{"id":"https://openalex.org/W3009887759","doi":"https://doi.org/10.1109/icsai48974.2019.9010227","title":"Hyperspectral image classification based on pre-post combination process","display_name":"Hyperspectral image classification based on pre-post combination process","publication_year":2019,"publication_date":"2019-11-01","ids":{"openalex":"https://openalex.org/W3009887759","doi":"https://doi.org/10.1109/icsai48974.2019.9010227","mag":"3009887759"},"language":"en","primary_location":{"id":"doi:10.1109/icsai48974.2019.9010227","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icsai48974.2019.9010227","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 6th International Conference on Systems and Informatics (ICSAI)","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/A5065122816","display_name":"Kaiqing Luo","orcid":"https://orcid.org/0000-0002-6278-0917"},"institutions":[{"id":"https://openalex.org/I187400657","display_name":"South China Normal University","ror":"https://ror.org/01kq0pv72","country_code":"CN","type":"education","lineage":["https://openalex.org/I187400657"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kaiqing Luo","raw_affiliation_strings":["School of Physics and Telecommunication Engineering, South China Normal University Guangzhou, Guangdong, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Physics and Telecommunication Engineering, South China Normal University Guangzhou, Guangdong, China","institution_ids":["https://openalex.org/I187400657"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Yang Qin","orcid":null},"institutions":[{"id":"https://openalex.org/I187400657","display_name":"South China Normal University","ror":"https://ror.org/01kq0pv72","country_code":"CN","type":"education","lineage":["https://openalex.org/I187400657"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yang Qin","raw_affiliation_strings":["School of Physics and Telecommunication Engineering, South China Normal University Guangzhou, Guangdong, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Physics and Telecommunication Engineering, South China Normal University Guangzhou, Guangdong, China","institution_ids":["https://openalex.org/I187400657"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100689146","display_name":"Dan Yin","orcid":"https://orcid.org/0000-0003-3680-5997"},"institutions":[{"id":"https://openalex.org/I187400657","display_name":"South China Normal University","ror":"https://ror.org/01kq0pv72","country_code":"CN","type":"education","lineage":["https://openalex.org/I187400657"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dan Yin","raw_affiliation_strings":["School of Physics and Telecommunication Engineering, South China Normal University Guangzhou, Guangdong, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Physics and Telecommunication Engineering, South China Normal University Guangzhou, Guangdong, China","institution_ids":["https://openalex.org/I187400657"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101568340","display_name":"Hua Xiao","orcid":"https://orcid.org/0000-0003-3407-7009"},"institutions":[{"id":"https://openalex.org/I187400657","display_name":"South China Normal University","ror":"https://ror.org/01kq0pv72","country_code":"CN","type":"education","lineage":["https://openalex.org/I187400657"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hua Xiao","raw_affiliation_strings":["School of Physics and Telecommunication Engineering, South China Normal University Guangzhou, Guangdong, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Physics and Telecommunication Engineering, South China Normal University Guangzhou, Guangdong, China","institution_ids":["https://openalex.org/I187400657"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I187400657"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.30486111,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9997000098228455,"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":0.9997000098228455,"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":0.9940999746322632,"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/T10111","display_name":"Remote Sensing in Agriculture","score":0.9445000290870667,"subfield":{"id":"https://openalex.org/subfields/2303","display_name":"Ecology"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.79287189245224},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7188752889633179},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7104905843734741},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.6726769804954529},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6461172699928284},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.5492175221443176},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.5303552746772766},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.46347248554229736},{"id":"https://openalex.org/keywords/sparse-approximation","display_name":"Sparse approximation","score":0.426181435585022},{"id":"https://openalex.org/keywords/linear-discriminant-analysis","display_name":"Linear discriminant analysis","score":0.41139858961105347},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3484344482421875},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3348884582519531},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.21519175171852112}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.79287189245224},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7188752889633179},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7104905843734741},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.6726769804954529},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6461172699928284},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5492175221443176},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.5303552746772766},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.46347248554229736},{"id":"https://openalex.org/C124066611","wikidata":"https://www.wikidata.org/wiki/Q28684319","display_name":"Sparse approximation","level":2,"score":0.426181435585022},{"id":"https://openalex.org/C69738355","wikidata":"https://www.wikidata.org/wiki/Q1228929","display_name":"Linear discriminant analysis","level":2,"score":0.41139858961105347},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3484344482421875},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3348884582519531},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.21519175171852112},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icsai48974.2019.9010227","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icsai48974.2019.9010227","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 6th International Conference on Systems and Informatics (ICSAI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.5400000214576721}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W1522547150","https://openalex.org/W2097092275","https://openalex.org/W2131725398","https://openalex.org/W2131864940","https://openalex.org/W2136251662","https://openalex.org/W2141125852","https://openalex.org/W2555840851","https://openalex.org/W2598997103","https://openalex.org/W2775816490","https://openalex.org/W2777186991","https://openalex.org/W2791061840","https://openalex.org/W2944090748","https://openalex.org/W2947810057","https://openalex.org/W2960160083"],"related_works":["https://openalex.org/W2072166414","https://openalex.org/W3209970181","https://openalex.org/W2060875994","https://openalex.org/W2374021060","https://openalex.org/W4288315282","https://openalex.org/W2951122819","https://openalex.org/W2792632245","https://openalex.org/W2005234362","https://openalex.org/W2162970382","https://openalex.org/W1997235926"],"abstract_inverted_index":{"Aiming":[0],"at":[1],"the":[2,38,56,89,92,96,134,155],"problem":[3],"of":[4,8,91,152,157],"poor":[5],"classification":[6,24,30,113,142],"accuracy":[7,143],"traditional":[9,62],"machine":[10,63],"learning":[11,64],"algorithms":[12,65],"based":[13,26],"on":[14,27,119],"spectral":[15],"information":[16,86],"analysis,":[17],"this":[18],"paper":[19],"proposes":[20],"a":[21,103,111,149],"hyperspectral":[22,121],"image":[23],"method":[25,136],"pre-processing":[28],"before":[29],"and":[31,46,53,144],"processing":[32],"optimization":[33,107],"combination":[34],"after":[35],"classification.":[36],"Firstly,":[37],"original":[39],"samples":[40],"are":[41],"subjected":[42],"to":[43,87,109],"Gaussian":[44],"filter":[45],"Linear":[47],"discriminant":[48],"analysis":[49],"for":[50],"reducing":[51],"noise":[52],"dimensions.":[54],"Then,":[55],"data":[57],"is":[58,100],"initially":[59],"classified":[60],"by":[61,102],"such":[66,125],"as":[67,126],"k-nearest":[68],"neighbor(KNN),":[69],"Support":[70],"Vector":[71],"Machine":[72],"(SVM),":[73],"sparse":[74],"representation-based":[75],"classifier":[76],"(SRC)or":[77],"multiple":[78,120],"logistic":[79],"regression":[80],"(MLR).":[81],"Combining":[82],"local":[83],"pixel":[84],"spatial":[85],"determine":[88],"confidence":[90],"prediction":[93,98],"labels.":[94],"Finally,":[95],"initial":[97],"label":[99],"corrected":[101],"continuous":[104],"multi-layer":[105],"neighborhood":[106],"layers":[108],"obtain":[110],"final":[112],"label.":[114],"Comparative":[115],"experiments":[116],"were":[117],"performed":[118],"remote":[122],"sensing":[123],"databases":[124],"Indian":[127],"Pines.":[128],"The":[129],"experimental":[130],"results":[131],"show":[132],"that":[133],"proposed":[135],"has":[137,148],"obvious":[138],"performance":[139],"improvement":[140],"in":[141,154],"time":[145],"efficiency,":[146],"which":[147],"certain":[150],"degree":[151],"robustness":[153],"process":[156],"combining":[158],"with":[159],"different":[160],"classifiers.":[161]},"counts_by_year":[{"year":2023,"cited_by_count":2}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
