{"id":"https://openalex.org/W4415707566","doi":"https://doi.org/10.1109/tgrs.2025.3627204","title":"Dual-Graph Knowledge Distillation Meets Multiscale CNN: A Novel Ensemble Approach for Few-Shot Hyperspectral Image Classification","display_name":"Dual-Graph Knowledge Distillation Meets Multiscale CNN: A Novel Ensemble Approach for Few-Shot Hyperspectral Image Classification","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4415707566","doi":"https://doi.org/10.1109/tgrs.2025.3627204"},"language":null,"primary_location":{"id":"doi:10.1109/tgrs.2025.3627204","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2025.3627204","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/A5111277313","display_name":"Xiaolong Li","orcid":null},"institutions":[{"id":"https://openalex.org/I68986083","display_name":"Northwest Normal University","ror":"https://ror.org/00gx3j908","country_code":"CN","type":"education","lineage":["https://openalex.org/I68986083"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xiaolong Li","raw_affiliation_strings":["College of Computer Science and Engineering, Northwest Normal University, Lanzhou, Gansu, China"],"affiliations":[{"raw_affiliation_string":"College of Computer Science and Engineering, Northwest Normal University, Lanzhou, Gansu, China","institution_ids":["https://openalex.org/I68986083"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053794277","display_name":"Huifang Ma","orcid":"https://orcid.org/0000-0002-5104-8982"},"institutions":[{"id":"https://openalex.org/I68986083","display_name":"Northwest Normal University","ror":"https://ror.org/00gx3j908","country_code":"CN","type":"education","lineage":["https://openalex.org/I68986083"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Huifang Ma","raw_affiliation_strings":["College of Computer Science and Engineering, Northwest Normal University, Lanzhou, Gansu, China"],"affiliations":[{"raw_affiliation_string":"College of Computer Science and Engineering, Northwest Normal University, Lanzhou, Gansu, China","institution_ids":["https://openalex.org/I68986083"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5091775807","display_name":"Shuliu Guo","orcid":null},"institutions":[{"id":"https://openalex.org/I68986083","display_name":"Northwest Normal University","ror":"https://ror.org/00gx3j908","country_code":"CN","type":"education","lineage":["https://openalex.org/I68986083"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shuheng Guo","raw_affiliation_strings":["College of Computer Science and Engineering, Northwest Normal University, Lanzhou, Gansu, China"],"affiliations":[{"raw_affiliation_string":"College of Computer Science and Engineering, Northwest Normal University, Lanzhou, Gansu, China","institution_ids":["https://openalex.org/I68986083"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113169706","display_name":"Di Zhang","orcid":"https://orcid.org/0009-0007-2878-7916"},"institutions":[{"id":"https://openalex.org/I68986083","display_name":"Northwest Normal University","ror":"https://ror.org/00gx3j908","country_code":"CN","type":"education","lineage":["https://openalex.org/I68986083"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Di Zhang","raw_affiliation_strings":["College of Computer Science and Engineering, Northwest Normal University, Lanzhou, Gansu, China"],"affiliations":[{"raw_affiliation_string":"College of Computer Science and Engineering, Northwest Normal University, Lanzhou, Gansu, China","institution_ids":["https://openalex.org/I68986083"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100701695","display_name":"Zhixin Li","orcid":null},"institutions":[{"id":"https://openalex.org/I29739308","display_name":"Guangxi Normal University","ror":"https://ror.org/02frt9q65","country_code":"CN","type":"education","lineage":["https://openalex.org/I29739308"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhixin Li","raw_affiliation_strings":["Key Laboratory of Education Blockchain and Intelligent Technology, Ministry of Education, Guangxi Normal University, Guilin, Guangxi, China","Ministry of Education, Key Laboratory of Education Blockchain and Intelligent Technology, Guangxi Normal University, Guilin, Guangxi, China"],"affiliations":[{"raw_affiliation_string":"Key Laboratory of Education Blockchain and Intelligent Technology, Ministry of Education, Guangxi Normal University, Guilin, Guangxi, China","institution_ids":["https://openalex.org/I29739308"]},{"raw_affiliation_string":"Ministry of Education, Key Laboratory of Education Blockchain and Intelligent Technology, Guangxi Normal University, Guilin, Guangxi, China","institution_ids":["https://openalex.org/I29739308"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5111277313"],"corresponding_institution_ids":["https://openalex.org/I68986083"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.43903379,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"63","issue":null,"first_page":"1","last_page":"22"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9889000058174133,"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":0.9889000058174133,"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":0.0017999999690800905,"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"}},{"id":"https://openalex.org/T11659","display_name":"Advanced Image Fusion Techniques","score":0.0013000000035390258,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.6794000267982483},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6524999737739563},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5651000142097473},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5360999703407288},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.517300009727478},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.4722999930381775},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.4674000144004822},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4546999931335449}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7354000210762024},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6951000094413757},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.6794000267982483},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6524999737739563},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5651000142097473},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5360999703407288},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.517300009727478},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.4722999930381775},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.4674000144004822},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4546999931335449},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.4480000138282776},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3467000126838684},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3433000147342682},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.3188000023365021},{"id":"https://openalex.org/C88230418","wikidata":"https://www.wikidata.org/wiki/Q131476","display_name":"Graph theory","level":2,"score":0.30799999833106995},{"id":"https://openalex.org/C48903430","wikidata":"https://www.wikidata.org/wiki/Q491370","display_name":"Graph partition","level":3,"score":0.3005000054836273},{"id":"https://openalex.org/C2780648208","wikidata":"https://www.wikidata.org/wiki/Q3001793","display_name":"Land cover","level":3,"score":0.3000999987125397},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.28200000524520874},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.26489999890327454},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.257999986410141}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tgrs.2025.3627204","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2025.3627204","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/G4054276619","display_name":null,"funder_award_id":"62441701","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5451957155","display_name":null,"funder_award_id":"62566059","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7602114162","display_name":null,"funder_award_id":"62567007","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":69,"referenced_works":["https://openalex.org/W2101640795","https://openalex.org/W2118246710","https://openalex.org/W2546942002","https://openalex.org/W2607603241","https://openalex.org/W2620998106","https://openalex.org/W2764276316","https://openalex.org/W2808098982","https://openalex.org/W2884276099","https://openalex.org/W2898204262","https://openalex.org/W2907147407","https://openalex.org/W2961290969","https://openalex.org/W2963854930","https://openalex.org/W2964105864","https://openalex.org/W2991616716","https://openalex.org/W2992641156","https://openalex.org/W3011495011","https://openalex.org/W3012405452","https://openalex.org/W3035442578","https://openalex.org/W3037613841","https://openalex.org/W3041133507","https://openalex.org/W3046698617","https://openalex.org/W3064134516","https://openalex.org/W3122028341","https://openalex.org/W3132867842","https://openalex.org/W3133055443","https://openalex.org/W3133271982","https://openalex.org/W3145049705","https://openalex.org/W3189063576","https://openalex.org/W3197866960","https://openalex.org/W3198529186","https://openalex.org/W3200760345","https://openalex.org/W3205614732","https://openalex.org/W3214821343","https://openalex.org/W4210734798","https://openalex.org/W4220841454","https://openalex.org/W4224919704","https://openalex.org/W4226038296","https://openalex.org/W4283760989","https://openalex.org/W4285106080","https://openalex.org/W4291727297","https://openalex.org/W4293193074","https://openalex.org/W4293731866","https://openalex.org/W4296339430","https://openalex.org/W4304481542","https://openalex.org/W4312455041","https://openalex.org/W4312636650","https://openalex.org/W4312815893","https://openalex.org/W4313598792","https://openalex.org/W4313627848","https://openalex.org/W4364323060","https://openalex.org/W4367359538","https://openalex.org/W4367835120","https://openalex.org/W4385154014","https://openalex.org/W4386598498","https://openalex.org/W4386634481","https://openalex.org/W4390097175","https://openalex.org/W4390691517","https://openalex.org/W4390871627","https://openalex.org/W4391020450","https://openalex.org/W4393182536","https://openalex.org/W4393405281","https://openalex.org/W4394585843","https://openalex.org/W4401413770","https://openalex.org/W4402912975","https://openalex.org/W4405023157","https://openalex.org/W4409494599","https://openalex.org/W4409537021","https://openalex.org/W4409761008","https://openalex.org/W4413887274"],"related_works":[],"abstract_inverted_index":{"HyperSpectral":[0],"Image":[1],"(HSI)":[2],"classification":[3,32,282],"aims":[4],"at":[5,65,109,293],"categorizing":[6],"each":[7,261],"pixel":[8,173],"in":[9,49,279],"an":[10,152,208],"HSI,":[11],"facilitating":[12],"precise":[13],"identification":[14],"and":[15,188,201,221,231,238,284],"differentiation":[16],"of":[17,73,219,229,240,281],"various":[18,192],"land":[19],"cover":[20],"types.":[21],"In":[22],"recent":[23],"years,":[24],"Graph":[25],"Neural":[26,178],"Networks":[27],"(GNNs)-based":[28],"methods":[29],"for":[30,103,119],"HSI":[31],"have":[33],"exhibited":[34],"impressive":[35],"performance":[36],"when":[37],"abundant":[38],"labeled":[39,54,265],"samples":[40,266],"are":[41],"available.":[42],"However,":[43],"their":[44],"effectiveness":[45],"is":[46,116,138,182,213,247,290],"notably":[47],"constrained":[48],"real-world":[50],"scenarios":[51],"with":[52,97,262],"limited":[53],"samples.":[55],"Furthermore,":[56],"current":[57],"approaches":[58],"predominantly":[59],"concentrate":[60],"on":[61,149,257],"extracting":[62],"global":[63,220],"features":[64,200],"the":[66,70,110,129,144,166,172,203,217,226,236,241,252,271],"superpixel":[67,111,158],"level,":[68,112,174],"overlooking":[69],"efficient":[71],"exploration":[72],"pixel-level":[74,198,204,232],"local":[75,186,199,222],"features,":[76,233],"leading":[77],"to":[78,184,249],"incomplete":[79],"feature":[80,168,205,242],"representations.":[81,243],"To":[82,127],"address":[83],"these":[84],"challenges,":[85],"we":[86],"introduce":[87],"a":[88,113,132,175],"novel":[89],"ensemble":[90,153,245],"approach":[91],"termed":[92],"Dual-Graph":[93],"Knowledge":[94],"Distillation":[95],"Integrated":[96],"Multi-Scale":[98],"CNN":[99],"(KDMSC),":[100],"specifically":[101],"designed":[102],"few-shot":[104],"hyperspectral":[105],"image":[106],"classification.":[107],"Specifically,":[108],"graph":[114,125,134,146],"structure":[115],"initially":[117],"constructed":[118],"segmented":[120],"superpixels,":[121,150],"enhancing":[122,235],"two":[123],"distinct":[124],"views.":[126],"amalgamate":[128],"dual-graph":[130],"model,":[131],"dual-student":[133],"knowledge":[135],"distillation":[136],"module":[137,141,181],"introduced.":[139],"This":[140,195,224],"intelligently":[142],"merges":[143],"dual":[145],"structures":[147],"based":[148],"creating":[151],"teacher":[154],"model":[155],"that":[156,270],"utilizes":[157],"predictions":[159],"as":[160],"supplementary":[161],"supervisory":[162],"signals,":[163],"thereby":[164],"bolstering":[165],"model\u2019s":[167],"learning":[169,246,277],"capabilities.":[170],"At":[171],"multi-scale":[176],"Convolutional":[177],"Network":[179],"(CNN)":[180],"integrated":[183],"capture":[185],"spatial":[187],"spectral":[189],"information":[190],"across":[191],"receptive":[193],"fields.":[194],"effectively":[196],"explores":[197],"enriches":[202],"representation.":[206],"Finally,":[207],"adaptive":[209],"weighted":[210],"fusion":[211,228],"strategy":[212],"proposed,":[214],"dynamically":[215],"adjusting":[216],"contribution":[218],"features.":[223],"enables":[225],"effective":[227],"superpixel-level":[230],"significantly":[234],"completeness":[237],"discriminability":[239],"Moreover,":[244],"introduced":[248],"further":[250],"enhance":[251],"method\u2019s":[253],"robustness.":[254],"Experimental":[255],"results":[256],"four":[258],"benchmark":[259],"datasets,":[260],"only":[263],"five":[264],"per":[267],"class,":[268],"demonstrate":[269],"proposed":[272],"method":[273],"surpasses":[274],"existing":[275],"deep":[276],"techniques":[278],"terms":[280],"accuracy":[283],"generalization":[285],"performance.":[286],"The":[287],"source":[288],"code":[289],"available":[291],"online":[292],"https://github.com/AXTX725/KDMSC.":[294]},"counts_by_year":[],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2025-10-30T00:00:00"}
