{"id":"https://openalex.org/W3092329251","doi":"https://doi.org/10.1109/tcyb.2020.3020540","title":"Symmetric All Convolutional Neural-Network-Based Unsupervised Feature Extraction for Hyperspectral Images Classification","display_name":"Symmetric All Convolutional Neural-Network-Based Unsupervised Feature Extraction for Hyperspectral Images Classification","publication_year":2020,"publication_date":"2020-10-07","ids":{"openalex":"https://openalex.org/W3092329251","doi":"https://doi.org/10.1109/tcyb.2020.3020540","mag":"3092329251"},"language":"en","primary_location":{"id":"doi:10.1109/tcyb.2020.3020540","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tcyb.2020.3020540","pdf_url":null,"source":{"id":"https://openalex.org/S4210191041","display_name":"IEEE Transactions on Cybernetics","issn_l":"2168-2267","issn":["2168-2267","2168-2275"],"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 Cybernetics","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/A5100432821","display_name":"Mingyang Zhang","orcid":"https://orcid.org/0000-0002-9768-516X"},"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":"Mingyang Zhang","raw_affiliation_strings":["School of Electronic Engineering, Xidian University, Xi’an, China","Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi'an, China"],"affiliations":[{"raw_affiliation_string":"School of Electronic Engineering, 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, Xidian University, Xi'an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5091227928","display_name":"Maoguo Gong","orcid":"https://orcid.org/0000-0002-0415-8556"},"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":"Maoguo Gong","raw_affiliation_strings":["School of Electronic Engineering, Xidian University, Xi’an, China","Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi'an, China"],"affiliations":[{"raw_affiliation_string":"School of Electronic Engineering, 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, Xidian University, Xi'an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100411297","display_name":"Haibo He","orcid":"https://orcid.org/0000-0002-5247-9370"},"institutions":[{"id":"https://openalex.org/I17626003","display_name":"University of Rhode Island","ror":"https://ror.org/013ckk937","country_code":"US","type":"education","lineage":["https://openalex.org/I17626003"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Haibo He","raw_affiliation_strings":["Department of Electrical, Computer and Biomedical Engineering, University of Rhode Island, Kingston, RI, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical, Computer and Biomedical Engineering, University of Rhode Island, Kingston, RI, USA","institution_ids":["https://openalex.org/I17626003"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5079377634","display_name":"Shengqi Zhu","orcid":"https://orcid.org/0000-0002-3119-4472"},"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":"Shengqi Zhu","raw_affiliation_strings":["National Laboratory of Radar Signal Processing, Xidian University, Xi’an, China"],"affiliations":[{"raw_affiliation_string":"National Laboratory of Radar Signal Processing, Xidian University, Xi’an, China","institution_ids":["https://openalex.org/I149594827"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5100432821"],"corresponding_institution_ids":["https://openalex.org/I149594827"],"apc_list":null,"apc_paid":null,"fwci":5.3444,"has_fulltext":false,"cited_by_count":41,"citation_normalized_percentile":{"value":0.96030833,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":99},"biblio":{"volume":"52","issue":"5","first_page":"2981","last_page":"2993"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":1.0,"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":1.0,"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/T13890","display_name":"Remote Sensing and Land Use","score":0.9947999715805054,"subfield":{"id":"https://openalex.org/subfields/1902","display_name":"Atmospheric Science"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9789999723434448,"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/upsampling","display_name":"Upsampling","score":0.8629206418991089},{"id":"https://openalex.org/keywords/subnetwork","display_name":"Subnetwork","score":0.7927874326705933},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7457755208015442},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7295252084732056},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7085933685302734},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.6941510438919067},{"id":"https://openalex.org/keywords/deconvolution","display_name":"Deconvolution","score":0.6895594000816345},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.6070229411125183},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5983245372772217},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.5848508477210999},{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.5498884916305542},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.5493570566177368},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5012176036834717},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4562685191631317},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.34496480226516724},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.2763490676879883},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.1670013964176178}],"concepts":[{"id":"https://openalex.org/C110384440","wikidata":"https://www.wikidata.org/wiki/Q1143270","display_name":"Upsampling","level":3,"score":0.8629206418991089},{"id":"https://openalex.org/C2780186347","wikidata":"https://www.wikidata.org/wiki/Q11414","display_name":"Subnetwork","level":2,"score":0.7927874326705933},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7457755208015442},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7295252084732056},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7085933685302734},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.6941510438919067},{"id":"https://openalex.org/C174576160","wikidata":"https://www.wikidata.org/wiki/Q1183700","display_name":"Deconvolution","level":2,"score":0.6895594000816345},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.6070229411125183},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5983245372772217},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.5848508477210999},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.5498884916305542},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.5493570566177368},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5012176036834717},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4562685191631317},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.34496480226516724},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2763490676879883},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.1670013964176178},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","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},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tcyb.2020.3020540","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tcyb.2020.3020540","pdf_url":null,"source":{"id":"https://openalex.org/S4210191041","display_name":"IEEE Transactions on Cybernetics","issn_l":"2168-2267","issn":["2168-2267","2168-2275"],"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 Cybernetics","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1965026428","display_name":null,"funder_award_id":"61906147","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G2331449583","display_name":null,"funder_award_id":"ECCS 1917275","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G4976278248","display_name":null,"funder_award_id":"2019JQ-471","funder_id":"https://openalex.org/F4320336567","funder_display_name":"Natural Science Basic Research Program of Shaanxi Province"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320336567","display_name":"Natural Science Basic Research Program of Shaanxi Province","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":52,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1535602073","https://openalex.org/W1555148682","https://openalex.org/W1600877088","https://openalex.org/W1799946925","https://openalex.org/W1849277567","https://openalex.org/W1971637299","https://openalex.org/W1985973695","https://openalex.org/W1993022127","https://openalex.org/W2018257962","https://openalex.org/W2029316659","https://openalex.org/W2041100636","https://openalex.org/W2050497921","https://openalex.org/W2068150776","https://openalex.org/W2087263574","https://openalex.org/W2090424610","https://openalex.org/W2100495367","https://openalex.org/W2110798204","https://openalex.org/W2120225005","https://openalex.org/W2156503248","https://openalex.org/W2159070926","https://openalex.org/W2159874418","https://openalex.org/W2163922914","https://openalex.org/W2170858770","https://openalex.org/W2179290474","https://openalex.org/W2183341477","https://openalex.org/W2314785379","https://openalex.org/W2345128667","https://openalex.org/W2412588858","https://openalex.org/W2500751094","https://openalex.org/W2506684654","https://openalex.org/W2518815253","https://openalex.org/W2548791488","https://openalex.org/W2603422184","https://openalex.org/W2737996023","https://openalex.org/W2757208835","https://openalex.org/W2764034829","https://openalex.org/W2764276316","https://openalex.org/W2769607111","https://openalex.org/W2782517596","https://openalex.org/W2782522152","https://openalex.org/W2801324747","https://openalex.org/W2890133123","https://openalex.org/W2900116731","https://openalex.org/W2908349615","https://openalex.org/W2920582597","https://openalex.org/W2940678725","https://openalex.org/W2962702700","https://openalex.org/W2963684088","https://openalex.org/W3014771378","https://openalex.org/W3083155260","https://openalex.org/W6676189307"],"related_works":["https://openalex.org/W2983142544","https://openalex.org/W2891059443","https://openalex.org/W4281663961","https://openalex.org/W3208888551","https://openalex.org/W4313561566","https://openalex.org/W3208386644","https://openalex.org/W4389832810","https://openalex.org/W4220682630","https://openalex.org/W3181622257","https://openalex.org/W3163146846"],"abstract_inverted_index":{"Recently,":[0],"deep-learning-based":[1,53],"feature":[2,153],"extraction":[3],"(FE)":[4],"methods":[5],"have":[6],"shown":[7],"great":[8],"potential":[9],"in":[10,47,61],"hyperspectral":[11],"image":[12],"(HSI)":[13],"processing.":[14],"Unfortunately,":[15],"it":[16],"also":[17],"brings":[18],"a":[19,50,74,138,143],"challenge":[20],"that":[21,162],"the":[22,25,80,109,128,131,163],"training":[23],"of":[24,33,69,79,130],"deep":[26],"learning":[27],"networks":[28],"always":[29],"requires":[30],"large":[31],"amounts":[32],"labeled":[34],"samples,":[35],"which":[36,58,125],"is":[37,56,59,83,147],"hardly":[38],"available":[39,159],"for":[40,85,172],"HSI":[41],"data.":[42],"To":[43,134],"address":[44],"this":[45,48,136],"issue,":[46],"article,":[49],"novel":[51,139],"unsupervised":[52],"FE":[54],"method":[55,165],"proposed,":[57],"trained":[60],"an":[62,70],"end-to-end":[63],"style.":[64],"The":[65,77],"proposed":[66,164],"framework":[67],"consists":[68],"encoder":[71,110],"subnetwork":[72,111],"and":[73,89,95,102,112,119,169],"decoder":[75,113],"subnetwork.":[76],"structure":[78,108],"two":[81],"subnetworks":[82],"symmetric":[84],"obtaining":[86],"better":[87],"downsampling":[88],"upsampling":[90],"representation.":[91,154],"Considering":[92],"both":[93],"spectral":[94],"spatial":[96],"information,":[97],"3-D":[98,117],"all":[99],"convolution":[100,118],"nets":[101,104],"deconvolution":[103,120],"are":[105],"used":[106],"to":[107,149],"subnetwork,":[114],"respectively.":[115],"However,":[116],"kernels":[121],"bring":[122],"more":[123,151],"parameters,":[124],"can":[126,166],"deteriorate":[127],"quality":[129],"obtained":[132],"features.":[133],"alleviate":[135],"problem,":[137],"cost":[140],"function":[141],"with":[142],"sparse":[144],"regular":[145],"term":[146],"designed":[148],"obtain":[150,167],"robust":[152,168],"Experimental":[155],"results":[156],"on":[157],"publicly":[158],"datasets":[160],"indicate":[161],"effective":[170],"features":[171],"subsequent":[173],"classification":[174],"tasks.":[175]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":9},{"year":2023,"cited_by_count":10},{"year":2022,"cited_by_count":10},{"year":2021,"cited_by_count":8}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2020-10-15T00:00:00"}