{"id":"https://openalex.org/W4224938958","doi":"https://doi.org/10.1109/icassp43922.2022.9747622","title":"Dual Graph Cross-Domain Few-Shot Learning for Hyperspectral Image Classification","display_name":"Dual Graph Cross-Domain Few-Shot Learning for Hyperspectral Image Classification","publication_year":2022,"publication_date":"2022-04-27","ids":{"openalex":"https://openalex.org/W4224938958","doi":"https://doi.org/10.1109/icassp43922.2022.9747622"},"language":"en","primary_location":{"id":"doi:10.1109/icassp43922.2022.9747622","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp43922.2022.9747622","pdf_url":null,"source":{"id":"https://openalex.org/S4363607702","display_name":"ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","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":"ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","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/A5054256790","display_name":"Yuxiang Zhang","orcid":"https://orcid.org/0000-0002-2913-3515"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuxiang Zhang","raw_affiliation_strings":["Beijing Institute of Technology,School of Information and Electronics","Beijing Key Lab of Fractional Signals and Systems","School of Information and Electronics, Beijing Institute of Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Institute of Technology,School of Information and Electronics","institution_ids":["https://openalex.org/I125839683"]},{"raw_affiliation_string":"Beijing Key Lab of Fractional Signals and Systems","institution_ids":[]},{"raw_affiliation_string":"School of Information and Electronics, Beijing Institute of Technology","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100317994","display_name":"Wei Li","orcid":"https://orcid.org/0000-0001-7015-7335"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Li","raw_affiliation_strings":["Beijing Institute of Technology,School of Information and Electronics","Beijing Key Lab of Fractional Signals and Systems","School of Information and Electronics, Beijing Institute of Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Institute of Technology,School of Information and Electronics","institution_ids":["https://openalex.org/I125839683"]},{"raw_affiliation_string":"Beijing Key Lab of Fractional Signals and Systems","institution_ids":[]},{"raw_affiliation_string":"School of Information and Electronics, Beijing Institute of Technology","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100442192","display_name":"Mengmeng Zhang","orcid":"https://orcid.org/0000-0002-5724-9785"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Mengmeng Zhang","raw_affiliation_strings":["Beijing Institute of Technology,School of Information and Electronics","Beijing Key Lab of Fractional Signals and Systems","School of Information and Electronics, Beijing Institute of Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Institute of Technology,School of Information and Electronics","institution_ids":["https://openalex.org/I125839683"]},{"raw_affiliation_string":"Beijing Key Lab of Fractional Signals and Systems","institution_ids":[]},{"raw_affiliation_string":"School of Information and Electronics, Beijing Institute of Technology","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5067803447","display_name":"Ran Tao","orcid":"https://orcid.org/0000-0002-5243-7189"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ran Tao","raw_affiliation_strings":["Beijing Institute of Technology,School of Information and Electronics","Beijing Key Lab of Fractional Signals and Systems","School of Information and Electronics, Beijing Institute of Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Institute of Technology,School of Information and Electronics","institution_ids":["https://openalex.org/I125839683"]},{"raw_affiliation_string":"Beijing Key Lab of Fractional Signals and Systems","institution_ids":[]},{"raw_affiliation_string":"School of Information and Electronics, Beijing Institute of Technology","institution_ids":["https://openalex.org/I125839683"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I125839683"],"apc_list":null,"apc_paid":null,"fwci":4.2173,"has_fulltext":false,"cited_by_count":25,"citation_normalized_percentile":{"value":0.95366273,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"3573","last_page":"3577"},"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":0.9951000213623047,"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":0.9951000213623047,"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":0.9858999848365784,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7269517779350281},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.683254599571228},{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.6609930396080017},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6372523307800293},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5720173716545105},{"id":"https://openalex.org/keywords/domain-adaptation","display_name":"Domain adaptation","score":0.5532005429267883},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.5399525165557861},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5163087248802185},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.46476349234580994},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.4548015594482422},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4114387631416321},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.34520938992500305},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.17731118202209473},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.10326564311981201},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.09294569492340088}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7269517779350281},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.683254599571228},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.6609930396080017},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6372523307800293},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5720173716545105},{"id":"https://openalex.org/C2776434776","wikidata":"https://www.wikidata.org/wiki/Q19246213","display_name":"Domain adaptation","level":3,"score":0.5532005429267883},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.5399525165557861},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5163087248802185},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.46476349234580994},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.4548015594482422},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4114387631416321},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.34520938992500305},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.17731118202209473},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.10326564311981201},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.09294569492340088},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp43922.2022.9747622","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp43922.2022.9747622","pdf_url":null,"source":{"id":"https://openalex.org/S4363607702","display_name":"ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","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":"ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W2159291411","https://openalex.org/W2572303978","https://openalex.org/W2601450892","https://openalex.org/W2898204262","https://openalex.org/W3004205097","https://openalex.org/W3012405452","https://openalex.org/W3021632667","https://openalex.org/W3034637015","https://openalex.org/W3037865115","https://openalex.org/W3174159092","https://openalex.org/W3195659354","https://openalex.org/W3201461236","https://openalex.org/W3202528422","https://openalex.org/W3205249428","https://openalex.org/W4300833946","https://openalex.org/W6683633756","https://openalex.org/W6735236233","https://openalex.org/W6760184523","https://openalex.org/W6779579431","https://openalex.org/W6788362015"],"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/W2404757046","https://openalex.org/W2044184146","https://openalex.org/W2070598848","https://openalex.org/W4313014865","https://openalex.org/W2400428875"],"abstract_inverted_index":{"Most":[0],"domain":[1,51,101,152],"adaptation":[2],"(DA)":[3],"methods":[4],"focus":[5],"on":[6,57,154,158],"the":[7,10,19,25,36,68,92,134,149,165,168],"case":[8],"where":[9],"source":[11],"data":[12,16,162],"(SD)":[13],"and":[14,109,132,140],"target":[15],"(TD)":[17],"with":[18,74,100,105,111],"same":[20,26],"classes":[21,46],"are":[22,44,116,145],"obtained":[23],"by":[24,95],"sensor":[27],"in":[28,47,61],"cross-scene":[29],"hyperspectral":[30],"image":[31],"(HSI)":[32],"classification":[33,37],"tasks.":[34],"However,":[35],"performance":[38],"is":[39,53,85,128],"significantly":[40],"reduced":[41],"when":[42],"there":[43],"new":[45],"TD.":[48],"In":[49],"addition,":[50],"alignment":[52],"carried":[54],"out":[55],"based":[56],"local":[58],"spatial":[59,70],"information":[60,71],"most":[62],"methods,":[63],"rarely":[64],"taking":[65],"into":[66],"account":[67],"non-local":[69,136],"(non-local":[72],"relationships)":[73],"strong":[75],"correspondence.":[76],"A":[77],"Dual":[78],"Graph":[79],"Cross-domain":[80],"Few-shot":[81,97],"Learning":[82,98],"(DG-CFSL)":[83],"framework":[84],"proposed,":[86],"trying":[87],"to":[88,130,147],"make":[89],"up":[90],"for":[91,118],"above":[93],"shortcomings":[94],"combining":[96],"(FSL)":[99],"alignment.":[102],"Both":[103],"SD":[104],"all":[106],"label":[107,114],"samples":[108,115],"TD":[110],"a":[112],"few":[113],"implemented":[117],"FSL":[119],"episodic":[120],"training.":[121],"Meanwhile,":[122],"Intra-domain":[123],"Distribution":[124],"Extraction":[125],"block":[126],"(IDE-block)":[127],"designed":[129],"characterize":[131],"aggregate":[133],"intra-domain":[135],"relationships.":[137],"Furthermore,":[138],"feature-":[139],"distribution-level":[141],"cross-domain":[142],"graph":[143],"alignments":[144],"used":[146],"mitigate":[148],"impact":[150],"of":[151,167],"shift":[153],"FSL.":[155],"Experimental":[156],"results":[157],"two":[159],"public":[160],"HSI":[161],"sets":[163],"demonstrate":[164],"effectiveness":[166],"proposed":[169],"method.":[170]},"counts_by_year":[{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":13},{"year":2023,"cited_by_count":6},{"year":2022,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
