{"id":"https://openalex.org/W2998185207","doi":"https://doi.org/10.1109/icfsp48124.2019.8938098","title":"Optimal Parameter Selection in Hyperspectral Classification Based on Convolutional Neural Network","display_name":"Optimal Parameter Selection in Hyperspectral Classification Based on Convolutional Neural Network","publication_year":2019,"publication_date":"2019-09-01","ids":{"openalex":"https://openalex.org/W2998185207","doi":"https://doi.org/10.1109/icfsp48124.2019.8938098","mag":"2998185207"},"language":"en","primary_location":{"id":"doi:10.1109/icfsp48124.2019.8938098","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icfsp48124.2019.8938098","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 5th International Conference on Frontiers of Signal Processing (ICFSP)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5061649163","display_name":"Qiaoqiao Sun","orcid":null},"institutions":[{"id":"https://openalex.org/I1294671590","display_name":"Centre National de la Recherche Scientifique","ror":"https://ror.org/02feahw73","country_code":"FR","type":"government","lineage":["https://openalex.org/I1294671590"]},{"id":"https://openalex.org/I21491767","display_name":"Aix-Marseille Universit\u00e9","ror":"https://ror.org/035xkbk20","country_code":"FR","type":"education","lineage":["https://openalex.org/I21491767"]},{"id":"https://openalex.org/I4210142724","display_name":"Centrale Marseille","ror":"https://ror.org/040baw385","country_code":"FR","type":"education","lineage":["https://openalex.org/I4210142724"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Qiaoqiao Sun","raw_affiliation_strings":["Institute Fresnel, Aix Marseille Universit\u00e9, Centrale Marseille, CNRS, Marseille, France"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute Fresnel, Aix Marseille Universit\u00e9, Centrale Marseille, CNRS, Marseille, France","institution_ids":["https://openalex.org/I1294671590","https://openalex.org/I21491767","https://openalex.org/I4210142724"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100400563","display_name":"Xuefeng Liu","orcid":"https://orcid.org/0000-0003-2705-8731"},"institutions":[{"id":"https://openalex.org/I143413998","display_name":"Qingdao University of Science and Technology","ror":"https://ror.org/041j8js14","country_code":"CN","type":"education","lineage":["https://openalex.org/I143413998"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xuefeng Liu","raw_affiliation_strings":["College of Automation & Electronic Engineering, Qingdao University of Science and Technology, Qingdao, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Automation & Electronic Engineering, Qingdao University of Science and Technology, Qingdao, China","institution_ids":["https://openalex.org/I143413998"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5052863335","display_name":"Salah Bourennane","orcid":"https://orcid.org/0000-0002-4683-6685"},"institutions":[{"id":"https://openalex.org/I1294671590","display_name":"Centre National de la Recherche Scientifique","ror":"https://ror.org/02feahw73","country_code":"FR","type":"government","lineage":["https://openalex.org/I1294671590"]},{"id":"https://openalex.org/I21491767","display_name":"Aix-Marseille Universit\u00e9","ror":"https://ror.org/035xkbk20","country_code":"FR","type":"education","lineage":["https://openalex.org/I21491767"]},{"id":"https://openalex.org/I4210142724","display_name":"Centrale Marseille","ror":"https://ror.org/040baw385","country_code":"FR","type":"education","lineage":["https://openalex.org/I4210142724"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Salah Bourennane","raw_affiliation_strings":["Institute Fresnel, Aix Marseille Universit\u00e9, Centrale Marseille, CNRS, Marseille, France"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute Fresnel, Aix Marseille Universit\u00e9, Centrale Marseille, CNRS, Marseille, France","institution_ids":["https://openalex.org/I1294671590","https://openalex.org/I21491767","https://openalex.org/I4210142724"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.26805556,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"3","issue":null,"first_page":"100","last_page":"104"},"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/T13890","display_name":"Remote Sensing and Land Use","score":0.998199999332428,"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/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9800999760627747,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.8374024629592896},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7964171171188354},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6997002363204956},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6993785500526428},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6080864667892456},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.6040408611297607},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.5333877205848694},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.47400960326194763},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.4722537398338318},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4216732382774353},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.34390637278556824}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.8374024629592896},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7964171171188354},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6997002363204956},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6993785500526428},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6080864667892456},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.6040408611297607},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.5333877205848694},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.47400960326194763},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.4722537398338318},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4216732382774353},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.34390637278556824},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icfsp48124.2019.8938098","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icfsp48124.2019.8938098","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 5th International Conference on Frontiers of Signal Processing (ICFSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4","score":0.47999998927116394}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W1904365287","https://openalex.org/W1985242206","https://openalex.org/W2003059629","https://openalex.org/W2067184918","https://openalex.org/W2090424610","https://openalex.org/W2107030918","https://openalex.org/W2500751094","https://openalex.org/W2516282711","https://openalex.org/W2523246573","https://openalex.org/W2547852346","https://openalex.org/W2548476632","https://openalex.org/W2548791488","https://openalex.org/W2604086375","https://openalex.org/W2605486153","https://openalex.org/W2738327665","https://openalex.org/W2764276316","https://openalex.org/W2765957155","https://openalex.org/W2767054946","https://openalex.org/W2806155925","https://openalex.org/W2887785636","https://openalex.org/W2891078019","https://openalex.org/W3098435832","https://openalex.org/W3101640299","https://openalex.org/W4240485910","https://openalex.org/W6640036494","https://openalex.org/W6727249380"],"related_works":["https://openalex.org/W2072166414","https://openalex.org/W3209970181","https://openalex.org/W2952813363","https://openalex.org/W4360783045","https://openalex.org/W2963346891","https://openalex.org/W3176438653","https://openalex.org/W2770149305","https://openalex.org/W3167930666","https://openalex.org/W3014952856","https://openalex.org/W3010730661"],"abstract_inverted_index":{"Classification":[0],"is":[1,61,79,87],"a":[2,106,111],"key":[3],"technique":[4],"in":[5,24,122],"hyperspectral":[6],"image":[7,28],"(HSI)":[8],"applications.":[9],"Deep":[10],"learning":[11],"algorithms,":[12],"which":[13],"exhibit":[14],"strong":[15],"modeling":[16],"and":[17,29,43,54,72],"representational":[18],"capabilities,":[19],"have":[20,37,47],"been":[21,38,48],"successfully":[22],"adopted":[23],"fields":[25],"such":[26],"as":[27],"language":[30],"processing.":[31],"And":[32],"convolutional":[33],"neural":[34],"networks":[35],"(CNNs)":[36],"used":[39],"for":[40,100,141],"HSI":[41,101,142],"classification":[42,107,143],"some":[44,64],"interesting":[45],"results":[46,131],"obtained.":[49],"Owing":[50],"to":[51,63,125,145],"local":[52],"connection":[53],"weight":[55],"sharing,":[56],"the":[57,73,75,77,80,91,96,126,134],"number":[58,81],"of":[59,82],"parameters":[60,71,99,119],"reduced":[62],"extent,":[65],"but":[66],"there":[67],"are":[68,120],"still":[69],"many":[70],"deeper":[74],"network,":[76],"larger":[78],"parameters.":[83],"The":[84,117],"network":[85,118],"performance":[86],"strongly":[88],"influenced":[89],"by":[90],"parameter":[92,114],"settings.":[93],"To":[94],"obtain":[95],"optimal":[97],"CNN":[98,112],"classification,":[102],"this":[103],"paper":[104],"proposes":[105],"method":[108,137],"based":[109],"on":[110],"with":[113],"tuning":[115],"(CNN-PT).":[116],"tuned":[121],"turn":[123],"according":[124],"unique":[127],"variable":[128],"principle.":[129],"Simulation":[130],"show":[132],"that":[133],"proposed":[135],"CNN-PT":[136],"has":[138],"considerable":[139],"potential":[140],"compared":[144],"previous":[146],"methods.":[147]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}