{"id":"https://openalex.org/W4226467560","doi":"https://doi.org/10.1109/tgrs.2022.3163326","title":"EMS-GCN: An End-to-End Mixhop Superpixel-Based Graph Convolutional Network for Hyperspectral Image Classification","display_name":"EMS-GCN: An End-to-End Mixhop Superpixel-Based Graph Convolutional Network for Hyperspectral Image Classification","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W4226467560","doi":"https://doi.org/10.1109/tgrs.2022.3163326"},"language":"en","primary_location":{"id":"doi:10.1109/tgrs.2022.3163326","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2022.3163326","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":["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/A5100385579","display_name":"Hongyan Zhang","orcid":"https://orcid.org/0000-0002-7894-5755"},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]},{"id":"https://openalex.org/I4210118728","display_name":"State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing","ror":"https://ror.org/02bpap860","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210118728"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hongyan Zhang","raw_affiliation_strings":["State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China"],"raw_orcid":"https://orcid.org/0000-0002-7894-5755","affiliations":[{"raw_affiliation_string":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China","institution_ids":["https://openalex.org/I37461747","https://openalex.org/I4210118728"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083535697","display_name":"Jiaqi Zou","orcid":"https://orcid.org/0000-0003-1360-2732"},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]},{"id":"https://openalex.org/I4210118728","display_name":"State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing","ror":"https://ror.org/02bpap860","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210118728"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiaqi Zou","raw_affiliation_strings":["State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China"],"raw_orcid":"https://orcid.org/0000-0003-1360-2732","affiliations":[{"raw_affiliation_string":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China","institution_ids":["https://openalex.org/I37461747","https://openalex.org/I4210118728"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100673818","display_name":"Liangpei Zhang","orcid":"https://orcid.org/0000-0001-6890-3650"},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]},{"id":"https://openalex.org/I4210118728","display_name":"State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing","ror":"https://ror.org/02bpap860","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210118728"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liangpei Zhang","raw_affiliation_strings":["State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China"],"raw_orcid":"https://orcid.org/0000-0001-6890-3650","affiliations":[{"raw_affiliation_string":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China","institution_ids":["https://openalex.org/I37461747","https://openalex.org/I4210118728"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":6.0303,"has_fulltext":false,"cited_by_count":73,"citation_normalized_percentile":{"value":0.97084017,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":100},"biblio":{"volume":"60","issue":null,"first_page":"1","last_page":"16"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":1.0,"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":1.0,"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/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9749000072479248,"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/T11667","display_name":"Advanced Chemical Sensor Technologies","score":0.9573000073432922,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.7307682037353516},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7126651406288147},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7018247842788696},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.5931513905525208},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5629868507385254},{"id":"https://openalex.org/keywords/preprocessor","display_name":"Preprocessor","score":0.5488787293434143},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5428611636161804},{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.5323391556739807},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5014853477478027},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.41970857977867126}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7307682037353516},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7126651406288147},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7018247842788696},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.5931513905525208},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5629868507385254},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.5488787293434143},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5428611636161804},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.5323391556739807},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5014853477478027},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.41970857977867126},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tgrs.2022.3163326","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2022.3163326","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"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2918223366","display_name":null,"funder_award_id":"2020CFA053","funder_id":"https://openalex.org/F4320322186","funder_display_name":"Natural Science Foundation of Hubei Province"},{"id":"https://openalex.org/G5377590583","display_name":null,"funder_award_id":"42071322","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8145771212","display_name":null,"funder_award_id":"61871298","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/F4320322186","display_name":"Natural Science Foundation of Hubei Province","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":66,"referenced_works":["https://openalex.org/W1686810756","https://openalex.org/W1903029394","https://openalex.org/W1964541653","https://openalex.org/W2004754531","https://openalex.org/W2029316659","https://openalex.org/W2048281487","https://openalex.org/W2090424610","https://openalex.org/W2194775991","https://openalex.org/W2342652911","https://openalex.org/W2345118402","https://openalex.org/W2609880332","https://openalex.org/W2752782242","https://openalex.org/W2764276316","https://openalex.org/W2771464104","https://openalex.org/W2791006446","https://openalex.org/W2808098982","https://openalex.org/W2883606943","https://openalex.org/W2888119354","https://openalex.org/W2892621946","https://openalex.org/W2898204262","https://openalex.org/W2914331134","https://openalex.org/W2941387379","https://openalex.org/W2942454403","https://openalex.org/W2945768950","https://openalex.org/W2945827377","https://openalex.org/W2950185713","https://openalex.org/W2950266692","https://openalex.org/W2952956606","https://openalex.org/W2964015378","https://openalex.org/W2964321699","https://openalex.org/W2977002487","https://openalex.org/W2991494819","https://openalex.org/W2991616716","https://openalex.org/W3001401628","https://openalex.org/W3006984222","https://openalex.org/W3008789903","https://openalex.org/W3014641072","https://openalex.org/W3024007459","https://openalex.org/W3031696400","https://openalex.org/W3035241330","https://openalex.org/W3035421056","https://openalex.org/W3044657819","https://openalex.org/W3046659257","https://openalex.org/W3047443805","https://openalex.org/W3048631361","https://openalex.org/W3051556114","https://openalex.org/W3100958491","https://openalex.org/W3101553402","https://openalex.org/W3102692100","https://openalex.org/W3103695279","https://openalex.org/W3105357426","https://openalex.org/W3107591966","https://openalex.org/W3114720220","https://openalex.org/W3115454272","https://openalex.org/W3120660573","https://openalex.org/W3122817280","https://openalex.org/W3167109952","https://openalex.org/W3178882511","https://openalex.org/W3214821343","https://openalex.org/W4240485910","https://openalex.org/W4288363255","https://openalex.org/W6637373629","https://openalex.org/W6720006811","https://openalex.org/W6726873649","https://openalex.org/W6761659004","https://openalex.org/W6787590994"],"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/W2076134148","https://openalex.org/W2095030957","https://openalex.org/W2066827917"],"abstract_inverted_index":{"The":[0],"lack":[1],"of":[2,6,53,160,195,227],"labels":[3],"is":[4,112,168,199],"one":[5,97],"the":[7,45,92,106,136,143,154,158,161,180,193,196,202,206,225,228],"major":[8],"challenges":[9],"in":[10,55,61],"hyperspectral":[11],"image":[12],"(HSI)":[13],"classification.":[14,131,221],"Widely":[15],"used":[16],"Deep":[17],"Learning":[18],"(DL)":[19],"models":[20],"such":[21],"as":[22,78,86,88],"convolutional":[23,39],"neural":[24],"networks":[25,40],"(CNNs)":[26],"experience":[27],"serious":[28],"performance":[29],"degradation":[30],"when":[31],"training":[32,159],"samples":[33],"are":[34,190,210],"limited.":[35],"In":[36],"contrast,":[37],"graph":[38,167,198],"(GCNs)":[41],"can":[42],"simultaneously":[43],"exploit":[44],"insufficient":[46],"labeled":[47],"data":[48,52],"and":[49,67,82,170,185],"massive":[50],"unlabeled":[51],"HSI":[54,130],"a":[56,79,147,165,173,214],"semisupervised":[57],"learning":[58],"fashion.":[59],"However,":[60],"order":[62],"to":[63,141,218],"reduce":[64],"computational":[65],"cost":[66],"mitigate":[68],"noise,":[69],"existing":[70],"GCN-based":[71],"classification":[72,90],"methods":[73],"usually":[74],"perform":[75],"superpixel":[76,94,98,138,148,155,166,197,208],"segmentation":[77,139],"preprocessing":[80],"step":[81],"implement":[83],"feature":[84,149,216],"extraction":[85],"well":[87],"node":[89],"on":[91],"predefined":[93],"graph,":[95],"where":[96,178],"might":[99],"incorporate":[100],"pixels":[101],"with":[102,157,233],"different":[103],"labels.":[104],"Moreover,":[105],"local":[107,181],"spectral\u2013spatial":[108],"information":[109,182,187],"within":[110,183],"superpixels":[111,184,189],"generally":[113],"ignored.":[114],"To":[115],"alleviate":[116],"these":[117],"two":[118],"issues,":[119],"we":[120,133],"propose":[121],"an":[122],"end-to-end":[123],"mixhop":[124,175],"superpixel-based":[125,176],"GCN":[126],"(EMS-GCN)":[127],"framework":[128],"for":[129],"Specifically,":[132],"first":[134],"introduce":[135],"differentiable":[137],"algorithm":[140],"map":[142],"pixel":[144,215],"representations":[145,209],"into":[146,172,213],"space,":[150],"which":[151],"allows":[152],"refining":[153],"boundary":[156],"network.":[162],"After":[163],"that,":[164],"constructed":[169],"fed":[171],"novel":[174],"GCN,":[177],"both":[179],"long-range":[186],"among":[188],"extracted,":[191],"while":[192],"structure":[194],"updated":[200],"at":[201],"same":[203],"time.":[204],"Finally,":[205],"enhanced":[207],"mapped":[211],"back":[212],"space":[217],"conduct":[219],"pixel-wise":[220],"Extensive":[222],"experiments":[223],"demonstrate":[224],"effectiveness":[226],"proposed":[229],"EMS-GCN":[230],"method":[231],"compared":[232],"other":[234],"state-of-the-art":[235],"methods.":[236]},"counts_by_year":[{"year":2026,"cited_by_count":5},{"year":2025,"cited_by_count":20},{"year":2024,"cited_by_count":25},{"year":2023,"cited_by_count":17},{"year":2022,"cited_by_count":6}],"updated_date":"2026-07-22T07:51:19.307946","created_date":"2025-10-10T00:00:00"}
