{"id":"https://openalex.org/W2609880332","doi":"https://doi.org/10.1109/tgrs.2019.2899129","title":"Cascaded Recurrent Neural Networks for Hyperspectral Image Classification","display_name":"Cascaded Recurrent Neural Networks for Hyperspectral Image Classification","publication_year":2019,"publication_date":"2019-03-07","ids":{"openalex":"https://openalex.org/W2609880332","doi":"https://doi.org/10.1109/tgrs.2019.2899129","mag":"2609880332"},"language":"en","primary_location":{"id":"doi:10.1109/tgrs.2019.2899129","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2019.2899129","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":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1902.10858","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Renlong Hang","orcid":"https://orcid.org/0000-0001-6046-3689"},"institutions":[{"id":"https://openalex.org/I200845125","display_name":"Nanjing University of Information Science and Technology","ror":"https://ror.org/02y0rxk19","country_code":"CN","type":"education","lineage":["https://openalex.org/I200845125"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Renlong Hang","raw_affiliation_strings":["Jiangsu Key Laboratory of Big Data Analysis Technology, School of Automation, Nanjing University of Information Science and Technology, Nanjing, China"],"raw_orcid":"https://orcid.org/0000-0001-6046-3689","affiliations":[{"raw_affiliation_string":"Jiangsu Key Laboratory of Big Data Analysis Technology, School of Automation, Nanjing University of Information Science and Technology, Nanjing, China","institution_ids":["https://openalex.org/I200845125"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Qingshan Liu","orcid":"https://orcid.org/0000-0002-5512-6984"},"institutions":[{"id":"https://openalex.org/I200845125","display_name":"Nanjing University of Information Science and Technology","ror":"https://ror.org/02y0rxk19","country_code":"CN","type":"education","lineage":["https://openalex.org/I200845125"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qingshan Liu","raw_affiliation_strings":["Jiangsu Key Laboratory of Big Data Analysis Technology, School of Automation, Nanjing University of Information Science and Technology, Nanjing, China"],"raw_orcid":"https://orcid.org/0000-0002-5512-6984","affiliations":[{"raw_affiliation_string":"Jiangsu Key Laboratory of Big Data Analysis Technology, School of Automation, Nanjing University of Information Science and Technology, Nanjing, China","institution_ids":["https://openalex.org/I200845125"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Danfeng Hong","orcid":"https://orcid.org/0000-0002-3212-9584"},"institutions":[{"id":"https://openalex.org/I62916508","display_name":"Technical University of Munich","ror":"https://ror.org/02kkvpp62","country_code":"DE","type":"education","lineage":["https://openalex.org/I62916508"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Danfeng Hong","raw_affiliation_strings":["Signal Processing in Earth Observation, Technical University of Munich, Munich, Germany"],"raw_orcid":"https://orcid.org/0000-0002-3212-9584","affiliations":[{"raw_affiliation_string":"Signal Processing in Earth Observation, Technical University of Munich, Munich, Germany","institution_ids":["https://openalex.org/I62916508"]}]},{"author_position":"last","author":{"id":null,"display_name":"Pedram Ghamisi","orcid":"https://orcid.org/0000-0003-1203-741X"},"institutions":[{"id":"https://openalex.org/I2801798921","display_name":"Helmholtz-Zentrum Dresden-Rossendorf","ror":"https://ror.org/01zy2cs03","country_code":"DE","type":"facility","lineage":["https://openalex.org/I1305996414","https://openalex.org/I2801798921"]},{"id":"https://openalex.org/I4210148560","display_name":"Helmholtz Institute Freiberg for Resource Technology","ror":"https://ror.org/04kdb0j04","country_code":"DE","type":"government","lineage":["https://openalex.org/I1305996414","https://openalex.org/I2801798921","https://openalex.org/I4210148560"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Pedram Ghamisi","raw_affiliation_strings":["Helmholtz-Zentrum Dresden-Rossendorf, Helmholtz Institute Freiberg for Resource Technology, Freiberg, Germany"],"raw_orcid":"https://orcid.org/0000-0003-1203-741X","affiliations":[{"raw_affiliation_string":"Helmholtz-Zentrum Dresden-Rossendorf, Helmholtz Institute Freiberg for Resource Technology, Freiberg, Germany","institution_ids":["https://openalex.org/I4210148560","https://openalex.org/I2801798921"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":46.0029,"has_fulltext":false,"cited_by_count":526,"citation_normalized_percentile":{"value":0.99918372,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":"57","issue":"8","first_page":"5384","last_page":"5394"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9628000259399414,"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":0.9628000259399414,"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/T10057","display_name":"Face and Expression Recognition","score":0.005900000222027302,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.002099999925121665,"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/discriminative-model","display_name":"Discriminative model","score":0.8812999725341797},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.8144999742507935},{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.7354999780654907},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6371999979019165},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.47029998898506165},{"id":"https://openalex.org/keywords/layer","display_name":"Layer (electronics)","score":0.3781000077724457}],"concepts":[{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.8812999725341797},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.8144999742507935},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.7354999780654907},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.732699990272522},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6704999804496765},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6371999979019165},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.47029998898506165},{"id":"https://openalex.org/C2779227376","wikidata":"https://www.wikidata.org/wiki/Q6505497","display_name":"Layer (electronics)","level":2,"score":0.3781000077724457},{"id":"https://openalex.org/C2779696439","wikidata":"https://www.wikidata.org/wiki/Q7512811","display_name":"Signature (topology)","level":2,"score":0.3727000057697296},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.33809998631477356},{"id":"https://openalex.org/C176641082","wikidata":"https://www.wikidata.org/wiki/Q2446767","display_name":"Spectral signature","level":2,"score":0.27799999713897705},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2667999863624573},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2581999897956848},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.25589999556541443}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/tgrs.2019.2899129","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2019.2899129","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"},{"id":"pmh:oai:elib.dlr.de:128211","is_oa":false,"landing_page_url":"https://doi.org/10.1109/TGRS.2019.2899129>.","pdf_url":null,"source":{"id":"https://openalex.org/S4377196266","display_name":"elib (German Aerospace Center)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I2898391981","host_organization_name":"Deutsches Zentrum f\u00fcr Luft- und Raumfahrt e. V. (DLR)","host_organization_lineage":["https://openalex.org/I2898391981"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":null,"raw_type":"Zeitschriftenbeitrag"},{"id":"pmh:oai:arXiv.org:1902.10858","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1902.10858","pdf_url":"https://arxiv.org/pdf/1902.10858","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1902.10858","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1902.10858","pdf_url":"https://arxiv.org/pdf/1902.10858","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G7208724564","display_name":null,"funder_award_id":"61532009","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7375732063","display_name":null,"funder_award_id":"61825601","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7833602292","display_name":null,"funder_award_id":"BK20180786","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/F4320321605","display_name":"Government of Jiangsu Province","ror":"https://ror.org/004svx814"},{"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":49,"referenced_works":["https://openalex.org/W1521436688","https://openalex.org/W1990895816","https://openalex.org/W2029316659","https://openalex.org/W2044439250","https://openalex.org/W2052160904","https://openalex.org/W2063385051","https://openalex.org/W2063907334","https://openalex.org/W2064886835","https://openalex.org/W2076063813","https://openalex.org/W2077028485","https://openalex.org/W2090424610","https://openalex.org/W2097915756","https://openalex.org/W2100975942","https://openalex.org/W2102605133","https://openalex.org/W2143612262","https://openalex.org/W2152057649","https://openalex.org/W2157331557","https://openalex.org/W2194775991","https://openalex.org/W2249336288","https://openalex.org/W2261059368","https://openalex.org/W2345128667","https://openalex.org/W2412588858","https://openalex.org/W2500751094","https://openalex.org/W2533102868","https://openalex.org/W2560523472","https://openalex.org/W2572303978","https://openalex.org/W2588702902","https://openalex.org/W2598551616","https://openalex.org/W2600746131","https://openalex.org/W2602024454","https://openalex.org/W2603422184","https://openalex.org/W2614326984","https://openalex.org/W2754356769","https://openalex.org/W2764034829","https://openalex.org/W2765739551","https://openalex.org/W2767805377","https://openalex.org/W2779530678","https://openalex.org/W2782517596","https://openalex.org/W2789643644","https://openalex.org/W2810170362","https://openalex.org/W2888715336","https://openalex.org/W2894165434","https://openalex.org/W2898938461","https://openalex.org/W2899401964","https://openalex.org/W2919115771","https://openalex.org/W6620707391","https://openalex.org/W6679436768","https://openalex.org/W6683738474","https://openalex.org/W6684191040"],"related_works":["https://openalex.org/W1997670935","https://openalex.org/W2783789044","https://openalex.org/W3211035526","https://openalex.org/W4291701050","https://openalex.org/W2759110340","https://openalex.org/W2972973180","https://openalex.org/W1992102478","https://openalex.org/W2018257962","https://openalex.org/W2736348740","https://openalex.org/W2500751094"],"abstract_inverted_index":{"By":[0],"considering":[1,125],"the":[2,32,44,64,93,100,109,113,121,126,136,150,177],"spectral":[3,34,90,105],"signature":[4],"as":[5],"a":[6,54],"sequence,":[7],"recurrent":[8,60],"neural":[9],"networks":[10],"(RNNs)":[11],"have":[12],"been":[13],"successfully":[14],"used":[15,83,162],"to":[16,62,84,98,139],"learn":[17,99],"discriminative":[18,110],"features":[19],"from":[20,103],"hyperspectral":[21],"images":[22],"(HSIs)":[23],"recently.":[24],"However,":[25],"most":[26],"of":[27,47,69,74,112,152],"these":[28],"models":[29,171],"only":[30],"input":[31],"whole":[33],"bands":[35],"into":[36],"RNNs":[37],"directly,":[38],"which":[39],"may":[40],"not":[41],"fully":[42],"explore":[43,63],"specific":[45],"properties":[46],"HSIs.":[48,70,163],"In":[49],"this":[50],"paper,":[51],"we":[52,116,133,156],"propose":[53],"cascaded":[55],"RNN":[56,76,80,95],"model":[57,138],"using":[58],"gated":[59],"units":[61],"redundant":[65,86],"and":[66],"complementary":[67,101],"information":[68,87,102,129],"It":[71],"mainly":[72],"consists":[73],"two":[75,118,160],"layers.":[77,147],"The":[78,164],"first":[79],"layer":[81,96],"is":[82],"eliminate":[85],"between":[88],"adjacent":[89],"bands,":[91],"while":[92],"second":[94],"aims":[97],"nonadjacent":[104],"bands.":[106],"To":[107,148],"improve":[108],"ability":[111],"learned":[114],"features,":[115],"design":[117],"strategies":[119],"for":[120],"proposed":[122,137,154,170],"model.":[123],"Besides,":[124],"rich":[127],"spatial":[128],"contained":[130],"in":[131],"HSIs,":[132],"further":[134],"extend":[135],"its":[140],"spectral-spatial":[141],"counterpart":[142],"by":[143],"incorporating":[144],"some":[145],"convolutional":[146],"test":[149],"effectiveness":[151],"our":[153,169],"models,":[155],"conduct":[157],"experiments":[158],"on":[159],"widely":[161],"experimental":[165],"results":[166,175],"show":[167],"that":[168],"can":[172],"achieve":[173],"better":[174],"than":[176],"compared":[178],"models.":[179]},"counts_by_year":[{"year":2026,"cited_by_count":11},{"year":2025,"cited_by_count":57},{"year":2024,"cited_by_count":87},{"year":2023,"cited_by_count":111},{"year":2022,"cited_by_count":89},{"year":2021,"cited_by_count":86},{"year":2020,"cited_by_count":72},{"year":2019,"cited_by_count":13}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2017-05-05T00:00:00"}
