{"id":"https://openalex.org/W4313306300","doi":"https://doi.org/10.1109/tnnls.2022.3232532","title":"Composite Neighbor-Aware Convolutional Metric Networks for Hyperspectral Image Classification","display_name":"Composite Neighbor-Aware Convolutional Metric Networks for Hyperspectral Image Classification","publication_year":2022,"publication_date":"2022-12-30","ids":{"openalex":"https://openalex.org/W4313306300","doi":"https://doi.org/10.1109/tnnls.2022.3232532","pmid":"https://pubmed.ncbi.nlm.nih.gov/37015657"},"language":"en","primary_location":{"id":"doi:10.1109/tnnls.2022.3232532","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2022.3232532","pdf_url":null,"source":{"id":"https://openalex.org/S4210175523","display_name":"IEEE Transactions on Neural Networks and Learning Systems","issn_l":"2162-237X","issn":["2162-237X","2162-2388"],"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 Neural Networks and Learning Systems","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"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/A5063646463","display_name":"Qichao Liu","orcid":"https://orcid.org/0000-0003-0134-9450"},"institutions":[{"id":"https://openalex.org/I36399199","display_name":"Nanjing University of Science and Technology","ror":"https://ror.org/00xp9wg62","country_code":"CN","type":"education","lineage":["https://openalex.org/I36399199"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Qichao Liu","raw_affiliation_strings":["School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China"],"raw_orcid":"https://orcid.org/0000-0003-0134-9450","affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China","institution_ids":["https://openalex.org/I36399199"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020302879","display_name":"Liang Xiao","orcid":"https://orcid.org/0000-0003-0178-9384"},"institutions":[{"id":"https://openalex.org/I36399199","display_name":"Nanjing University of Science and Technology","ror":"https://ror.org/00xp9wg62","country_code":"CN","type":"education","lineage":["https://openalex.org/I36399199"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liang Xiao","raw_affiliation_strings":["School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China"],"raw_orcid":"https://orcid.org/0000-0003-0178-9384","affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China","institution_ids":["https://openalex.org/I36399199"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5104099185","display_name":"Nan Huang","orcid":"https://orcid.org/0000-0002-3064-817X"},"institutions":[{"id":"https://openalex.org/I41198531","display_name":"Nanjing University of Posts and Telecommunications","ror":"https://ror.org/043bpky34","country_code":"CN","type":"education","lineage":["https://openalex.org/I41198531"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Nan Huang","raw_affiliation_strings":["School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing, China"],"raw_orcid":"https://orcid.org/0000-0002-3064-817X","affiliations":[{"raw_affiliation_string":"School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing, China","institution_ids":["https://openalex.org/I41198531"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5035112538","display_name":"Jinhui Tang","orcid":"https://orcid.org/0000-0001-9008-222X"},"institutions":[{"id":"https://openalex.org/I36399199","display_name":"Nanjing University of Science and Technology","ror":"https://ror.org/00xp9wg62","country_code":"CN","type":"education","lineage":["https://openalex.org/I36399199"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jinhui Tang","raw_affiliation_strings":["School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China"],"raw_orcid":"https://orcid.org/0000-0001-9008-222X","affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China","institution_ids":["https://openalex.org/I36399199"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5063646463"],"corresponding_institution_ids":["https://openalex.org/I36399199"],"apc_list":null,"apc_paid":null,"fwci":3.7301,"has_fulltext":false,"cited_by_count":34,"citation_normalized_percentile":{"value":0.94014252,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":100},"biblio":{"volume":"35","issue":"7","first_page":"9297","last_page":"9311"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9994000196456909,"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":0.9994000196456909,"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":0.9125999808311462,"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/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6675114035606384},{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.6333533525466919},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6319175362586975},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5828344821929932},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5628810524940491},{"id":"https://openalex.org/keywords/euclidean-distance","display_name":"Euclidean distance","score":0.5282328128814697},{"id":"https://openalex.org/keywords/euclidean-geometry","display_name":"Euclidean geometry","score":0.4937179982662201},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4896905720233917},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.44258591532707214},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.44004130363464355},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3414519131183624},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.14719229936599731}],"concepts":[{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6675114035606384},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.6333533525466919},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6319175362586975},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5828344821929932},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5628810524940491},{"id":"https://openalex.org/C120174047","wikidata":"https://www.wikidata.org/wiki/Q847073","display_name":"Euclidean distance","level":2,"score":0.5282328128814697},{"id":"https://openalex.org/C129782007","wikidata":"https://www.wikidata.org/wiki/Q162886","display_name":"Euclidean geometry","level":2,"score":0.4937179982662201},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4896905720233917},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.44258591532707214},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.44004130363464355},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3414519131183624},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.14719229936599731},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","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/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","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}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tnnls.2022.3232532","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2022.3232532","pdf_url":null,"source":{"id":"https://openalex.org/S4210175523","display_name":"IEEE Transactions on Neural Networks and Learning Systems","issn_l":"2162-237X","issn":["2162-237X","2162-2388"],"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 Neural Networks and Learning Systems","raw_type":"journal-article"},{"id":"pmid:37015657","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/37015657","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE transactions on neural networks and learning systems","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1665565477","display_name":null,"funder_award_id":"61571230","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G1732618519","display_name":null,"funder_award_id":"2022M721627","funder_id":"https://openalex.org/F4320321543","funder_display_name":"China Postdoctoral Science Foundation"},{"id":"https://openalex.org/G2351125877","display_name":null,"funder_award_id":"61732007","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G4457837193","display_name":null,"funder_award_id":"JSGP202203","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"},{"id":"https://openalex.org/G6529097563","display_name":null,"funder_award_id":"JSGP202204","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"},{"id":"https://openalex.org/G8432856719","display_name":null,"funder_award_id":"61871226","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/F4320321543","display_name":"China Postdoctoral Science Foundation","ror":"https://ror.org/0426zh255"},{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2090152127","https://openalex.org/W1965169884","https://openalex.org/W3125580510","https://openalex.org/W2008939113","https://openalex.org/W2033213447","https://openalex.org/W14679004","https://openalex.org/W1566651525","https://openalex.org/W2977652649","https://openalex.org/W2318206461","https://openalex.org/W37157938"],"abstract_inverted_index":{"Supervised":[0],"classification":[1],"of":[2,48,54,75,215,241],"hyperspectral":[3],"image":[4,174],"(HSI)":[5],"is":[6,169,221,261],"generally":[7],"required":[8],"to":[9,110,197,208,227],"obtain":[10],"better":[11],"performance":[12,255],"in":[13,89,125,133,142,256],"spectral-spatial":[14],"feature":[15,55],"learning":[16,248],"by":[17,30,171,218],"fully":[18],"using":[19],"complex":[20],"pixel-":[21],"and":[22,122,150,176,189,212,250],"superpixel-level":[23],"interdependencies":[24],"with":[25,93,224],"small":[26],"labeled":[27],"samples.":[28],"Limited":[29],"the":[31,44,52,73,140,143,160,225,229,239,242,253],"local":[32,144],"regular":[33],"convolutions,":[34],"convolutional":[35,60,105],"neural":[36],"networks":[37,61],"(CNNs)":[38],"can":[39,63,181],"only":[40],"exploit":[41],"information":[42],"from":[43,80,115,159,194],"short-range":[45],"Euclidean":[46,121,137],"neighbors":[47,69,118,138,153,188],"a":[49,76,81,90,102,126,165,177,204],"target,":[50],"hindering":[51],"effectiveness":[53],"representation.":[56],"In":[57,97],"contrast,":[58],"graph":[59,78,178],"(GCNs)":[62],"learn":[64,111],"long-range":[65],"dependencies":[66],"between":[67],"non-Euclidean":[68,123,152],"but":[70],"usually":[71],"require":[72],"input":[74],"full":[77,162],"constructed":[79,161],"whole":[82],"HSI,":[83,135],"making":[84],"GCNs":[85],"must":[86],"be":[87],"trained":[88],"full-batch":[91],"manner":[92],"tremendous":[94],"computational":[95],"consumption.":[96],"this":[98],"work,":[99],"we":[100,201],"propose":[101,203],"composite":[103,117,166,187],"neighbor-aware":[104],"metric":[106,206],"network":[107],"(CNCMN),":[108],"aiming":[109],"each":[112,131],"target's":[113],"representation":[114],"its":[116,136,151],"(i.e.,":[119],"both":[120],"neighbors)":[124],"batchwise":[127],"manner.":[128],"Specifically,":[129],"for":[130],"target":[132],"an":[134,173],"are":[139,154],"pixels":[141],"square":[145],"region":[146],"centered":[147],"on":[148,185,234],"itself,":[149],"several":[155],"related":[156],"nodes":[157],"selected":[158],"graph.":[163],"Correspondingly,":[164],"convolution":[167,175],"(CoConv)":[168],"proposed":[170,243],"coupling":[172],"convolution,":[179],"which":[180,220],"perform":[182],"flexible":[183],"convolutions":[184],"those":[186],"extract":[190],"adaptively":[191],"fused":[192],"features":[193],"them.":[195],"Besides,":[196],"further":[198],"boost":[199],"classification,":[200],"also":[202],"mini-batch":[205,230,246],"classifier":[207],"dynamically":[209],"optimize":[210],"interclass":[211],"intraclass":[213],"distances":[214],"samples":[216],"batch":[217],"batch,":[219],"then":[222],"combined":[223],"CoConv":[226],"form":[228],"CNCMN.":[231],"Extensive":[232],"experiments":[233],"three":[235],"real-world":[236],"HSIs":[237],"demonstrate":[238],"advantages":[240],"method":[244],"over":[245],"deep":[247],"algorithms":[249],"have":[251],"obtained":[252],"state-of-the-art":[254],"these":[257],"fields.":[258],"The":[259],"code":[260],"available":[262],"at:":[263],"https://github.com/qichaoliu/HSI-CNCMN.":[264]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":14},{"year":2024,"cited_by_count":12},{"year":2023,"cited_by_count":7}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}