{"id":"https://openalex.org/W4394938888","doi":"https://doi.org/10.1109/tgrs.2024.3390928","title":"Multiscale Random-Shape Convolution and Adaptive Graph Convolution Fusion Network for Hyperspectral Image Classification","display_name":"Multiscale Random-Shape Convolution and Adaptive Graph Convolution Fusion Network for Hyperspectral Image Classification","publication_year":2024,"publication_date":"2024-01-01","ids":{"openalex":"https://openalex.org/W4394938888","doi":"https://doi.org/10.1109/tgrs.2024.3390928"},"language":"en","primary_location":{"id":"doi:10.1109/tgrs.2024.3390928","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2024.3390928","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/A5044826967","display_name":"Hongmin Gao","orcid":"https://orcid.org/0000-0002-8404-2464"},"institutions":[{"id":"https://openalex.org/I163340411","display_name":"Hohai University","ror":"https://ror.org/01wd4xt90","country_code":"CN","type":"education","lineage":["https://openalex.org/I163340411"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Hongmin Gao","raw_affiliation_strings":["Information Department, Hohai University, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"Information Department, Hohai University, Nanjing, China","institution_ids":["https://openalex.org/I163340411"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082155344","display_name":"Runhua Sheng","orcid":"https://orcid.org/0009-0003-6695-1888"},"institutions":[{"id":"https://openalex.org/I163340411","display_name":"Hohai University","ror":"https://ror.org/01wd4xt90","country_code":"CN","type":"education","lineage":["https://openalex.org/I163340411"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Runhua Sheng","raw_affiliation_strings":["Information Department, Hohai University, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"Information Department, Hohai University, Nanjing, China","institution_ids":["https://openalex.org/I163340411"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101407855","display_name":"Zhonghao Chen","orcid":"https://orcid.org/0000-0002-3524-2742"},"institutions":[{"id":"https://openalex.org/I163340411","display_name":"Hohai University","ror":"https://ror.org/01wd4xt90","country_code":"CN","type":"education","lineage":["https://openalex.org/I163340411"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhonghao Chen","raw_affiliation_strings":["Information Department, Hohai University, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"Information Department, Hohai University, Nanjing, China","institution_ids":["https://openalex.org/I163340411"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5024575377","display_name":"Haiyun Liu","orcid":"https://orcid.org/0000-0001-5222-1858"},"institutions":[{"id":"https://openalex.org/I163340411","display_name":"Hohai University","ror":"https://ror.org/01wd4xt90","country_code":"CN","type":"education","lineage":["https://openalex.org/I163340411"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haiyun Liu","raw_affiliation_strings":["Information Department, Hohai University, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"Information Department, Hohai University, Nanjing, China","institution_ids":["https://openalex.org/I163340411"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064694486","display_name":"Shufang Xu","orcid":"https://orcid.org/0000-0002-6802-9083"},"institutions":[{"id":"https://openalex.org/I163340411","display_name":"Hohai University","ror":"https://ror.org/01wd4xt90","country_code":"CN","type":"education","lineage":["https://openalex.org/I163340411"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shufang Xu","raw_affiliation_strings":["Information Department, Hohai University, Nanjing, China","Shaanxi Key Laboratory of Optical Remote Sensing and Intelligent Information Processing, Xi'an, China"],"affiliations":[{"raw_affiliation_string":"Information Department, Hohai University, Nanjing, China","institution_ids":["https://openalex.org/I163340411"]},{"raw_affiliation_string":"Shaanxi Key Laboratory of Optical Remote Sensing and Intelligent Information Processing, Xi'an, China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100389658","display_name":"Bing Zhang","orcid":"https://orcid.org/0000-0001-7311-9844"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"funder","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210137199","display_name":"Aerospace Information Research Institute","ror":"https://ror.org/0419fj215","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210137199"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bing Zhang","raw_affiliation_strings":["Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210137199","https://openalex.org/I19820366"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5044826967"],"corresponding_institution_ids":["https://openalex.org/I163340411"],"apc_list":null,"apc_paid":null,"fwci":6.0731,"has_fulltext":false,"cited_by_count":18,"citation_normalized_percentile":{"value":0.96497258,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":"62","issue":null,"first_page":"1","last_page":"17"},"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":0.9921000003814697,"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/T12157","display_name":"Geochemistry and Geologic Mapping","score":0.9700000286102295,"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":0.683457612991333},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6269669532775879},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.6039979457855225},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.594801127910614},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5726903080940247},{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.5573201775550842},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.4965694546699524},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.49210184812545776},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4486145079135895},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4198054373264313},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3190174102783203},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.20643654465675354},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.14635461568832397}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.683457612991333},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6269669532775879},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.6039979457855225},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.594801127910614},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5726903080940247},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.5573201775550842},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.4965694546699524},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.49210184812545776},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4486145079135895},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4198054373264313},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3190174102783203},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.20643654465675354},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.14635461568832397},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"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/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tgrs.2024.3390928","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2024.3390928","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"},{"id":"pmh:oai:ir.opt.ac.cn:181661/97432","is_oa":false,"landing_page_url":"http://ir.opt.ac.cn/handle/181661/97432","pdf_url":null,"source":{"id":"https://openalex.org/S4377196962","display_name":"Institutional Repository of Xi'an Institute of Optics and Fine Mechanics, Chinese Academy of Sciences (Xian Institute of Optics and Precision Mechanics)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210144662","host_organization_name":"Xi'an Institute of Optics and Precision Mechanics","host_organization_lineage":["https://openalex.org/I4210144662"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"\u671f\u520a\u8bba\u6587"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1812132384","display_name":null,"funder_award_id":"2021M690885","funder_id":"https://openalex.org/F4320321543","funder_display_name":"China Postdoctoral Science Foundation"},{"id":"https://openalex.org/G2100995429","display_name":null,"funder_award_id":"BK20211201","funder_id":"https://openalex.org/F4320322769","funder_display_name":"Natural Science Foundation of Jiangsu Province"},{"id":"https://openalex.org/G4658630977","display_name":null,"funder_award_id":"62071168","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/F4320322769","display_name":"Natural Science Foundation of Jiangsu Province","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":59,"referenced_works":["https://openalex.org/W2004104348","https://openalex.org/W2043665634","https://openalex.org/W2052160904","https://openalex.org/W2087263574","https://openalex.org/W2101711129","https://openalex.org/W2116341502","https://openalex.org/W2117130368","https://openalex.org/W2118246710","https://openalex.org/W2162698522","https://openalex.org/W2163640899","https://openalex.org/W2171171329","https://openalex.org/W2396056163","https://openalex.org/W2570194385","https://openalex.org/W2601564443","https://openalex.org/W2602024454","https://openalex.org/W2608787653","https://openalex.org/W2764276316","https://openalex.org/W2767805377","https://openalex.org/W2789876780","https://openalex.org/W2794284562","https://openalex.org/W2804902458","https://openalex.org/W2910182061","https://openalex.org/W2914331134","https://openalex.org/W2942454403","https://openalex.org/W2985331920","https://openalex.org/W2991494819","https://openalex.org/W3024007459","https://openalex.org/W3028306149","https://openalex.org/W3044657819","https://openalex.org/W3047443805","https://openalex.org/W3102274762","https://openalex.org/W3103695279","https://openalex.org/W3107591966","https://openalex.org/W3114720220","https://openalex.org/W3125860323","https://openalex.org/W3137191419","https://openalex.org/W3139578059","https://openalex.org/W3145049705","https://openalex.org/W3191251640","https://openalex.org/W3200705513","https://openalex.org/W3214821343","https://openalex.org/W4205849075","https://openalex.org/W4210525792","https://openalex.org/W4214711995","https://openalex.org/W4240485910","https://openalex.org/W4309367935","https://openalex.org/W4312845279","https://openalex.org/W4316661207","https://openalex.org/W4321380750","https://openalex.org/W4322576316","https://openalex.org/W4328007071","https://openalex.org/W4364323060","https://openalex.org/W4366148227","https://openalex.org/W4381885522","https://openalex.org/W4381886300","https://openalex.org/W4385151993","https://openalex.org/W4387188337","https://openalex.org/W4387602604","https://openalex.org/W6736893582"],"related_works":["https://openalex.org/W2072166414","https://openalex.org/W3209970181","https://openalex.org/W2964954556","https://openalex.org/W3034421924","https://openalex.org/W2982536526","https://openalex.org/W4386858688","https://openalex.org/W4380302312","https://openalex.org/W3008689640","https://openalex.org/W4385338604","https://openalex.org/W3081626085"],"abstract_inverted_index":{"Convolution":[0],"neural":[1,60],"networks":[2,26,61,66],"(CNNs)":[3],"are":[4,77,197,228],"extensively":[5,78],"utilized":[6],"in":[7,57,80,87],"hyperspectral":[8],"image":[9],"(HSI)":[10],"classification":[11,122,263],"due":[12],"to":[13,17,30,50,52,172,175,200,230,234,252],"their":[14,111],"remarkable":[15],"capability":[16],"extract":[18],"features":[19,33,233],"from":[20,204,211],"patterns":[21],"with":[22,177],"fixed":[23,40],"shapes.":[24,257],"These":[25],"have":[27],"been":[28],"shown":[29],"effectively":[31],"capture":[32],"at":[34,282],"the":[35,39,53,91,102,158,161,192,243],"pixel":[36],"level.":[37],"However,":[38,83],"shape":[41,159],"of":[42,93,105,137,160,245,276],"convolution":[43,65,127,131,151,155,162,184,189],"kernels":[44],"poses":[45],"a":[46,117,138,148,167],"challenge":[47,86],"for":[48,97,153,186,194,221],"CNNs":[49],"adapt":[51],"diverse":[54,178,255],"shapes":[55],"found":[56],"HSIs.":[58],"Graph":[59],"(GNNs),":[62],"particularly":[63],"graph":[64,74,130,183,188],"(GCNs),":[67],"possess":[68],"robust":[69],"feature":[70,202,209,218,239,256],"extraction":[71],"capabilities":[72],"on":[73,260],"structures":[75],"and":[76,107,109,128,142,166,207,214,247],"applied":[79,171],"HSI":[81,262],"classification.":[82],"one":[84],"significant":[85],"using":[88],"GNNs":[89],"is":[90,164,170],"selection":[92],"appropriate":[94],"neighboring":[95],"nodes":[96,206],"information":[98],"aggregation.":[99],"To":[100],"address":[101],"existing":[103,272],"challenges":[104],"GCN":[106,248],"CNN":[108,246],"leverage":[110],"respective":[112],"advantages,":[113],"this":[114],"paper":[115],"introduces":[116],"novel":[118],"patch-based":[119],"CNN-GCN":[120],"fusion":[121,132,203,210],"network,":[123],"named":[124],"multi-scale":[125,149,168],"random-shape":[126,150],"adaptive":[129,181,216],"network":[133],"(MRCAGCFN).":[134],"It":[135],"consists":[136],"spectral":[139],"transformation":[140],"module":[141,152,185,220],"three":[143,261],"main":[144],"modules":[145],"we":[146],"proposed:":[147],"extracting":[154,187],"features,":[156,190,223,236],"where":[157,191,224],"kernel":[163],"randomized":[165],"approach":[169],"enhance":[173],"adaptability":[174],"data":[176],"shapes;":[179],"an":[180,215],"feature-fusion":[182],"weights":[193],"neighborhood":[195],"aggregation":[196],"learned":[198],"adaptively":[199],"reduce":[201],"dissimilar":[205],"strengthen":[208],"similar":[212],"nodes;":[213],"local":[217],"processing":[219,222],"two":[225],"different":[226],"methods":[227],"employed":[229],"convert":[231],"patch-level":[232],"pixel-level":[235],"thereby":[237],"improving":[238],"representation.":[240],"MRCAGCFN":[241,269,278],"combines":[242],"strengths":[244],"while":[249],"introducing":[250],"enhancements":[251],"better":[253],"accommodate":[254],"Experimental":[258],"results":[259],"datasets":[264],"demonstrate":[265],"that":[266],"our":[267,277],"proposed":[268],"outperforms":[270],"some":[271],"methods.":[273],"The":[274],"codes":[275],"will":[279],"be":[280],"available":[281],"https://github.com/shengrunhua/MRCAGCFN.":[283]},"counts_by_year":[{"year":2025,"cited_by_count":10},{"year":2024,"cited_by_count":8}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
