{"id":"https://openalex.org/W4312524730","doi":"https://doi.org/10.1109/tgrs.2022.3217406","title":"A Group-Based Embedding Learning and Integration Network for Hyperspectral Image Super-Resolution","display_name":"A Group-Based Embedding Learning and Integration Network for Hyperspectral Image Super-Resolution","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W4312524730","doi":"https://doi.org/10.1109/tgrs.2022.3217406"},"language":"en","primary_location":{"id":"doi:10.1109/tgrs.2022.3217406","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2022.3217406","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/A5101552051","display_name":"Xinya Wang","orcid":"https://orcid.org/0000-0003-2144-9811"},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xinya Wang","raw_affiliation_strings":["Electronic Information School, Wuhan University, Wuhan, China"],"affiliations":[{"raw_affiliation_string":"Electronic Information School, Wuhan University, Wuhan, China","institution_ids":["https://openalex.org/I37461747"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5091867249","display_name":"Qian Hu","orcid":"https://orcid.org/0009-0005-7613-988X"},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qian Hu","raw_affiliation_strings":["Electronic Information School, Wuhan University, Wuhan, China"],"affiliations":[{"raw_affiliation_string":"Electronic Information School, Wuhan University, Wuhan, China","institution_ids":["https://openalex.org/I37461747"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087165831","display_name":"Junjun Jiang","orcid":"https://orcid.org/0000-0002-5694-505X"},"institutions":[{"id":"https://openalex.org/I204983213","display_name":"Harbin Institute of Technology","ror":"https://ror.org/01yqg2h08","country_code":"CN","type":"education","lineage":["https://openalex.org/I204983213"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Junjun Jiang","raw_affiliation_strings":["School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China","institution_ids":["https://openalex.org/I204983213"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5040010053","display_name":"Jiayi Ma","orcid":"https://orcid.org/0000-0003-3264-3265"},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiayi Ma","raw_affiliation_strings":["Electronic Information School, Wuhan University, Wuhan, China"],"affiliations":[{"raw_affiliation_string":"Electronic Information School, Wuhan University, Wuhan, China","institution_ids":["https://openalex.org/I37461747"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5101552051"],"corresponding_institution_ids":["https://openalex.org/I37461747"],"apc_list":null,"apc_paid":null,"fwci":4.2797,"has_fulltext":false,"cited_by_count":38,"citation_normalized_percentile":{"value":0.95071239,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":100},"biblio":{"volume":"60","issue":null,"first_page":"1","last_page":"16"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11659","display_name":"Advanced Image Fusion Techniques","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/T11659","display_name":"Advanced Image Fusion Techniques","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/T11105","display_name":"Advanced Image Processing Techniques","score":0.9997000098228455,"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"}},{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.9990000128746033,"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":0.9348845481872559},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7112636566162109},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6968700885772705},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.605542778968811},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5741370916366577},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.5521984696388245},{"id":"https://openalex.org/keywords/image-resolution","display_name":"Image resolution","score":0.5358579754829407},{"id":"https://openalex.org/keywords/full-spectral-imaging","display_name":"Full spectral imaging","score":0.5301754474639893},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.47043344378471375},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.43333807587623596},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.4292508661746979},{"id":"https://openalex.org/keywords/iterative-reconstruction","display_name":"Iterative reconstruction","score":0.4290462136268616},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.13881516456604004}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.9348845481872559},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7112636566162109},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6968700885772705},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.605542778968811},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5741370916366577},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.5521984696388245},{"id":"https://openalex.org/C205372480","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"Image resolution","level":2,"score":0.5358579754829407},{"id":"https://openalex.org/C78660771","wikidata":"https://www.wikidata.org/wiki/Q5508206","display_name":"Full spectral imaging","level":3,"score":0.5301754474639893},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.47043344378471375},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.43333807587623596},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.4292508661746979},{"id":"https://openalex.org/C141379421","wikidata":"https://www.wikidata.org/wiki/Q6094427","display_name":"Iterative reconstruction","level":2,"score":0.4290462136268616},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.13881516456604004}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tgrs.2022.3217406","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2022.3217406","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/G415731887","display_name":null,"funder_award_id":"62276192","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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":54,"referenced_works":["https://openalex.org/W1885185971","https://openalex.org/W1916874600","https://openalex.org/W1988386267","https://openalex.org/W2022470997","https://openalex.org/W2055225600","https://openalex.org/W2067042811","https://openalex.org/W2077865152","https://openalex.org/W2097259623","https://openalex.org/W2100109944","https://openalex.org/W2165709183","https://openalex.org/W2214802144","https://openalex.org/W2242218935","https://openalex.org/W2494274259","https://openalex.org/W2520430674","https://openalex.org/W2588000623","https://openalex.org/W2607041014","https://openalex.org/W2747898905","https://openalex.org/W2752716059","https://openalex.org/W2761176843","https://openalex.org/W2767522909","https://openalex.org/W2773850265","https://openalex.org/W2792144524","https://openalex.org/W2804744787","https://openalex.org/W2866634454","https://openalex.org/W2898062170","https://openalex.org/W2931993473","https://openalex.org/W2954930822","https://openalex.org/W2963284277","https://openalex.org/W2963372104","https://openalex.org/W2963470893","https://openalex.org/W2963729050","https://openalex.org/W2964101377","https://openalex.org/W2964277374","https://openalex.org/W2981151698","https://openalex.org/W3025440211","https://openalex.org/W3027120144","https://openalex.org/W3034752215","https://openalex.org/W3076169370","https://openalex.org/W3102745911","https://openalex.org/W3109953061","https://openalex.org/W3123265576","https://openalex.org/W3136032573","https://openalex.org/W3172165319","https://openalex.org/W3174531399","https://openalex.org/W3193508667","https://openalex.org/W3196594491","https://openalex.org/W3208460268","https://openalex.org/W3217526930","https://openalex.org/W4283732315","https://openalex.org/W4294811193","https://openalex.org/W6753074096","https://openalex.org/W6782282802","https://openalex.org/W6797243835","https://openalex.org/W6804892872"],"related_works":["https://openalex.org/W4386427838","https://openalex.org/W2533019003","https://openalex.org/W2800956885","https://openalex.org/W2626158795","https://openalex.org/W2057283258","https://openalex.org/W1788560349","https://openalex.org/W2324845311","https://openalex.org/W2391021239","https://openalex.org/W2579567122","https://openalex.org/W1990800631"],"abstract_inverted_index":{"Although":[0],"natural":[1,188],"image":[2,11,28,105,173,182],"super-resolution":[3,12,29,106],"methods":[4,30,46,79,204],"have":[5],"achieved":[6],"impressive":[7],"performance,":[8],"single":[9,26,103],"hyperspectral":[10,27,73,104,128,192],"still":[13,47],"remains":[14],"a":[15,100,115,131,157],"challenge":[16],"due":[17],"to":[18,35,110,138,164,175,201],"the":[19,32,37,41,55,63,68,76,120,145,151,167,180,196],"high":[20,152],"dimensionality.":[21],"In":[22,95],"recent":[23],"years,":[24],"many":[25],"adopted":[31],"group-convolution":[33],"strategy":[34],"design":[36],"network":[38],"for":[39,127],"reducing":[40],"computational":[42],"burden.":[43],"However,":[44],"these":[45],"process":[48],"all":[49],"spectral":[50],"bands":[51],"at":[52],"once":[53],"during":[54],"deep":[56],"feature":[57,123],"extraction":[58,124],"and":[59,125,143,189,207],"reconstruction,":[60],"which":[61,118],"increases":[62],"difficulty":[64,121],"of":[65,72,83,122],"fully":[66,165],"exploring":[67],"inherent":[69],"data":[70],"characteristic":[71],"images.":[74,129],"Moreover,":[75],"advanced":[77],"group-based":[78,102],"make":[80],"insufficient":[81],"exploitation":[82],"complementary":[84,168],"information":[85,169],"contained":[86,170],"in":[87,91,114,171,179],"different":[88,155],"bands,":[89,156],"resulting":[90],"limited":[92],"reconstruction":[93,126],"performance.":[94],"this":[96],"paper,":[97],"we":[98],"propose":[99],"novel":[101],"method":[107,198],"termed":[108],"GELIN":[109],"reconstruct":[111],"high-resolution":[112],"images":[113],"group-by-group":[116],"manner,":[117],"alleviates":[119],"Specifically,":[130],"spatial-spectral":[132],"embedding":[133],"learning":[134],"module":[135,161],"is":[136,162,199],"designed":[137],"extract":[139],"rewarding":[140],"spatial":[141],"details":[142,178],"explore":[144],"correlations":[146],"among":[147,154],"spectra":[148],"simultaneously.":[149],"Considering":[150],"similarity":[153],"neighboring":[158,172],"group":[159],"integration":[160],"proposed":[163,197],"exploit":[166],"groups":[174],"recover":[176],"missing":[177],"target":[181],"group.":[183],"Experimental":[184],"results":[185],"on":[186],"both":[187,205],"remote":[190],"sensing":[191],"datasets":[193],"demonstrate":[194],"that":[195],"superior":[200],"other":[202],"state-of-the-art":[203],"visually":[206],"metrically.":[208]},"counts_by_year":[{"year":2025,"cited_by_count":15},{"year":2024,"cited_by_count":16},{"year":2023,"cited_by_count":7}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}
