{"id":"https://openalex.org/W4415594186","doi":"https://doi.org/10.1109/jstars.2025.3625299","title":"CMHLFG: A Cross-Layer Multihop Graph Convolution and High-Low-Frequency Graph Attention Fusion Network for Hyperspectral Image Classification","display_name":"CMHLFG: A Cross-Layer Multihop Graph Convolution and High-Low-Frequency Graph Attention Fusion Network for Hyperspectral Image Classification","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4415594186","doi":"https://doi.org/10.1109/jstars.2025.3625299"},"language":"en","primary_location":{"id":"doi:10.1109/jstars.2025.3625299","is_oa":true,"landing_page_url":"https://doi.org/10.1109/jstars.2025.3625299","pdf_url":null,"source":{"id":"https://openalex.org/S117727964","display_name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","issn_l":"1939-1404","issn":["1939-1404","2151-1535"],"is_oa":true,"is_in_doaj":true,"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 Journal of Selected Topics in Applied Earth Observations and Remote Sensing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/jstars.2025.3625299","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5120152337","display_name":"Qiaoshuang Si","orcid":null},"institutions":[{"id":"https://openalex.org/I3125743391","display_name":"China University of Geosciences (Beijing)","ror":"https://ror.org/04q6c7p66","country_code":"CN","type":"education","lineage":["https://openalex.org/I3125743391"]},{"id":"https://openalex.org/I4210100255","display_name":"Beijing Academy of Artificial Intelligence","ror":"https://ror.org/016a74861","country_code":"CN","type":"other","lineage":["https://openalex.org/I4210100255"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qiaoshuang Si","raw_affiliation_strings":["School of Artificial Intelligence, China University of Geosciences (Beijing), Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, China University of Geosciences (Beijing), Beijing, China","institution_ids":["https://openalex.org/I3125743391","https://openalex.org/I4210100255"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Jinghui Yang","orcid":"https://orcid.org/0000-0001-9940-4125"},"institutions":[{"id":"https://openalex.org/I3125743391","display_name":"China University of Geosciences (Beijing)","ror":"https://ror.org/04q6c7p66","country_code":"CN","type":"education","lineage":["https://openalex.org/I3125743391"]},{"id":"https://openalex.org/I4210100255","display_name":"Beijing Academy of Artificial Intelligence","ror":"https://ror.org/016a74861","country_code":"CN","type":"other","lineage":["https://openalex.org/I4210100255"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jinghui Yang","raw_affiliation_strings":["School of Artificial Intelligence, China University of Geosciences (Beijing), Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-9940-4125","affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, China University of Geosciences (Beijing), Beijing, China","institution_ids":["https://openalex.org/I3125743391","https://openalex.org/I4210100255"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100387083","display_name":"Ning Wang","orcid":"https://orcid.org/0000-0001-6877-4211"},"institutions":[{"id":"https://openalex.org/I3125743391","display_name":"China University of Geosciences (Beijing)","ror":"https://ror.org/04q6c7p66","country_code":"CN","type":"education","lineage":["https://openalex.org/I3125743391"]},{"id":"https://openalex.org/I4210100255","display_name":"Beijing Academy of Artificial Intelligence","ror":"https://ror.org/016a74861","country_code":"CN","type":"other","lineage":["https://openalex.org/I4210100255"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ning Wang","raw_affiliation_strings":["School of Artificial Intelligence, China University of Geosciences (Beijing), Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, China University of Geosciences (Beijing), Beijing, China","institution_ids":["https://openalex.org/I3125743391","https://openalex.org/I4210100255"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100636411","display_name":"Liguo Wang","orcid":"https://orcid.org/0000-0001-9373-6233"},"institutions":[{"id":"https://openalex.org/I61565387","display_name":"Dalian Minzu University","ror":"https://ror.org/02hxfx521","country_code":"CN","type":"education","lineage":["https://openalex.org/I61565387"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liguo Wang","raw_affiliation_strings":["College of Information and Communication Engineering, Dalian Minzu University, Dalian, China"],"raw_orcid":"https://orcid.org/0000-0001-9373-6233","affiliations":[{"raw_affiliation_string":"College of Information and Communication Engineering, Dalian Minzu University, Dalian, China","institution_ids":["https://openalex.org/I61565387"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1250,"currency":"USD","value_usd":1250},"apc_paid":{"value":1250,"currency":"USD","value_usd":1250},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.39354755,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"18","issue":null,"first_page":"27700","last_page":"27718"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9969000220298767,"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":0.9969000220298767,"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":0.9847999811172485,"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/T12702","display_name":"Brain Tumor Detection and Classification","score":0.9758999943733215,"subfield":{"id":"https://openalex.org/subfields/2808","display_name":"Neurology"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.6353999972343445},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5616999864578247},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.5228000283241272},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.44620001316070557},{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.43230000138282776},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4244000017642975},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.3910999894142151},{"id":"https://openalex.org/keywords/adaptability","display_name":"Adaptability","score":0.37950000166893005},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.35409998893737793}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7407000064849854},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.6353999972343445},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5616999864578247},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.5228000283241272},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4957999885082245},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.44620001316070557},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.43230000138282776},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4244000017642975},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.3910999894142151},{"id":"https://openalex.org/C177606310","wikidata":"https://www.wikidata.org/wiki/Q5674297","display_name":"Adaptability","level":2,"score":0.37950000166893005},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.35409998893737793},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3508000075817108},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3488999903202057},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.3301999866962433},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.32739999890327454},{"id":"https://openalex.org/C157406716","wikidata":"https://www.wikidata.org/wiki/Q4115842","display_name":"Topological graph theory","level":5,"score":0.31349998712539673},{"id":"https://openalex.org/C106937863","wikidata":"https://www.wikidata.org/wiki/Q7236518","display_name":"Power graph analysis","level":3,"score":0.31130000948905945},{"id":"https://openalex.org/C88230418","wikidata":"https://www.wikidata.org/wiki/Q131476","display_name":"Graph theory","level":2,"score":0.3046000003814697},{"id":"https://openalex.org/C2993807640","wikidata":"https://www.wikidata.org/wiki/Q103709453","display_name":"Attention network","level":2,"score":0.29899999499320984},{"id":"https://openalex.org/C2778915421","wikidata":"https://www.wikidata.org/wiki/Q3643177","display_name":"Performance improvement","level":2,"score":0.28700000047683716},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2854999899864197},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.2768000066280365},{"id":"https://openalex.org/C151319957","wikidata":"https://www.wikidata.org/wiki/Q752739","display_name":"Asynchronous communication","level":2,"score":0.271699994802475},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.2689000070095062},{"id":"https://openalex.org/C199845137","wikidata":"https://www.wikidata.org/wiki/Q145490","display_name":"Network topology","level":2,"score":0.2662999927997589},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2648000121116638},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.2587999999523163},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.25859999656677246},{"id":"https://openalex.org/C193415008","wikidata":"https://www.wikidata.org/wiki/Q639681","display_name":"Network architecture","level":2,"score":0.25519999861717224}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/jstars.2025.3625299","is_oa":true,"landing_page_url":"https://doi.org/10.1109/jstars.2025.3625299","pdf_url":null,"source":{"id":"https://openalex.org/S117727964","display_name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","issn_l":"1939-1404","issn":["1939-1404","2151-1535"],"is_oa":true,"is_in_doaj":true,"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 Journal of Selected Topics in Applied Earth Observations and Remote Sensing","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:59ece672f59449f6972c18d5dc7ffc69","is_oa":true,"landing_page_url":"https://doaj.org/article/59ece672f59449f6972c18d5dc7ffc69","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 18, Pp 27700-27718 (2025)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/jstars.2025.3625299","is_oa":true,"landing_page_url":"https://doi.org/10.1109/jstars.2025.3625299","pdf_url":null,"source":{"id":"https://openalex.org/S117727964","display_name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","issn_l":"1939-1404","issn":["1939-1404","2151-1535"],"is_oa":true,"is_in_doaj":true,"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 Journal of Selected Topics in Applied Earth Observations and Remote Sensing","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1889439371","display_name":null,"funder_award_id":"62001434","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7424700364","display_name":"\u57fa\u4e8eSVM\u7684\u9ad8\u5149\u8c31\u6570\u636e\u89e3\u6df7\u6280\u672f\u7814\u7a76","funder_award_id":"62071084","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":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":51,"referenced_works":["https://openalex.org/W1521436688","https://openalex.org/W1974981350","https://openalex.org/W2039409148","https://openalex.org/W2043665634","https://openalex.org/W2101711129","https://openalex.org/W2118246710","https://openalex.org/W2314785379","https://openalex.org/W2892621946","https://openalex.org/W2971432438","https://openalex.org/W3047443805","https://openalex.org/W3107358227","https://openalex.org/W3114720220","https://openalex.org/W3128443161","https://openalex.org/W3128776197","https://openalex.org/W3173856251","https://openalex.org/W3200959564","https://openalex.org/W3210012904","https://openalex.org/W4210541032","https://openalex.org/W4210794570","https://openalex.org/W4214536100","https://openalex.org/W4226467560","https://openalex.org/W4233838870","https://openalex.org/W4285106710","https://openalex.org/W4285180452","https://openalex.org/W4285299034","https://openalex.org/W4296339430","https://openalex.org/W4307091817","https://openalex.org/W4309089742","https://openalex.org/W4313627848","https://openalex.org/W4313639623","https://openalex.org/W4324144346","https://openalex.org/W4328007071","https://openalex.org/W4364323060","https://openalex.org/W4386076442","https://openalex.org/W4386212367","https://openalex.org/W4386634481","https://openalex.org/W4392399472","https://openalex.org/W4392939636","https://openalex.org/W4394862687","https://openalex.org/W4394896875","https://openalex.org/W4396753596","https://openalex.org/W4399527425","https://openalex.org/W4400672326","https://openalex.org/W4400877899","https://openalex.org/W4401413770","https://openalex.org/W4401608495","https://openalex.org/W4403918495","https://openalex.org/W4404847745","https://openalex.org/W4405056202","https://openalex.org/W4405717304","https://openalex.org/W4409019631"],"related_works":[],"abstract_inverted_index":{"Graph":[0],"convolutional":[1],"networks":[2],"(GCNs)":[3],"have":[4],"emerged":[5],"as":[6],"a":[7,62,67,85,92,98,106,184],"prominent":[8],"research":[9],"focus":[10],"for":[11,78,158],"hyperspectral":[12,119,218],"image":[13],"classification":[14,241],"(HSIC).":[15],"However,":[16],"existing":[17],"GCN-based":[18],"HSIC":[19,249],"methods":[20],"still":[21],"face":[22],"the":[23,26,37,47,136,154,198,206,223,236,239],"following":[24],"challenges:":[25],"graph":[27,59,70,74,101,109,168,180],"structure":[28],"has":[29],"difficulty":[30],"fully":[31],"capturing":[32,167],"complex":[33,214],"neighborhood":[34],"relationships,":[35],"and":[36,57,65,72,105,127,161,182,193,235],"adaptability":[38,208],"to":[39,144,152,187,209],"heterogeneous":[40,210],"topological":[41,211],"structures":[42,212],"is":[43],"limited.":[44],"To":[45],"address":[46],"aforementioned":[48],"issues,":[49],"we":[50],"integrate":[51],"multiscale":[52,93],"information,":[53],"cross-layer":[54,68,99],"multihop":[55,69,100],"graphs,":[56],"high-low-frequency":[58,73],"attention":[60,75,110],"into":[61],"unified":[63],"framework":[64],"propose":[66],"convolution":[71,95,102],"fusion":[76],"network":[77],"HSIC,":[79],"namely,":[80],"CMHLFG.":[81],"The":[82,113,174,220],"CMHLFG":[83,224,237],"employs":[84],"dual-branch":[86],"network,":[87],"including":[88],"three":[89],"core":[90],"components:":[91],"dilated":[94],"module":[96],"(MDC-M),":[97],"branch":[103,111],"(CMGC-B),":[104],"high-low":[107],"frequency":[108,177,195],"(HLFGA-B).":[112],"MDC-M":[114],"extracts":[115],"hierarchical":[116],"features":[117,160],"from":[118],"images":[120],"while":[121],"preserving":[122],"both":[123],"detailed":[124],"spatial":[125],"information":[126,170],"contextual":[128],"semantic":[129],"representations,":[130],"thereby":[131],"enhancing":[132],"feature":[133,146],"discriminability.":[134],"In":[135],"CMGC-B,":[137],"diversified":[138],"node":[139,159],"relationships":[140],"are":[141,150],"comprehensively":[142],"modeled":[143],"improve":[145],"separability.":[147],"Cross-layer":[148],"connections":[149],"utilized":[151],"strengthen":[153],"model's":[155,207],"expressive":[156],"capacity":[157],"broaden":[162],"its":[163],"learning":[164],"scope,":[165],"effectively":[166],"structural":[169],"across":[171],"different":[172],"scales.":[173],"HLFGA-B":[175],"distinguishes":[176],"components":[178],"in":[179,213],"signals":[181],"uses":[183],"gating":[185],"mechanism":[186],"dynamically":[188],"allocate":[189],"weights":[190],"between":[191],"high-frequency":[192],"low":[194],"components,":[196],"with":[197,244],"residual":[199],"channel":[200],"fusing":[201],"original":[202],"input":[203],"features,":[204],"improving":[205],"land-cover":[215],"distributions":[216],"of":[217,222],"images.":[219],"efficacy":[221],"was":[225],"confirmed":[226],"through":[227],"experiments":[228],"on":[229],"some":[230],"real":[231],"public":[232],"HSI":[233],"datasets,":[234],"achieved":[238],"best":[240],"performance":[242],"compared":[243],"recently":[245],"related":[246],"state":[247],"of-the-art":[248],"approaches.":[250]},"counts_by_year":[],"updated_date":"2025-11-14T23:14:49.485078","created_date":"2025-10-28T00:00:00"}
