{"id":"https://openalex.org/W4323338598","doi":"https://doi.org/10.1109/tgrs.2023.3253248","title":"DSR-GCN: Differentiated-Scale Restricted Graph Convolutional Network for Few-Shot Hyperspectral Image Classification","display_name":"DSR-GCN: Differentiated-Scale Restricted Graph Convolutional Network for Few-Shot Hyperspectral Image Classification","publication_year":2023,"publication_date":"2023-01-01","ids":{"openalex":"https://openalex.org/W4323338598","doi":"https://doi.org/10.1109/tgrs.2023.3253248"},"language":"en","primary_location":{"id":"doi:10.1109/tgrs.2023.3253248","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2023.3253248","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/A5018243278","display_name":"Zhaohui Xue","orcid":"https://orcid.org/0000-0001-6253-2967"},"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":"Zhaohui Xue","raw_affiliation_strings":["School of Earth Sciences and Engineering, Hohai University, Nanjing, China","Jiangsu Province Engineering Research Center of Water Resources and Environment Assessment Using Remote Sensing, Hohai University, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"School of Earth Sciences and Engineering, Hohai University, Nanjing, China","institution_ids":["https://openalex.org/I163340411"]},{"raw_affiliation_string":"Jiangsu Province Engineering Research Center of Water Resources and Environment Assessment Using Remote Sensing, Hohai University, Nanjing, China","institution_ids":["https://openalex.org/I163340411"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100321249","display_name":"Zhi\u2010Wei Liu","orcid":"https://orcid.org/0000-0003-3005-1792"},"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":"Zhiwei Liu","raw_affiliation_strings":["School of Earth Sciences and Engineering, Hohai University, Nanjing, China","Jiangsu Province Engineering Research Center of Water Resources and Environment Assessment Using Remote Sensing, Hohai University, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"School of Earth Sciences and Engineering, Hohai University, Nanjing, China","institution_ids":["https://openalex.org/I163340411"]},{"raw_affiliation_string":"Jiangsu Province Engineering Research Center of Water Resources and Environment Assessment Using Remote Sensing, Hohai University, Nanjing, China","institution_ids":["https://openalex.org/I163340411"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5016879046","display_name":"Mengxue Zhang","orcid":"https://orcid.org/0000-0002-8587-4334"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mengxue Zhang","raw_affiliation_strings":["Image and Signal Processing (ISP) Group, University of València, València, Spain"],"affiliations":[{"raw_affiliation_string":"Image and Signal Processing (ISP) Group, University of València, València, Spain","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5018243278"],"corresponding_institution_ids":["https://openalex.org/I163340411"],"apc_list":null,"apc_paid":null,"fwci":5.6871,"has_fulltext":false,"cited_by_count":35,"citation_normalized_percentile":{"value":0.96460919,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":100},"biblio":{"volume":"61","issue":null,"first_page":"1","last_page":"18"},"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/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9962000250816345,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9714000225067139,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.7585511207580566},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.6747475862503052},{"id":"https://openalex.org/keywords/adjacency-list","display_name":"Adjacency list","score":0.6413273811340332},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6340417861938477},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6024848818778992},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5960970520973206},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.516382098197937},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.5010669231414795},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.42649444937705994},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.301655113697052},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.17016959190368652},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.11483734846115112}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.7585511207580566},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.6747475862503052},{"id":"https://openalex.org/C110484373","wikidata":"https://www.wikidata.org/wiki/Q264398","display_name":"Adjacency list","level":2,"score":0.6413273811340332},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6340417861938477},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6024848818778992},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5960970520973206},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.516382098197937},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.5010669231414795},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.42649444937705994},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.301655113697052},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.17016959190368652},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.11483734846115112}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tgrs.2023.3253248","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2023.3253248","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":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.6800000071525574}],"awards":[{"id":"https://openalex.org/G179363741","display_name":null,"funder_award_id":"41971279","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G2659737875","display_name":null,"funder_award_id":"42271324","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5116539810","display_name":null,"funder_award_id":"BK20221506","funder_id":"https://openalex.org/F4320322769","funder_display_name":"Natural Science Foundation of Jiangsu Province"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320322769","display_name":"Natural Science Foundation of Jiangsu Province","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":71,"referenced_works":["https://openalex.org/W1521436688","https://openalex.org/W1930624869","https://openalex.org/W1950365613","https://openalex.org/W2029316659","https://openalex.org/W2048281487","https://openalex.org/W2069231830","https://openalex.org/W2090424610","https://openalex.org/W2118246710","https://openalex.org/W2136251662","https://openalex.org/W2165698076","https://openalex.org/W2345118402","https://openalex.org/W2464755555","https://openalex.org/W2500751094","https://openalex.org/W2546942002","https://openalex.org/W2548037809","https://openalex.org/W2574144551","https://openalex.org/W2602024454","https://openalex.org/W2752782242","https://openalex.org/W2764276316","https://openalex.org/W2768537477","https://openalex.org/W2791006446","https://openalex.org/W2800371750","https://openalex.org/W2804902458","https://openalex.org/W2892621946","https://openalex.org/W2894165434","https://openalex.org/W2898204262","https://openalex.org/W2899747753","https://openalex.org/W2903158431","https://openalex.org/W2907100627","https://openalex.org/W2940678725","https://openalex.org/W2947471972","https://openalex.org/W2967667754","https://openalex.org/W2971432438","https://openalex.org/W2991286101","https://openalex.org/W2991494819","https://openalex.org/W2996457415","https://openalex.org/W2998247074","https://openalex.org/W3013493337","https://openalex.org/W3036589244","https://openalex.org/W3037613841","https://openalex.org/W3043181422","https://openalex.org/W3046476394","https://openalex.org/W3047443805","https://openalex.org/W3048029844","https://openalex.org/W3064134516","https://openalex.org/W3066454894","https://openalex.org/W3088800454","https://openalex.org/W3103695279","https://openalex.org/W3105492824","https://openalex.org/W3106294914","https://openalex.org/W3107591966","https://openalex.org/W3120356488","https://openalex.org/W3127249031","https://openalex.org/W3128077822","https://openalex.org/W3132867842","https://openalex.org/W3138725786","https://openalex.org/W3194691225","https://openalex.org/W3197529035","https://openalex.org/W3200760345","https://openalex.org/W3200799048","https://openalex.org/W3204445362","https://openalex.org/W3205614732","https://openalex.org/W3215588016","https://openalex.org/W4210692941","https://openalex.org/W4210794570","https://openalex.org/W4213420036","https://openalex.org/W4225630686","https://openalex.org/W4240485910","https://openalex.org/W4285106080","https://openalex.org/W6743446608","https://openalex.org/W6743731764"],"related_works":["https://openalex.org/W3034655717","https://openalex.org/W2546645752","https://openalex.org/W3173596272","https://openalex.org/W2028628118","https://openalex.org/W2546871836","https://openalex.org/W2136485282","https://openalex.org/W2986832195","https://openalex.org/W4293057251","https://openalex.org/W1997912302","https://openalex.org/W4361305700"],"abstract_inverted_index":{"Graph":[0],"convolution":[1],"networks":[2],"(GCNs)":[3],"have":[4],"shown":[5],"great":[6],"potentials":[7],"for":[8,50],"few-shot":[9,123],"hyperspectral":[10],"image":[11],"(HSI)":[12],"classification.":[13,52],"Mainstream":[14],"GCNs":[15],"construct":[16],"graph":[17,58],"according":[18],"to":[19,33,77,96],"single":[20],"scale":[21],"segmentation,":[22],"which":[23],"usually":[24],"ignores":[25],"subtle":[26,64],"adjacency":[27],"relation":[28],"between":[29],"small":[30],"regions,":[31],"leading":[32],"unreliable":[34],"initial":[35],"local":[36,98],"graph.":[37],"To":[38],"overcome":[39],"the":[40,63,79,139,153],"above":[41],"issue,":[42],"we":[43,54,89],"propose":[44,55],"a":[45,56,91,110],"differentiated-scale":[46,57,85],"restricted":[47,72],"GCN":[48,86],"(DSR-GCN)":[49],"HSI":[51],"Firstly,":[53],"construction":[59],"method":[60],"considering":[61],"both":[62],"and":[65,112,115,149,170],"relative":[66],"wider":[67],"range":[68],"spectral-spatial":[69],"relation.":[70],"Secondly,":[71],"fusion":[73,80],"loss":[74],"is":[75],"designed":[76],"restrict":[78],"of":[81,146,157,166,173],"features":[82,121],"extracted":[83],"with":[84,109,152],"branches.":[87],"Finally,":[88],"design":[90],"lightweight":[92],"spatial-spectral":[93],"siamese":[94],"network":[95],"remedy":[97],"pixel-level":[99],"features.":[100],"The":[101],"proposed":[102],"DSR-GCN":[103,137],"can":[104,117],"better":[105],"model":[106],"spatial":[107],"structure":[108],"reliable":[111],"refined":[113],"graph,":[114],"it":[116],"capture":[118],"more":[119],"discriminate":[120],"in":[122,144,155],"learning":[124,142],"(FSL)":[125],"scenario.":[126],"Extensive":[127],"experiments":[128],"conducted":[129],"on":[130],"four":[131],"benchmark":[132],"data":[133],"sets":[134],"demonstrate":[135],"that":[136],"outperforms":[138],"other":[140],"deep":[141],"methods":[143],"terms":[145,156],"classification":[147],"accuracy":[148],"generalization":[150],"performance,":[151],"improvements":[154],"OA":[158],"around":[159],"6.20%~23.41%":[160],"(Indian":[161],"Pines),":[162],"4.45%~":[163],"16.48%":[164],"(University":[165,172],"Pavia),":[167],"4.25%~11.85%":[168],"(Salinas),":[169],"2.0%~17.23%":[171],"Houston)":[174],"under":[175],"5":[176],"labeled":[177],"samples":[178],"per":[179],"class.":[180]},"counts_by_year":[{"year":2025,"cited_by_count":11},{"year":2024,"cited_by_count":19},{"year":2023,"cited_by_count":5}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}