{"id":"https://openalex.org/W3134832764","doi":"https://doi.org/10.1109/jstsp.2021.3063805","title":"Lightweight Tensor Attention-Driven ConvLSTM Neural Network for Hyperspectral Image Classification","display_name":"Lightweight Tensor Attention-Driven ConvLSTM Neural Network for Hyperspectral Image Classification","publication_year":2021,"publication_date":"2021-03-04","ids":{"openalex":"https://openalex.org/W3134832764","doi":"https://doi.org/10.1109/jstsp.2021.3063805","mag":"3134832764"},"language":"en","primary_location":{"id":"doi:10.1109/jstsp.2021.3063805","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jstsp.2021.3063805","pdf_url":null,"source":{"id":"https://openalex.org/S42167783","display_name":"IEEE Journal of Selected Topics in Signal Processing","issn_l":"1932-4553","issn":["1932-4553","1941-0484"],"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 Journal of Selected Topics in Signal Processing","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/A5067999166","display_name":"Wen-Shuai Hu","orcid":null},"institutions":[{"id":"https://openalex.org/I4800084","display_name":"Southwest Jiaotong University","ror":"https://ror.org/00hn7w693","country_code":"CN","type":"education","lineage":["https://openalex.org/I4800084"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wen-Shuai Hu","raw_affiliation_strings":["School of Information Science and Technology, Southwest Jiaotong University, Chengdu, China"],"raw_orcid":"https://orcid.org/0000-0002-4757-2765","affiliations":[{"raw_affiliation_string":"School of Information Science and Technology, Southwest Jiaotong University, Chengdu, China","institution_ids":["https://openalex.org/I4800084"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015155189","display_name":"Heng-Chao Li","orcid":"https://orcid.org/0000-0002-9735-570X"},"institutions":[{"id":"https://openalex.org/I4800084","display_name":"Southwest Jiaotong University","ror":"https://ror.org/00hn7w693","country_code":"CN","type":"education","lineage":["https://openalex.org/I4800084"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Heng-Chao Li","raw_affiliation_strings":["School of Information Science and Technology, Southwest Jiaotong University, Chengdu, China"],"raw_orcid":"https://orcid.org/0000-0002-9735-570X","affiliations":[{"raw_affiliation_string":"School of Information Science and Technology, Southwest Jiaotong University, Chengdu, China","institution_ids":["https://openalex.org/I4800084"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021308087","display_name":"Yang\u2010Jun Deng","orcid":"https://orcid.org/0000-0003-2532-1567"},"institutions":[{"id":"https://openalex.org/I52180223","display_name":"Hunan Agricultural University","ror":"https://ror.org/01dzed356","country_code":"CN","type":"education","lineage":["https://openalex.org/I52180223"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yang-Jun Deng","raw_affiliation_strings":["College of Information and Intelligence, Hunan Agricultural University, Changsha, China"],"raw_orcid":"https://orcid.org/0000-0003-2532-1567","affiliations":[{"raw_affiliation_string":"College of Information and Intelligence, Hunan Agricultural University, Changsha, China","institution_ids":["https://openalex.org/I52180223"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5005905514","display_name":"Xian Sun","orcid":"https://orcid.org/0000-0002-0038-9816"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210137199","display_name":"Aerospace Information Research Institute","ror":"https://ror.org/0419fj215","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210137199"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xian Sun","raw_affiliation_strings":["Aerospace Information Research Institute, Key Laboratory of Network Information System Technology (NIST), Chinese Academy of Sciences, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-0038-9816","affiliations":[{"raw_affiliation_string":"Aerospace Information Research Institute, Key Laboratory of Network Information System Technology (NIST), Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210137199","https://openalex.org/I19820366"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033017179","display_name":"Qian Du","orcid":"https://orcid.org/0000-0001-8354-7500"},"institutions":[{"id":"https://openalex.org/I99041443","display_name":"Mississippi State University","ror":"https://ror.org/0432jq872","country_code":"US","type":"education","lineage":["https://openalex.org/I4210141039","https://openalex.org/I99041443"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Qian Du","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Mississippi State University, Mississippi State, MS, USA"],"raw_orcid":"https://orcid.org/0000-0001-8354-7500","affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Mississippi State University, Mississippi State, MS, USA","institution_ids":["https://openalex.org/I99041443"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5054292278","display_name":"Antonio Plaza","orcid":"https://orcid.org/0000-0002-9613-1659"},"institutions":[{"id":"https://openalex.org/I80606768","display_name":"Universidad de Extremadura","ror":"https://ror.org/0174shg90","country_code":"ES","type":"education","lineage":["https://openalex.org/I80606768"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Antonio Plaza","raw_affiliation_strings":["Department of Technology of Computers and Communications, Escuela Polit\u00e9cnica, Hyperspectral Computing Laboratory, University of Extremadura, C\u00e1ceres, Spain"],"raw_orcid":"https://orcid.org/0000-0002-9613-1659","affiliations":[{"raw_affiliation_string":"Department of Technology of Computers and Communications, Escuela Polit\u00e9cnica, Hyperspectral Computing Laboratory, University of Extremadura, C\u00e1ceres, Spain","institution_ids":["https://openalex.org/I80606768"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":3.6694,"has_fulltext":false,"cited_by_count":40,"citation_normalized_percentile":{"value":0.93446415,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":95,"max":99},"biblio":{"volume":"15","issue":"3","first_page":"734","last_page":"745"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9986000061035156,"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":0.9986000061035156,"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/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9916999936103821,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"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/T12303","display_name":"Tensor decomposition and applications","score":0.9908999800682068,"subfield":{"id":"https://openalex.org/subfields/2605","display_name":"Computational Mathematics"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7557132244110107},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7144097685813904},{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.678754448890686},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6069176197052002},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5816184878349304},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5455876588821411},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.5179897546768188},{"id":"https://openalex.org/keywords/tensor-decomposition","display_name":"Tensor decomposition","score":0.5000247955322266},{"id":"https://openalex.org/keywords/tensor","display_name":"Tensor (intrinsic definition)","score":0.4563998281955719},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.44430550932884216},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4246375262737274},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3470384180545807},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.13452529907226562},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.11400070786476135}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7557132244110107},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7144097685813904},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.678754448890686},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6069176197052002},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5816184878349304},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5455876588821411},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.5179897546768188},{"id":"https://openalex.org/C2986737658","wikidata":"https://www.wikidata.org/wiki/Q30103009","display_name":"Tensor decomposition","level":3,"score":0.5000247955322266},{"id":"https://openalex.org/C155281189","wikidata":"https://www.wikidata.org/wiki/Q3518150","display_name":"Tensor (intrinsic definition)","level":2,"score":0.4563998281955719},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.44430550932884216},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4246375262737274},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3470384180545807},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.13452529907226562},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.11400070786476135},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/jstsp.2021.3063805","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jstsp.2021.3063805","pdf_url":null,"source":{"id":"https://openalex.org/S42167783","display_name":"IEEE Journal of Selected Topics in Signal Processing","issn_l":"1932-4553","issn":["1932-4553","1941-0484"],"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 Journal of Selected Topics in Signal Processing","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure","score":0.4099999964237213}],"awards":[{"id":"https://openalex.org/G3552334747","display_name":null,"funder_award_id":"2682020XG02","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"},{"id":"https://openalex.org/G4677181399","display_name":null,"funder_award_id":"2682020ZT35","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"},{"id":"https://openalex.org/G7569220417","display_name":"\u57fa\u4e8e\u7ed3\u6784\u975e\u8d1f\u77e9\u9635/\u5f20\u91cf\u5206\u89e3\u7406\u8bba\u7684\u591a\u65f6\u76f8(\u6781\u5316)SAR\u56fe\u50cf\u53d8\u5316\u68c0\u6d4b\u7814\u7a76","funder_award_id":"61871335","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"},{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":64,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W1485009520","https://openalex.org/W1521436688","https://openalex.org/W1798945469","https://openalex.org/W1963882359","https://openalex.org/W1993482030","https://openalex.org/W2064675550","https://openalex.org/W2076961568","https://openalex.org/W2087263574","https://openalex.org/W2122585011","https://openalex.org/W2133564696","https://openalex.org/W2152233525","https://openalex.org/W2153635508","https://openalex.org/W2314785379","https://openalex.org/W2465503420","https://openalex.org/W2500751094","https://openalex.org/W2548791488","https://openalex.org/W2550553598","https://openalex.org/W2559813832","https://openalex.org/W2602024454","https://openalex.org/W2609880332","https://openalex.org/W2619959423","https://openalex.org/W2768537477","https://openalex.org/W2768975974","https://openalex.org/W2771065697","https://openalex.org/W2772452219","https://openalex.org/W2774426077","https://openalex.org/W2790473231","https://openalex.org/W2799390666","https://openalex.org/W2809113079","https://openalex.org/W2888715336","https://openalex.org/W2899747753","https://openalex.org/W2901117552","https://openalex.org/W2908955282","https://openalex.org/W2914331134","https://openalex.org/W2914959431","https://openalex.org/W2922379874","https://openalex.org/W2936222941","https://openalex.org/W2942170965","https://openalex.org/W2948157022","https://openalex.org/W2950198859","https://openalex.org/W2950266692","https://openalex.org/W2961699889","https://openalex.org/W2963403868","https://openalex.org/W2963704562","https://openalex.org/W2963755276","https://openalex.org/W2964308564","https://openalex.org/W2969393175","https://openalex.org/W2991616716","https://openalex.org/W2999446243","https://openalex.org/W3004480865","https://openalex.org/W3036589244","https://openalex.org/W3101640299","https://openalex.org/W3105213343","https://openalex.org/W3106294914","https://openalex.org/W4297781745","https://openalex.org/W4385245566","https://openalex.org/W6628877408","https://openalex.org/W6638060716","https://openalex.org/W6679434410","https://openalex.org/W6730093588","https://openalex.org/W6739901393","https://openalex.org/W6741042017","https://openalex.org/W6763016736"],"related_works":["https://openalex.org/W4379256054","https://openalex.org/W2093953080","https://openalex.org/W47805180","https://openalex.org/W2963838862","https://openalex.org/W3015641590","https://openalex.org/W3216281372","https://openalex.org/W2987657992","https://openalex.org/W2949531434","https://openalex.org/W3155683369","https://openalex.org/W4286927328"],"abstract_inverted_index":{"Recurrent":[0],"neural":[1,39,89],"networks,":[2],"especially":[3],"the":[4,52,61,109,114,143,156,162],"convolutional":[5],"long":[6],"short-term":[7],"memory":[8,56],"(ConvLSTM),":[9],"have":[10],"attracted":[11],"plenty":[12],"of":[13,58,64,164],"attention":[14,122],"and":[15,55,166],"shown":[16],"promising":[17],"results":[18],"due":[19],"to":[20,50,100,129,137,175],"their":[21],"ability":[22],"in":[23,27,79,117],"modeling":[24],"long-term":[25],"dependencies":[26],"many":[28],"research":[29],"fields.":[30],"In":[31,154],"this":[32],"paper,":[33],"a":[34,68,80,85,119],"lightweight":[35,69],"tensor":[36,121],"attention-driven":[37],"ConvLSTM":[38,59],"network":[40,90],"(TACLNN)":[41],"is":[42,72,92,96,127],"proposed":[43,144,157],"for":[44,98,104,151],"hyperspectral":[45],"image":[46],"(HSI)":[47],"classification.":[48,106,153],"Firstly,":[49],"reduce":[51,161],"trainable":[53],"parameters":[54,165],"requirements":[57,168],"(specifically,":[60],"2-D":[62,88],"version":[63],"LSTM,":[65],"i.e.,":[66],"ConvLSTM2D),":[67],"ConvLSTM2D":[70],"cell":[71,116],"developed":[73],"by":[74,113],"utilizing":[75],"tensor-train":[76],"decomposition,":[77],"resulting":[78],"TT-ConvLSTM2D":[81,115],"cell,":[82],"with":[83],"which":[84],"spatial-spectral":[86],"TT-ConvLSTM":[87],"(SSTTCL2DNN)":[91],"built.":[93],"However,":[94],"it":[95],"inevitable":[97],"SSTTCL2DNN":[99],"obtain":[101],"lower":[102],"accuracies":[103,172],"HSI":[105,141,152],"To":[107],"recover":[108],"accuracy":[110],"loss":[111],"caused":[112],"SSTTCL2DNN,":[118],"learnable":[120],"residual":[123],"block":[124],"(TARB)":[125],"module":[126],"built":[128],"further":[130],"enhance":[131],"its":[132],"geometrical":[133],"structure.":[134],"When":[135],"applied":[136],"three":[138],"widely":[139],"used":[140],"benchmarks,":[142],"TACLNN":[145,158],"model":[146],"outperforms":[147],"several":[148],"state-of-the-art":[149],"methods":[150],"addition,":[155],"can":[159],"effectively":[160],"number":[163],"storage":[167],"achieving":[169],"higher":[170],"classification":[171],"as":[173],"compared":[174],"other":[176],"competitive":[177],"baselines.":[178]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":9},{"year":2024,"cited_by_count":13},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":8},{"year":2021,"cited_by_count":3}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
