{"id":"https://openalex.org/W2937638900","doi":"https://doi.org/10.1109/tgrs.2019.2951433","title":"BS-Nets: An End-to-End Framework for Band Selection of Hyperspectral Image","display_name":"BS-Nets: An End-to-End Framework for Band Selection of Hyperspectral Image","publication_year":2019,"publication_date":"2019-11-20","ids":{"openalex":"https://openalex.org/W2937638900","doi":"https://doi.org/10.1109/tgrs.2019.2951433","mag":"2937638900"},"language":"en","primary_location":{"id":"doi:10.1109/tgrs.2019.2951433","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2019.2951433","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/1904.08269","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5044359278","display_name":"Yaoming Cai","orcid":"https://orcid.org/0000-0002-2609-3036"},"institutions":[{"id":"https://openalex.org/I3124059619","display_name":"China University of Geosciences","ror":"https://ror.org/04gcegc37","country_code":"CN","type":"education","lineage":["https://openalex.org/I3124059619"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yaoming Cai","raw_affiliation_strings":["School of Computer Science, China University of Geosciences, Wuhan, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, China University of Geosciences, Wuhan, China","institution_ids":["https://openalex.org/I3124059619"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062400971","display_name":"Xiaobo Liu","orcid":"https://orcid.org/0000-0001-8298-7715"},"institutions":[{"id":"https://openalex.org/I3124059619","display_name":"China University of Geosciences","ror":"https://ror.org/04gcegc37","country_code":"CN","type":"education","lineage":["https://openalex.org/I3124059619"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaobo Liu","raw_affiliation_strings":["Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, China University of Geosciences, Wuhan, China","School of Automation, China University of Geosciences, Wuhan, China"],"affiliations":[{"raw_affiliation_string":"Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, China University of Geosciences, Wuhan, China","institution_ids":["https://openalex.org/I3124059619"]},{"raw_affiliation_string":"School of Automation, China University of Geosciences, Wuhan, China","institution_ids":["https://openalex.org/I3124059619"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5038861185","display_name":"Zhihua Cai","orcid":"https://orcid.org/0000-0003-0020-6503"},"institutions":[{"id":"https://openalex.org/I4210105374","display_name":"Beibu Gulf University","ror":"https://ror.org/01dq3qq95","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210105374"]},{"id":"https://openalex.org/I3124059619","display_name":"China University of Geosciences","ror":"https://ror.org/04gcegc37","country_code":"CN","type":"education","lineage":["https://openalex.org/I3124059619"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhihua Cai","raw_affiliation_strings":["Beibu Gulf Big Data Resources Utilization Laboratory, Qinzhou University, Qinzhou, China","School of Computer Science, China University of Geosciences, Wuhan, China"],"affiliations":[{"raw_affiliation_string":"Beibu Gulf Big Data Resources Utilization Laboratory, Qinzhou University, Qinzhou, China","institution_ids":["https://openalex.org/I4210105374"]},{"raw_affiliation_string":"School of Computer Science, China University of Geosciences, Wuhan, China","institution_ids":["https://openalex.org/I3124059619"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5044359278"],"corresponding_institution_ids":["https://openalex.org/I3124059619"],"apc_list":null,"apc_paid":null,"fwci":19.9031,"has_fulltext":false,"cited_by_count":257,"citation_normalized_percentile":{"value":0.99459244,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":100},"biblio":{"volume":"58","issue":"3","first_page":"1969","last_page":"1984"},"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":0.9934999942779541,"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/T12389","display_name":"Infrared Target Detection Methodologies","score":0.9876000285148621,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.8215148448944092},{"id":"https://openalex.org/keywords/redundancy","display_name":"Redundancy (engineering)","score":0.800572395324707},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7531018853187561},{"id":"https://openalex.org/keywords/spectral-bands","display_name":"Spectral bands","score":0.6824496984481812},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5698435306549072},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5562390089035034},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.5524908900260925},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4763393998146057},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.42275270819664},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.33921897411346436},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.27198225259780884}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.8215148448944092},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.800572395324707},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7531018853187561},{"id":"https://openalex.org/C114700698","wikidata":"https://www.wikidata.org/wiki/Q2882278","display_name":"Spectral bands","level":2,"score":0.6824496984481812},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5698435306549072},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5562390089035034},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.5524908900260925},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4763393998146057},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.42275270819664},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.33921897411346436},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.27198225259780884},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tgrs.2019.2951433","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2019.2951433","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:arXiv.org:1904.08269","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1904.08269","pdf_url":"https://arxiv.org/pdf/1904.08269","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:1904.08269","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1904.08269","pdf_url":"https://arxiv.org/pdf/1904.08269","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":[{"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure","score":0.4000000059604645}],"awards":[{"id":"https://openalex.org/G301648404","display_name":null,"funder_award_id":"61773355","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G4220299630","display_name":null,"funder_award_id":"61603355","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5580311328","display_name":null,"funder_award_id":"2018CFB528","funder_id":"https://openalex.org/F4320322186","funder_display_name":"Natural Science Foundation of Hubei Province"},{"id":"https://openalex.org/G6573391973","display_name":null,"funder_award_id":"G1323541717","funder_id":"https://openalex.org/F4320322815","funder_display_name":"China University of Geosciences, Wuhan"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320322186","display_name":"Natural Science Foundation of Hubei Province","ror":null},{"id":"https://openalex.org/F4320322815","display_name":"China University of Geosciences, Wuhan","ror":"https://ror.org/04q6c7p66"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":72,"referenced_works":["https://openalex.org/W603908379","https://openalex.org/W1514535095","https://openalex.org/W1902237438","https://openalex.org/W1902936532","https://openalex.org/W1972702299","https://openalex.org/W1977791453","https://openalex.org/W1980007140","https://openalex.org/W1982251953","https://openalex.org/W1988386267","https://openalex.org/W2012255037","https://openalex.org/W2028436154","https://openalex.org/W2039409148","https://openalex.org/W2071185414","https://openalex.org/W2071821878","https://openalex.org/W2078491260","https://openalex.org/W2099471712","https://openalex.org/W2136251662","https://openalex.org/W2138153039","https://openalex.org/W2150990614","https://openalex.org/W2165755981","https://openalex.org/W2282289695","https://openalex.org/W2288723698","https://openalex.org/W2314785379","https://openalex.org/W2412588858","https://openalex.org/W2500751094","https://openalex.org/W2550553598","https://openalex.org/W2592311268","https://openalex.org/W2602024454","https://openalex.org/W2732412926","https://openalex.org/W2743111138","https://openalex.org/W2747189523","https://openalex.org/W2749577643","https://openalex.org/W2752782242","https://openalex.org/W2764276316","https://openalex.org/W2765811365","https://openalex.org/W2776265614","https://openalex.org/W2787953245","https://openalex.org/W2789249105","https://openalex.org/W2790801839","https://openalex.org/W2793357412","https://openalex.org/W2799780652","https://openalex.org/W2799954862","https://openalex.org/W2803413127","https://openalex.org/W2804951847","https://openalex.org/W2807022399","https://openalex.org/W2808233078","https://openalex.org/W2808366416","https://openalex.org/W2808682285","https://openalex.org/W2808931143","https://openalex.org/W2884585870","https://openalex.org/W2896340099","https://openalex.org/W2897121118","https://openalex.org/W2906616692","https://openalex.org/W2919115771","https://openalex.org/W2952921651","https://openalex.org/W2955058313","https://openalex.org/W2962949934","https://openalex.org/W2963091558","https://openalex.org/W2963403868","https://openalex.org/W2963420686","https://openalex.org/W2963495494","https://openalex.org/W2964184826","https://openalex.org/W3101640299","https://openalex.org/W3101781204","https://openalex.org/W3105100264","https://openalex.org/W4240485910","https://openalex.org/W4320013936","https://openalex.org/W4385245566","https://openalex.org/W6618372016","https://openalex.org/W6630875275","https://openalex.org/W6739901393","https://openalex.org/W6753412334"],"related_works":["https://openalex.org/W1982418987","https://openalex.org/W1978077614","https://openalex.org/W4327563507","https://openalex.org/W2603494857","https://openalex.org/W2024377932","https://openalex.org/W2799746630","https://openalex.org/W4310079726","https://openalex.org/W4390582117","https://openalex.org/W2889956472","https://openalex.org/W3137839769"],"abstract_inverted_index":{"Hyperspectral":[0],"image":[1],"(HSI)":[2],"consists":[3,100],"of":[4,6,101,165,214],"hundreds":[5],"continuous":[7],"narrowbands":[8],"with":[9,157,183,205,217],"high":[10,23],"spectral":[11,68,116],"correlation,":[12],"which":[13,107,123],"would":[14],"lead":[15],"to":[16,34,109,126,150,155],"the":[17,22,52,62,112,128,132,196,210],"so-called":[18],"Hughes":[19],"phenomenon":[20],"and":[21,58,64,118,154,173,178,208],"computational":[24],"cost":[25],"in":[26,37,137,212],"processing.":[27],"Band":[28],"selection":[29],"(BS)":[30],"has":[31],"been":[32],"proven":[33],"be":[35,82],"effective":[36],"avoiding":[38],"such":[39],"problems":[40],"by":[41,73],"removing":[42],"redundant":[43],"bands.":[44,69],"However,":[45],"many":[46,158],"existing":[47,159,185],"BS":[48,93,95,186],"methods":[49],"separately":[50],"estimate":[51],"significance":[53],"for":[54],"every":[55],"single":[56],"band":[57,79,103,203],"cannot":[59],"fully":[60,169],"consider":[61],"nonlinear":[63,113],"global":[65],"interaction":[66],"between":[67,115],"In":[70],"this":[71],"article,":[72],"assuming":[74],"that":[75,195],"a":[76,91,102,119,138],"complete":[77],"HSI":[78,130],"set":[80],"can":[81,199],"reconstructed":[83],"from":[84,131,152],"its":[85],"few":[86],"informative":[87,134,202],"bands,":[88,117,135],"we":[89],"propose":[90],"unified":[92],"framework,":[94],"Network":[96],"(BS-Net).":[97],"The":[98,141],"framework":[99,143],"attention":[104],"module":[105],"(BAM),":[106],"aims":[108],"explicitly":[110],"model":[111],"interdependences":[114],"reconstruction":[120],"network":[121],"(RecNet),":[122],"is":[124,144],"used":[125],"restore":[127],"original":[129],"learned":[133],"resulting":[136,142],"flexible":[139],"architecture.":[140],"end-to-end":[145],"trainable,":[146],"making":[147],"it":[148],"easier":[149],"train":[151],"scratch":[153],"combine":[156],"networks.":[160],"We":[161],"implement":[162],"two":[163],"versions":[164],"BS-Nets,":[166],"respectively,":[167],"using":[168],"connected":[170],"networks":[171,176],"(BS-Net-FC)":[172],"convolutional":[174],"neural":[175],"(BS-Net-Conv),":[177],"extensively":[179],"compare":[180],"their":[181],"results":[182],"popular":[184],"approaches":[187],"on":[188],"three":[189],"real":[190],"hyperspectral":[191],"data":[192],"sets,":[193],"showing":[194],"proposed":[197],"BS-Nets":[198],"accurately":[200],"select":[201],"subset":[204],"less":[206],"redundancy":[207],"outperform":[209],"competitors":[211],"terms":[213],"classification":[215],"accuracy":[216],"competitive":[218],"time":[219],"cost.":[220]},"counts_by_year":[{"year":2026,"cited_by_count":11},{"year":2025,"cited_by_count":34},{"year":2024,"cited_by_count":61},{"year":2023,"cited_by_count":41},{"year":2022,"cited_by_count":37},{"year":2021,"cited_by_count":52},{"year":2020,"cited_by_count":19},{"year":2019,"cited_by_count":2}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}
