{"id":"https://openalex.org/W7131887355","doi":"https://doi.org/10.1109/tmm.2026.3668544","title":"Multi-Contrastive and Dynamic Topological Matching Network for Cross-Scene Hyperspectral Image Classification","display_name":"Multi-Contrastive and Dynamic Topological Matching Network for Cross-Scene Hyperspectral Image Classification","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7131887355","doi":"https://doi.org/10.1109/tmm.2026.3668544"},"language":null,"primary_location":{"id":"doi:10.1109/tmm.2026.3668544","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tmm.2026.3668544","pdf_url":null,"source":{"id":"https://openalex.org/S137030581","display_name":"IEEE Transactions on Multimedia","issn_l":"1520-9210","issn":["1520-9210","1941-0077"],"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 Multimedia","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/A5102506532","display_name":"Yujie Ning","orcid":null},"institutions":[{"id":"https://openalex.org/I75900474","display_name":"Hubei University","ror":"https://ror.org/03a60m280","country_code":"CN","type":"education","lineage":["https://openalex.org/I75900474"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yujie Ning","raw_affiliation_strings":["Hubei University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hubei University","institution_ids":["https://openalex.org/I75900474"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5127271286","display_name":"Na Chen","orcid":null},"institutions":[{"id":"https://openalex.org/I75900474","display_name":"Hubei University","ror":"https://ror.org/03a60m280","country_code":"CN","type":"education","lineage":["https://openalex.org/I75900474"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Na Chen","raw_affiliation_strings":["Hubei University"],"raw_orcid":"https://orcid.org/0009-0003-4672-3247","affiliations":[{"raw_affiliation_string":"Hubei University","institution_ids":["https://openalex.org/I75900474"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5127166624","display_name":"Jiangtao Peng","orcid":null},"institutions":[{"id":"https://openalex.org/I75900474","display_name":"Hubei University","ror":"https://ror.org/03a60m280","country_code":"CN","type":"education","lineage":["https://openalex.org/I75900474"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiangtao Peng","raw_affiliation_strings":["Hubei University"],"raw_orcid":"https://orcid.org/0000-0002-4759-0584","affiliations":[{"raw_affiliation_string":"Hubei University","institution_ids":["https://openalex.org/I75900474"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5127280841","display_name":"Weiwei Sun","orcid":null},"institutions":[{"id":"https://openalex.org/I109935558","display_name":"Ningbo University","ror":"https://ror.org/03et85d35","country_code":"CN","type":"education","lineage":["https://openalex.org/I109935558"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weiwei Sun","raw_affiliation_strings":["Ningbo University"],"raw_orcid":"https://orcid.org/0000-0003-3399-7858","affiliations":[{"raw_affiliation_string":"Ningbo University","institution_ids":["https://openalex.org/I109935558"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5075247836","display_name":"Zhijing Ye","orcid":"https://orcid.org/0000-0002-0369-4795"},"institutions":[{"id":"https://openalex.org/I111950717","display_name":"Macau University of Science and Technology","ror":"https://ror.org/03jqs2n27","country_code":"MO","type":"education","lineage":["https://openalex.org/I111950717","https://openalex.org/I4391767947"]}],"countries":["MO"],"is_corresponding":false,"raw_author_name":"Zhijing Ye","raw_affiliation_strings":["Macau University of Science and Technology"],"raw_orcid":"https://orcid.org/0000-0002-0369-4795","affiliations":[{"raw_affiliation_string":"Macau University of Science and Technology","institution_ids":["https://openalex.org/I111950717"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":2645,"currency":"USD","value_usd":2645},"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.13930771,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"28","issue":null,"first_page":"6275","last_page":"6286"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.4830999970436096,"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":0.4830999970436096,"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/T10057","display_name":"Face and Expression Recognition","score":0.05860000103712082,"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"}},{"id":"https://openalex.org/T10052","display_name":"Medical Image Segmentation Techniques","score":0.05810000002384186,"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/discriminative-model","display_name":"Discriminative model","score":0.6876000165939331},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6259999871253967},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5573999881744385},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5284000039100647},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.46880000829696655},{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.3894999921321869},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.3806000053882599},{"id":"https://openalex.org/keywords/network-topology","display_name":"Network topology","score":0.34689998626708984}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7659000158309937},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.6876000165939331},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6259999871253967},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.557699978351593},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5573999881744385},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5284000039100647},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.46880000829696655},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.3894999921321869},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.3806000053882599},{"id":"https://openalex.org/C199845137","wikidata":"https://www.wikidata.org/wiki/Q145490","display_name":"Network topology","level":2,"score":0.34689998626708984},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.33489999175071716},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.32710000872612},{"id":"https://openalex.org/C88230418","wikidata":"https://www.wikidata.org/wiki/Q131476","display_name":"Graph theory","level":2,"score":0.31859999895095825},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3165000081062317},{"id":"https://openalex.org/C2776434776","wikidata":"https://www.wikidata.org/wiki/Q19246213","display_name":"Domain adaptation","level":3,"score":0.31290000677108765},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.29019999504089355},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.28949999809265137},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2865000069141388},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.28369998931884766},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2773999869823456},{"id":"https://openalex.org/C106937863","wikidata":"https://www.wikidata.org/wiki/Q7236518","display_name":"Power graph analysis","level":3,"score":0.27000001072883606},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2655999958515167},{"id":"https://openalex.org/C13540734","wikidata":"https://www.wikidata.org/wiki/Q5318996","display_name":"Dynamic network analysis","level":2,"score":0.25459998846054077},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.25450000166893005}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tmm.2026.3668544","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tmm.2026.3668544","pdf_url":null,"source":{"id":"https://openalex.org/S137030581","display_name":"IEEE Transactions on Multimedia","issn_l":"1520-9210","issn":["1520-9210","1941-0077"],"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 Multimedia","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G6749982366","display_name":null,"funder_award_id":"42471417","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"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":49,"referenced_works":["https://openalex.org/W1965309615","https://openalex.org/W1990283121","https://openalex.org/W2017856447","https://openalex.org/W2096943734","https://openalex.org/W2104068492","https://openalex.org/W2115403315","https://openalex.org/W2603834682","https://openalex.org/W2615977073","https://openalex.org/W2764276316","https://openalex.org/W2794905319","https://openalex.org/W2889945482","https://openalex.org/W2956367483","https://openalex.org/W2968584961","https://openalex.org/W2979509742","https://openalex.org/W2998142089","https://openalex.org/W3021632667","https://openalex.org/W3035524453","https://openalex.org/W3044657819","https://openalex.org/W3047443805","https://openalex.org/W3133682369","https://openalex.org/W3166855724","https://openalex.org/W3168931281","https://openalex.org/W3191251640","https://openalex.org/W3201461236","https://openalex.org/W4210541032","https://openalex.org/W4213174824","https://openalex.org/W4226144843","https://openalex.org/W4285106710","https://openalex.org/W4285223701","https://openalex.org/W4288076010","https://openalex.org/W4291910362","https://openalex.org/W4312775058","https://openalex.org/W4313476697","https://openalex.org/W4316661318","https://openalex.org/W4319865968","https://openalex.org/W4327522253","https://openalex.org/W4327662218","https://openalex.org/W4364323060","https://openalex.org/W4377971298","https://openalex.org/W4383113403","https://openalex.org/W4384304527","https://openalex.org/W4386858191","https://openalex.org/W4386858209","https://openalex.org/W4387272100","https://openalex.org/W4387789732","https://openalex.org/W4387805901","https://openalex.org/W4392207697","https://openalex.org/W4399347356","https://openalex.org/W4399728305"],"related_works":[],"abstract_inverted_index":{"Due":[0],"to":[1,17,23,38,86,101,145,185],"the":[2,68,106,120,128,133,137,146,151,154,158,162,165,182,200,216],"complex":[3,189],"acquisition":[4],"environment":[5],"and":[6,60,78,108,132,156,172,178,187,213,219],"scarcity":[7],"of":[8,70,97,153,161,167,210],"labels,":[9],"domain":[10,134],"adaptation":[11],"(DA)":[12],"techniques":[13],"are":[14,99],"widely":[15],"applied":[16],"cross-scenario":[18],"hyperspectral":[19],"image":[20],"(HSI)":[21],"classification":[22],"achieve":[24],"more":[25],"precise":[26],"labeling.":[27],"Many":[28],"existing":[29],"approaches":[30],"mainly":[31],"rely":[32],"on":[33,215],"convolutional":[34,47],"neural":[35],"networks":[36,48],"(CNNs)":[37],"capture":[39],"local":[40],"spatial":[41],"contextual":[42],"relationships,":[43],"supplemented":[44],"by":[45],"graph":[46,63,93,123,139,155,173,179],"(GCNs)":[49],"for":[50,122],"long-range":[51],"modeling.":[52],"However,":[53],"GCNs":[54],"usually":[55],"require":[56],"full":[57],"batch":[58],"training":[59],"fixed":[61,92],"initial":[62],"structures,":[64],"which":[65],"significantly":[66],"limits":[67],"exploration":[69],"topological":[71,80],"structures.":[72],"To":[73],"address":[74],"this,":[75],"a":[76],"multi-contrastive":[77],"dynamic":[79,103,138],"matching":[81,174],"network":[82],"(MCDTM)":[83],"is":[84,130,141],"introduced":[85],"accomplish":[87],"cross-domain":[88],"HSI":[89],"classification.":[90],"Unlike":[91],"construction":[94],"methods,":[95,206],"mini-batches":[96],"samples":[98],"utilized":[100],"construct":[102],"subgraphs":[104],"within":[105],"source":[107],"target":[109],"domains,":[110],"respectively,":[111],"with":[112],"locally":[113],"extracted":[114],"features":[115],"from":[116],"CNNs":[117],"serving":[118],"as":[119,127],"basis":[121],"construction.":[124],"More":[125],"importantly,":[126],"model":[129],"optimized":[131],"gap":[135],"narrows,":[136],"structure":[140],"adaptively":[142],"adjusted":[143],"according":[144],"evolving":[147],"samples,":[148],"thereby":[149,222],"boosting":[150],"accuracy":[152],"enhancing":[157,181],"discriminative":[159],"power":[160],"model.":[163],"Moreover,":[164],"integration":[166],"weighted":[168],"multi-positive":[169],"contrastive":[170],"learning":[171],"achieves":[175],"distribution":[176],"alignment":[177],"alignment,":[180],"model's":[183],"capacity":[184],"distinguish":[186],"align":[188],"patterns":[190],"in":[191,195],"HSI.":[192],"Experimental":[193],"results":[194],"three":[196],"tasks":[197],"show":[198],"that":[199],"MCDTM":[201],"surpasses":[202],"several":[203],"advanced":[204],"DA":[205],"achieving":[207],"impressive":[208],"accuracies":[209],"80.10%,":[211],"70.01%,":[212],"94.17%":[214],"Houston,":[217],"HyRank,":[218],"YC-YC":[220],"tasks,":[221],"showcasing":[223],"its":[224],"superior":[225],"performance.":[226]},"counts_by_year":[],"updated_date":"2026-08-21T09:56:20.448147","created_date":"2026-02-28T00:00:00"}