{"id":"https://openalex.org/W4396628258","doi":"https://doi.org/10.1145/3647649.3647664","title":"CNN -Enhanced Multi-Scale Graph Attention Network for Hyperspectral Image Classification","display_name":"CNN -Enhanced Multi-Scale Graph Attention Network for Hyperspectral Image Classification","publication_year":2024,"publication_date":"2024-01-19","ids":{"openalex":"https://openalex.org/W4396628258","doi":"https://doi.org/10.1145/3647649.3647664"},"language":"en","primary_location":{"id":"doi:10.1145/3647649.3647664","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3647649.3647664","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2024 7th International Conference on Image and Graphics Processing","raw_type":"proceedings-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/A5013077766","display_name":"Z. Wang","orcid":"https://orcid.org/0009-0006-2253-4005"},"institutions":[{"id":"https://openalex.org/I78675632","display_name":"Beijing Information Science & Technology University","ror":"https://ror.org/04xnqep60","country_code":"CN","type":"education","lineage":["https://openalex.org/I78675632"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zhongqi Wang","raw_affiliation_strings":["School of Automation, Beijing Information Science and Technology University, China"],"raw_orcid":"https://orcid.org/0009-0006-2253-4005","affiliations":[{"raw_affiliation_string":"School of Automation, Beijing Information Science and Technology University, China","institution_ids":["https://openalex.org/I78675632"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100386304","display_name":"Lu Li","orcid":"https://orcid.org/0000-0001-9823-0565"},"institutions":[{"id":"https://openalex.org/I78675632","display_name":"Beijing Information Science & Technology University","ror":"https://ror.org/04xnqep60","country_code":"CN","type":"education","lineage":["https://openalex.org/I78675632"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lu Li","raw_affiliation_strings":["School of Automation, Beijing Information Science and Technology University, China"],"raw_orcid":"https://orcid.org/0000-0001-9823-0565","affiliations":[{"raw_affiliation_string":"School of Automation, Beijing Information Science and Technology University, China","institution_ids":["https://openalex.org/I78675632"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083686499","display_name":"Junfang Fan","orcid":"https://orcid.org/0000-0001-6484-5457"},"institutions":[{"id":"https://openalex.org/I78675632","display_name":"Beijing Information Science & Technology University","ror":"https://ror.org/04xnqep60","country_code":"CN","type":"education","lineage":["https://openalex.org/I78675632"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Junfang Fan","raw_affiliation_strings":["School of Automation, Beijing Information Science and Technology University, China"],"raw_orcid":"https://orcid.org/0000-0001-6484-5457","affiliations":[{"raw_affiliation_string":"School of Automation, Beijing Information Science and Technology University, China","institution_ids":["https://openalex.org/I78675632"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5092061288","display_name":"Chen Wang","orcid":"https://orcid.org/0000-0003-4305-1169"},"institutions":[{"id":"https://openalex.org/I78675632","display_name":"Beijing Information Science & Technology University","ror":"https://ror.org/04xnqep60","country_code":"CN","type":"education","lineage":["https://openalex.org/I78675632"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chen Wang","raw_affiliation_strings":["School of Automation, Beijing Information Science and Technology University, China"],"raw_orcid":"https://orcid.org/0000-0003-4305-1169","affiliations":[{"raw_affiliation_string":"School of Automation, Beijing Information Science and Technology University, China","institution_ids":["https://openalex.org/I78675632"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5096260442","display_name":"Jingyao Ma","orcid":"https://orcid.org/0009-0002-1055-1087"},"institutions":[{"id":"https://openalex.org/I78675632","display_name":"Beijing Information Science & Technology University","ror":"https://ror.org/04xnqep60","country_code":"CN","type":"education","lineage":["https://openalex.org/I78675632"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jingyao Ma","raw_affiliation_strings":["School of Automation, Beijing Information Science and Technology University, China"],"raw_orcid":"https://orcid.org/0009-0002-1055-1087","affiliations":[{"raw_affiliation_string":"School of Automation, Beijing Information Science and Technology University, China","institution_ids":["https://openalex.org/I78675632"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5013077766"],"corresponding_institution_ids":["https://openalex.org/I78675632"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.10904309,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"92","last_page":"98"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9110999703407288,"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"}},{"id":"https://openalex.org/T13890","display_name":"Remote Sensing and Land Use","score":0.9009000062942505,"subfield":{"id":"https://openalex.org/subfields/1902","display_name":"Atmospheric Science"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.7470369338989258},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7116732001304626},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6935667991638184},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6650664806365967},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.644087553024292},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.6058924198150635},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5443453788757324},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5375418663024902},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.48368343710899353},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.41663509607315063},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.20784005522727966},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.11289238929748535}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.7470369338989258},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7116732001304626},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6935667991638184},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6650664806365967},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.644087553024292},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.6058924198150635},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5443453788757324},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5375418663024902},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.48368343710899353},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.41663509607315063},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.20784005522727966},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.11289238929748535}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3647649.3647664","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3647649.3647664","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2024 7th International Conference on Image and Graphics Processing","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.6700000166893005}],"awards":[],"funders":[{"id":"https://openalex.org/F4320323817","display_name":"Universitas Brawijaya","ror":"https://ror.org/01wk3d929"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W1966580635","https://openalex.org/W1976152398","https://openalex.org/W2104269704","https://openalex.org/W2131864940","https://openalex.org/W2151599207","https://openalex.org/W2163640899","https://openalex.org/W2166923144","https://openalex.org/W2331181944","https://openalex.org/W2577493361","https://openalex.org/W2589840226","https://openalex.org/W2750083646","https://openalex.org/W2764276316","https://openalex.org/W2822065499","https://openalex.org/W2914331134","https://openalex.org/W2945768950","https://openalex.org/W2955589003","https://openalex.org/W2962770389","https://openalex.org/W3002244172","https://openalex.org/W3047443805","https://openalex.org/W3103695279","https://openalex.org/W3125860323","https://openalex.org/W3200705513","https://openalex.org/W4243650027","https://openalex.org/W4293428591"],"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/W2070598848","https://openalex.org/W2044184146","https://openalex.org/W4313014865","https://openalex.org/W2019190440","https://openalex.org/W2565656575"],"abstract_inverted_index":{"In":[0,89],"recent":[1],"years,":[2],"the":[3,37,44,56,68,130,134,148,152,155,159,176,179,191,201,207,215,221,237],"utilization":[4],"of":[5,39,50,58,70,151,178,181,239],"both":[6,71],"Graph":[7,85],"Neural":[8,13],"Network":[9,14,87],"(GNN)":[10],"and":[11,43,66,73,102,118,174],"Convolutional":[12],"(CNN)":[15],"in":[16,26,31],"hyperspectral":[17,227,234],"image":[18,34,228],"(HSI)":[19],"classification":[20,180],"has":[21],"gained":[22],"significant":[23],"attention.":[24],"GNN,":[25,74],"particular,":[27],"have":[28],"proven":[29],"effective":[30],"modelling":[32],"irregular":[33],"regions.":[35],"However,":[36],"limitations":[38],"single-scale":[40],"graph":[41,122,192,209],"structures":[42],"focus":[45],"on":[46,232],"super-pixel":[47,112,202],"nodes":[48,52],"instead":[49],"pixel":[51,216],"within":[53],"GNN":[54],"hinder":[55],"extraction":[57,173],"pixel-level":[59],"spectral-spatial":[60,103,135,161],"features.":[61,108],"To":[62],"address":[63],"these":[64],"challenges":[65],"leverage":[67],"strengths":[69],"CNN":[72],"we":[75,91],"propose":[76],"a":[77,166,197],"novel":[78],"heterogeneous":[79],"deep":[80,171],"network":[81,169,211,224],"called":[82],"CNN-Enhanced":[83],"Multi-Scale":[84],"Attention":[86],"(CEMSGAT).":[88],"CEMSGAT,":[90],"employ":[92],"semi-supervised":[93],"Local":[94],"Fisher":[95],"Discriminant":[96],"Analysis":[97],"(SELF)":[98],"for":[99,170,225],"dimensionality":[100],"reduction":[101],"convolution":[104,162],"to":[105,114,128,143,195],"extract":[106],"surface":[107],"Furthermore,":[109],"our":[110],"utilize":[111],"segmentation":[113],"create":[115],"multi-scale":[116,208],"graphs":[117],"implementing":[119],"an":[120],"improved":[121],"attention":[123,193,210],"algorithm":[124,194],"at":[125],"each":[126],"scale":[127],"process":[129],"features":[131,156,204,218],"obtained":[132,157,219],"from":[133,158,206,220],"convolutions.":[136],"A":[137],"spatial":[138],"transformation":[139],"operation":[140],"is":[141],"designed":[142],"enable":[144],"seamless":[145],"integration":[146],"between":[147,184],"different":[149,185],"scales":[150],"graphs.":[153],"Simultaneously,":[154],"previous":[160],"are":[163,212],"fed":[164],"into":[165],"multilayer":[167,222],"convolutional":[168,223],"feature":[172],"enhance":[175],"accuracy":[177],"connected":[182],"areas":[183],"land":[186],"cover":[187],"types":[188],"calculated":[189],"by":[190],"achieve":[196],"clearer":[198],"classification.":[199,229],"Finally,":[200],"level":[203,217],"derived":[205],"fused":[213],"with":[214],"precise":[226],"Experimental":[230],"results":[231],"three":[233],"datasets":[235],"demonstrate":[236],"superiority":[238],"CEMSGAT":[240],"over":[241],"numerous":[242],"state-of-the-art":[243],"methods.":[244]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
