{"id":"https://openalex.org/W2025884142","doi":"https://doi.org/10.1109/igarss.2014.6947224","title":"Hyperspectral image classification using sparse representation-based classifier","display_name":"Hyperspectral image classification using sparse representation-based classifier","publication_year":2014,"publication_date":"2014-07-01","ids":{"openalex":"https://openalex.org/W2025884142","doi":"https://doi.org/10.1109/igarss.2014.6947224","mag":"2025884142"},"language":"en","primary_location":{"id":"doi:10.1109/igarss.2014.6947224","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2014.6947224","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 IEEE Geoscience and Remote Sensing Symposium","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/A5025235318","display_name":"Yufang Tang","orcid":"https://orcid.org/0000-0002-8038-6550"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]},{"id":"https://openalex.org/I28006308","display_name":"Shandong Normal University","ror":"https://ror.org/01wy3h363","country_code":"CN","type":"education","lineage":["https://openalex.org/I28006308"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yufang Tang","raw_affiliation_strings":["Beijing University of Posts and Telecommunications, Beijing, P. R. China","Shandong Normal University, Jinan, P. R. China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, Beijing, P. R. China","institution_ids":["https://openalex.org/I139759216"]},{"raw_affiliation_string":"Shandong Normal University, Jinan, P. R. China","institution_ids":["https://openalex.org/I28006308"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029828175","display_name":"Xueming Li","orcid":"https://orcid.org/0000-0003-1058-2799"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xueming Li","raw_affiliation_strings":["Beijing University of Posts and Telecommunications, Beijing, P. R. China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, Beijing, P. R. China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100569239","display_name":"Yan Xu","orcid":null},"institutions":[{"id":"https://openalex.org/I28006308","display_name":"Shandong Normal University","ror":"https://ror.org/01wy3h363","country_code":"CN","type":"education","lineage":["https://openalex.org/I28006308"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yan Xu","raw_affiliation_strings":["Shandong Normal University, Jinan, P. R. China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shandong Normal University, Jinan, P. R. China","institution_ids":["https://openalex.org/I28006308"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103009087","display_name":"Yang Liu","orcid":"https://orcid.org/0000-0001-9224-3277"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yang Liu","raw_affiliation_strings":["Beijing University of Posts and Telecommunications, Beijing, P. R. China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, Beijing, P. R. China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083690982","display_name":"Jizhe Wang","orcid":"https://orcid.org/0000-0003-1813-2290"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jizhe Wang","raw_affiliation_strings":["Beijing University of Posts and Telecommunications, Beijing, P. R. China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, Beijing, P. R. China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100731340","display_name":"Chenyu Liu","orcid":"https://orcid.org/0000-0003-4290-1205"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chenyu Liu","raw_affiliation_strings":["Beijing University of Posts and Telecommunications, Beijing, P. R. China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, Beijing, P. R. China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100600804","display_name":"Shuchang Liu","orcid":"https://orcid.org/0000-0001-5977-9221"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shuchang Liu","raw_affiliation_strings":["Beijing University of Posts and Telecommunications, Beijing, P. R. China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, Beijing, P. R. China","institution_ids":["https://openalex.org/I139759216"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":15,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"49","issue":null,"first_page":"3450","last_page":"3453"},"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":0.9869999885559082,"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":0.9496999979019165,"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/discriminative-model","display_name":"Discriminative model","score":0.8426651358604431},{"id":"https://openalex.org/keywords/sparse-approximation","display_name":"Sparse approximation","score":0.7926825284957886},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7268519997596741},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7219510078430176},{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.6991320252418518},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.663642942905426},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6283136606216431},{"id":"https://openalex.org/keywords/k-svd","display_name":"K-SVD","score":0.6274729371070862},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.5783689022064209},{"id":"https://openalex.org/keywords/neural-coding","display_name":"Neural coding","score":0.5108622908592224},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.5008389949798584},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.46299228072166443},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.4235365092754364},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.2819538116455078}],"concepts":[{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.8426651358604431},{"id":"https://openalex.org/C124066611","wikidata":"https://www.wikidata.org/wiki/Q28684319","display_name":"Sparse approximation","level":2,"score":0.7926825284957886},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7268519997596741},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7219510078430176},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.6991320252418518},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.663642942905426},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6283136606216431},{"id":"https://openalex.org/C154771677","wikidata":"https://www.wikidata.org/wiki/Q17098361","display_name":"K-SVD","level":3,"score":0.6274729371070862},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.5783689022064209},{"id":"https://openalex.org/C77637269","wikidata":"https://www.wikidata.org/wiki/Q7002051","display_name":"Neural coding","level":2,"score":0.5108622908592224},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.5008389949798584},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.46299228072166443},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.4235365092754364},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2819538116455078}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss.2014.6947224","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2014.6947224","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 IEEE Geoscience and Remote Sensing Symposium","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.75}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":6,"referenced_works":["https://openalex.org/W2081452833","https://openalex.org/W2084716923","https://openalex.org/W2097915756","https://openalex.org/W2105464873","https://openalex.org/W2164437025","https://openalex.org/W4386748160"],"related_works":["https://openalex.org/W2016265625","https://openalex.org/W2111634407","https://openalex.org/W2552089492","https://openalex.org/W2982240858","https://openalex.org/W4205656132","https://openalex.org/W2008821896","https://openalex.org/W3004790527","https://openalex.org/W2067062989","https://openalex.org/W2080187108","https://openalex.org/W634011990"],"abstract_inverted_index":{"Based":[0],"on":[1,42,88],"the":[2,37,43,55,62,68,92,109,115,132],"idea":[3],"of":[4,39,45,57,64,124,138],"SRC(Sparse":[5],"Representation":[6],"based":[7],"Classification),":[8],"a":[9,73],"novel":[10],"approach":[11],"HSIC-SRC":[12,128],"is":[13,129],"proposed":[14,127],"in":[15,32],"this":[16],"paper.":[17],"Unlike":[18],"most":[19],"existing":[20],"algorithms":[21],"for":[22,79,140],"HSIC(HSI":[23],"Classification)":[24],"via":[25],"sparse":[26,80,116,122],"representation,":[27,81],"our":[28],"main":[29],"contributions":[30],"lie":[31],"two":[33],"aspects,":[34],"1)":[35],"Considering":[36,91],"performance":[38],"SRC":[40],"depending":[41],"quality":[44],"dictionary,":[46],"we":[47,83,107],"employ":[48,108],"LC-KSVD(Label":[49],"Consistent":[50],"KSVD)":[51],"algorithm":[52],"which":[53],"joints":[54],"regularization":[56,63],"discriminative":[58],"sparse-code":[59],"error":[60,66],"and":[61,76,142,159],"classification":[65,152],"into":[67,114],"sparsity":[69],"model,":[70,117],"to":[71,118,131],"learn":[72],"more":[74,120],"`compact'":[75],"`discriminative'":[77],"dictionary":[78],"hence":[82],"can":[84],"achieve":[85],"better":[86],"accuracy":[87],"classification;":[89],"2)":[90],"neighboring":[93],"pixels":[94],"usually":[95],"composed":[96],"by":[97],"similar":[98],"materials,":[99],"their":[100],"spectral":[101],"characteristics":[102],"are":[103],"highly":[104],"correlated.":[105],"Therefore,":[106],"contextual":[110],"information(or":[111],"spatial":[112],"information)":[113],"obtain":[119],"optimized":[121],"representation":[123],"pixels.":[125],"The":[126],"applied":[130],"well":[133],"known":[134],"hyperspectral":[135],"image":[136],"`University":[137],"Pavia'":[139],"classification,":[141],"experimental":[143],"results":[144],"show":[145],"that":[146],"it":[147],"outperforms":[148],"some":[149],"other":[150],"state-of-the-art":[151],"algorithms,":[153],"such":[154],"as":[155],"SVM,":[156],"SP,":[157],"OMP":[158],"SPG-l":[160],"<sub":[161],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[162],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">1</sub>":[163],",":[164],"with":[165],"only":[166],"one":[167],"simple":[168],"linear":[169],"classifier.":[170]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":4},{"year":2015,"cited_by_count":3}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}