://openalex.org/W2060875994","https://openalex.org/W3034375524","https://openalex.org/W4230131218","https://openalex.org/W2070598848","https://openalex.org/W2044184146","https://openalex.org/W4313014865","https://openalex.org/W2019190440","https://openalex.org/W2343470940"],"abstract_inverted_index":{"While":[0],"deep":[1],"learning":[2,31],"has":[3,32],"been":[4,33],"widely":[5,89],"employed":[6],"for":[7,148],"the":[8,28,70,76,81,84,102,139,149],"classification":[9,141],"of":[10,47],"hyperspectral":[11,140],"imagery,":[12],"many":[13],"scenarios":[14],"arise":[15],"in":[16,18,64,90,101,128],"practice":[17],"which":[19],"too":[20],"few":[21,58],"labeled":[22,48,59],"samples":[23,49],"exist":[24],"to":[25,36,50,109,130,135,138,154],"effectively":[26,67],"train":[27],"networks.":[29],"Few-shot":[30],"recently":[34],"used":[35],"deploy":[37],"classifiers":[38,98,157],"trained":[39],"on":[40],"source-domain":[41],"datasets":[42,51],"comprising":[43],"a":[44,53],"large":[45],"number":[46],"from":[52],"target":[54,73,105,115],"domain":[55,93],"with":[56,122],"only":[57],"samples.":[60],"However,":[61],"most":[62],"techniques":[63],"this":[65],"vein":[66],"assume":[68],"that":[69],"source":[71,103,113],"and":[72,104,114],"domains":[74,86,106],"possess":[75],"same":[77],"data":[78],"distribution,":[79],"whereas":[80],"distributions":[82],"between":[83],"two":[85],"often":[87],"differ":[88],"practice.":[91],"Adversarial":[92],"adaption":[94],"driven":[95],"by":[96],"prototype":[97],"deployed":[99],"independently":[100],"is":[107,126],"proposed":[108,150],"handle":[110],"such":[111],"differing":[112],"distributions,":[116],"while":[117],"an":[118],"attention-based":[119],"feature":[120],"extractor":[121],"residual":[123],"skip":[124],"connections":[125],"developed":[127],"order":[129],"weight":[131],"spectral":[132],"bands":[133],"according":[134],"their":[136],"importance":[137],"task.":[142],"Experimental":[143],"results":[144],"demonstrate":[145],"improved":[146],"performance":[147],"few-shot-learning":[151],"framework":[152],"relative":[153],"both":[155],"fully-supervised":[156],"as":[158,160],"well":[159],"other":[161],"few-shot":[162],"techniques.":[163]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":2}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}
