{"id":"https://openalex.org/W3049194204","doi":"https://doi.org/10.1109/lgrs.2020.3013707","title":"A Two-Branch Network Combined With Robust Principal Component Analysis for Hyperspectral Image Classification","display_name":"A Two-Branch Network Combined With Robust Principal Component Analysis for Hyperspectral Image Classification","publication_year":2020,"publication_date":"2020-08-14","ids":{"openalex":"https://openalex.org/W3049194204","doi":"https://doi.org/10.1109/lgrs.2020.3013707","mag":"3049194204"},"language":"en","primary_location":{"id":"doi:10.1109/lgrs.2020.3013707","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2020.3013707","pdf_url":null,"source":{"id":"https://openalex.org/S126920919","display_name":"IEEE Geoscience and Remote Sensing Letters","issn_l":"1545-598X","issn":["1545-598X","1558-0571"],"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 Geoscience and Remote Sensing Letters","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/A5000814301","display_name":"Caihong Mu","orcid":"https://orcid.org/0000-0003-4373-3661"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Caihong Mu","raw_affiliation_strings":["Joint International Research Laboratory of Intelligent Perception and Computation, School of Artificial Intelligence, Xidian University, Xi\u2019an, China","Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, International Research Center for Intelligent Perception and Computation, Xidian University, Xi\u2019an, China","Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, International Research Center for Intelligent Perception and Computation, Xidian University, Xi'an, China","Joint International Research Laboratory of Intelligent Perception and Computation, School of Artificial Intelligence, Xidian University, Xi'an, China"],"raw_orcid":"https://orcid.org/0000-0003-4373-3661","affiliations":[{"raw_affiliation_string":"Joint International Research Laboratory of Intelligent Perception and Computation, School of Artificial Intelligence, Xidian University, Xi\u2019an, China","institution_ids":["https://openalex.org/I149594827"]},{"raw_affiliation_string":"Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, International Research Center for Intelligent Perception and Computation, Xidian University, Xi\u2019an, China","institution_ids":["https://openalex.org/I149594827"]},{"raw_affiliation_string":"Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, International Research Center for Intelligent Perception and Computation, Xidian University, Xi'an, China","institution_ids":["https://openalex.org/I149594827"]},{"raw_affiliation_string":"Joint International Research Laboratory of Intelligent Perception and Computation, School of Artificial Intelligence, Xidian University, Xi'an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016984455","display_name":"Qize Zeng","orcid":"https://orcid.org/0000-0002-2672-2015"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qize Zeng","raw_affiliation_strings":["Joint International Research Laboratory of Intelligent Perception and Computation, School of Artificial Intelligence, Xidian University, Xi\u2019an, China","Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, International Research Center for Intelligent Perception and Computation, Xidian University, Xi\u2019an, China","Joint International Research Laboratory of Intelligent Perception and Computation, School of Artificial Intelligence, Xidian University, Xi'an, China","Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, International Research Center for Intelligent Perception and Computation, Xidian University, Xi'an, China"],"raw_orcid":"https://orcid.org/0000-0002-2672-2015","affiliations":[{"raw_affiliation_string":"Joint International Research Laboratory of Intelligent Perception and Computation, School of Artificial Intelligence, Xidian University, Xi\u2019an, China","institution_ids":["https://openalex.org/I149594827"]},{"raw_affiliation_string":"Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, International Research Center for Intelligent Perception and Computation, Xidian University, Xi\u2019an, China","institution_ids":["https://openalex.org/I149594827"]},{"raw_affiliation_string":"Joint International Research Laboratory of Intelligent Perception and Computation, School of Artificial Intelligence, Xidian University, Xi'an, China","institution_ids":["https://openalex.org/I149594827"]},{"raw_affiliation_string":"Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, International Research Center for Intelligent Perception and Computation, Xidian University, Xi'an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100695128","display_name":"Yi Liu","orcid":"https://orcid.org/0000-0001-9993-0731"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yi Liu","raw_affiliation_strings":["School of Electronic Engineering, Xidian University, Xi\u2019an, China","School of Electronic Engineering, Xidian University, Xi'an, China"],"raw_orcid":"https://orcid.org/0000-0001-9993-0731","affiliations":[{"raw_affiliation_string":"School of Electronic Engineering, Xidian University, Xi\u2019an, China","institution_ids":["https://openalex.org/I149594827"]},{"raw_affiliation_string":"School of Electronic Engineering, Xidian University, Xi'an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5025695399","display_name":"Yi Qu","orcid":"https://orcid.org/0000-0002-7773-8949"},"institutions":[{"id":"https://openalex.org/I32394136","display_name":"Northumbria University","ror":"https://ror.org/049e6bc10","country_code":"GB","type":"education","lineage":["https://openalex.org/I32394136"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Yi Qu","raw_affiliation_strings":["Newcastle Business School, Northumbria University, Newcastle upon Tyne, U.K"],"raw_orcid":"https://orcid.org/0000-0002-7773-8949","affiliations":[{"raw_affiliation_string":"Newcastle Business School, Northumbria University, Newcastle upon Tyne, U.K","institution_ids":["https://openalex.org/I32394136"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5000814301"],"corresponding_institution_ids":["https://openalex.org/I149594827"],"apc_list":null,"apc_paid":null,"fwci":1.3381,"has_fulltext":false,"cited_by_count":12,"citation_normalized_percentile":{"value":0.85856421,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"18","issue":"12","first_page":"2147","last_page":"2151"},"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":0.9988999962806702,"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.9980000257492065,"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/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7541947364807129},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.719328761100769},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6967445611953735},{"id":"https://openalex.org/keywords/robust-principal-component-analysis","display_name":"Robust principal component analysis","score":0.663256049156189},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.6495920419692993},{"id":"https://openalex.org/keywords/principal-component-analysis","display_name":"Principal component analysis","score":0.5881847143173218},{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.5079558491706848},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.46471914649009705},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.45856913924217224},{"id":"https://openalex.org/keywords/subspace-topology","display_name":"Subspace topology","score":0.4518314599990845},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.4245079755783081},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3245600163936615}],"concepts":[{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7541947364807129},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.719328761100769},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6967445611953735},{"id":"https://openalex.org/C2777749129","wikidata":"https://www.wikidata.org/wiki/Q17148469","display_name":"Robust principal component analysis","level":3,"score":0.663256049156189},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.6495920419692993},{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.5881847143173218},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.5079558491706848},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.46471914649009705},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.45856913924217224},{"id":"https://openalex.org/C32834561","wikidata":"https://www.wikidata.org/wiki/Q660730","display_name":"Subspace topology","level":2,"score":0.4518314599990845},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.4245079755783081},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3245600163936615},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/lgrs.2020.3013707","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2020.3013707","pdf_url":null,"source":{"id":"https://openalex.org/S126920919","display_name":"IEEE Geoscience and Remote Sensing Letters","issn_l":"1545-598X","issn":["1545-598X","1558-0571"],"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 Geoscience and Remote Sensing Letters","raw_type":"journal-article"},{"id":"pmh:oai:nrl.northumbria.ac.uk:44371","is_oa":false,"landing_page_url":"http://nrl.northumbria.ac.uk/id/eprint/44371/","pdf_url":null,"source":{"id":"https://openalex.org/S4306401884","display_name":"Northumbria Research Link (Northumbria University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I32394136","host_organization_name":"Northumbria University","host_organization_lineage":["https://openalex.org/I32394136"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1149346757","display_name":null,"funder_award_id":"61773304","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G1840888965","display_name":null,"funder_award_id":"61773300","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G1957488185","display_name":null,"funder_award_id":"61672405","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G240101957","display_name":null,"funder_award_id":"61876141","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G2552413355","display_name":null,"funder_award_id":"U1701267","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G4210063020","display_name":null,"funder_award_id":"61772399","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":22,"referenced_works":["https://openalex.org/W1539062876","https://openalex.org/W2040511445","https://openalex.org/W2056370875","https://openalex.org/W2145962650","https://openalex.org/W2164330327","https://openalex.org/W2579929594","https://openalex.org/W2586898334","https://openalex.org/W2614256707","https://openalex.org/W2623518586","https://openalex.org/W2735711969","https://openalex.org/W2773415061","https://openalex.org/W2808098982","https://openalex.org/W2810707356","https://openalex.org/W2884276099","https://openalex.org/W2897452159","https://openalex.org/W2916206107","https://openalex.org/W2987315422","https://openalex.org/W3046027728","https://openalex.org/W3099831940","https://openalex.org/W3103753223","https://openalex.org/W3105357426","https://openalex.org/W3122774149"],"related_works":["https://openalex.org/W1585144779","https://openalex.org/W2909119362","https://openalex.org/W2602031553","https://openalex.org/W2065962751","https://openalex.org/W3154145980","https://openalex.org/W2136016640","https://openalex.org/W3173596272","https://openalex.org/W2354332871","https://openalex.org/W2935967304","https://openalex.org/W2075527894"],"abstract_inverted_index":{"Noise":[0],"in":[1,31,36,49,109,153],"hyperspectral":[2],"images":[3],"(HSIs)":[4],"may":[5],"degrade":[6],"the":[7,95,99,137,147,154,169,193,220],"HSI":[8,37,78,165],"classification":[9,79,155,166,179,217],"result.":[10],"Robust":[11],"principal":[12],"component":[13,97,101],"analysis":[14],"(RPCA)":[15],"is":[16,28,56,62],"an":[17,181],"excellent":[18],"method":[19,76,159,195],"to":[20,135,145],"obtain":[21],"low-rank":[22],"(LR)":[23],"representation":[24,205],"of":[25,150,222],"data":[26,40,189],"and":[27,34,64,98,104,128,171,178,206,225],"widely":[29],"used":[30,105],"image":[32],"denoising":[33],"also":[35],"classification.":[38],"However,":[39],"are":[41,102,132,143],"drawn":[42],"as":[43],"a":[44,73,90,129,161],"union":[45],"from":[46],"multiple":[47],"subspaces":[48],"HSIs,":[50],"so":[51],"LR":[52,96,204],"subspace":[53],"estimation":[54],"(LRSE)":[55],"necessary":[57],"when":[58],"using":[59,122],"RPCA,":[60,85],"which":[61,86],"complicated":[63],"time-consuming.":[65],"To":[66],"solve":[67],"this":[68,70],"problem,":[69],"letter":[71],"proposes":[72],"novel":[74],"LR-based":[75],"for":[77,106,164,219],"called":[80],"two-branch":[81],"network":[82],"combined":[83],"with":[84,89],"combines":[87],"RPCA":[88],"neural":[91],"network.":[92,183],"Specifically,":[93],"both":[94],"sparse":[100],"preserved":[103],"feature":[107,138,174,176],"extraction":[108],"two":[110],"independent":[111],"convolutional":[112],"branches.":[113],"This":[114],"way,":[115],"we":[116],"can":[117],"avoid":[118],"information":[119],"loss":[120],"without":[121],"accurate":[123],"LRSE.":[124],"A":[125],"concatenate":[126],"operation":[127],"pointwise":[130],"convolution":[131],"then":[133],"adopted":[134],"realize":[136],"fusion.":[139],"Finally,":[140],"fused":[141],"features":[142],"constructed":[144],"indicate":[146],"ground":[148],"truth":[149],"each":[151],"pixel":[152],"process.":[156],"The":[157,184],"proposed":[158,194],"constructs":[160],"convenient":[162],"model":[163],"by":[167],"discarding":[168],"LRSE":[170],"combining":[172],"denoising,":[173],"extraction,":[175],"fusion,":[177],"into":[180],"end-to-end":[182],"experimental":[185],"results":[186],"on":[187,203,209],"three":[188],"sets":[190],"demonstrate":[191],"that":[192],"outperforms":[196],"many":[197],"state-of-the-art":[198],"methods":[199],"including":[200],"ones":[201,207],"based":[202,208],"deep":[210],"learning.":[211],"In":[212],"addition,":[213],"it":[214],"maintains":[215],"good":[216],"performance":[218],"cases":[221],"small":[223],"samples":[224],"class":[226],"imbalance.":[227]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
