{"id":"https://openalex.org/W3132067409","doi":"https://doi.org/10.1109/igarss39084.2020.9323751","title":"Fusion of Linear and Nonlinear Classifiers for Kernel Dictionary Learning: Application to Sar Target Recognition","display_name":"Fusion of Linear and Nonlinear Classifiers for Kernel Dictionary Learning: Application to Sar Target Recognition","publication_year":2020,"publication_date":"2020-09-26","ids":{"openalex":"https://openalex.org/W3132067409","doi":"https://doi.org/10.1109/igarss39084.2020.9323751","mag":"3132067409"},"language":"en","primary_location":{"id":"doi:10.1109/igarss39084.2020.9323751","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss39084.2020.9323751","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2020 - 2020 IEEE International 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/A5102899186","display_name":"Lei Tao","orcid":"https://orcid.org/0000-0002-0152-8224"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lei Tao","raw_affiliation_strings":["School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074129836","display_name":"Xue Jiang","orcid":"https://orcid.org/0000-0001-7099-6817"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xue Jiang","raw_affiliation_strings":["School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081045402","display_name":"Li Zhou","orcid":"https://orcid.org/0000-0003-2142-2811"},"institutions":[{"id":"https://openalex.org/I4210166112","display_name":"State Key Laboratory of Remote Sensing Science","ror":"https://ror.org/05wzjqa24","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210166112"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhou Li","raw_affiliation_strings":["Beijing Institute of Remote Sensing Information, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Institute of Remote Sensing Information, Beijing, China","institution_ids":["https://openalex.org/I4210166112"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5061319150","display_name":"Xingzhao Liu","orcid":"https://orcid.org/0000-0002-4533-3904"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xingzhao Liu","raw_affiliation_strings":["School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China","institution_ids":["https://openalex.org/I183067930"]}]}],"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":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"313","issue":null,"first_page":"742","last_page":"745"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"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/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"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/T11038","display_name":"Advanced SAR Imaging Techniques","score":0.9994999766349792,"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"}},{"id":"https://openalex.org/T11609","display_name":"Geophysical Methods and Applications","score":0.9977999925613403,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean 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/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7765297889709473},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7415144443511963},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6428878307342529},{"id":"https://openalex.org/keywords/kernel-principal-component-analysis","display_name":"Kernel principal component analysis","score":0.5385345816612244},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.5340947508811951},{"id":"https://openalex.org/keywords/principal-component-analysis","display_name":"Principal component analysis","score":0.5312787294387817},{"id":"https://openalex.org/keywords/nonlinear-system","display_name":"Nonlinear system","score":0.5265536308288574},{"id":"https://openalex.org/keywords/synthetic-aperture-radar","display_name":"Synthetic aperture radar","score":0.4998197555541992},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.47557225823402405},{"id":"https://openalex.org/keywords/kernel-method","display_name":"Kernel method","score":0.4526481628417969},{"id":"https://openalex.org/keywords/linear-classifier","display_name":"Linear classifier","score":0.4479965269565582},{"id":"https://openalex.org/keywords/sparse-approximation","display_name":"Sparse approximation","score":0.4419018626213074},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.41533994674682617},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.3442010283470154},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2537318766117096}],"concepts":[{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7765297889709473},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7415144443511963},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6428878307342529},{"id":"https://openalex.org/C182335926","wikidata":"https://www.wikidata.org/wiki/Q17093020","display_name":"Kernel principal component analysis","level":4,"score":0.5385345816612244},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.5340947508811951},{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.5312787294387817},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.5265536308288574},{"id":"https://openalex.org/C87360688","wikidata":"https://www.wikidata.org/wiki/Q740686","display_name":"Synthetic aperture radar","level":2,"score":0.4998197555541992},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.47557225823402405},{"id":"https://openalex.org/C122280245","wikidata":"https://www.wikidata.org/wiki/Q620622","display_name":"Kernel method","level":3,"score":0.4526481628417969},{"id":"https://openalex.org/C139532973","wikidata":"https://www.wikidata.org/wiki/Q2679259","display_name":"Linear classifier","level":3,"score":0.4479965269565582},{"id":"https://openalex.org/C124066611","wikidata":"https://www.wikidata.org/wiki/Q28684319","display_name":"Sparse approximation","level":2,"score":0.4419018626213074},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.41533994674682617},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.3442010283470154},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2537318766117096},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss39084.2020.9323751","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss39084.2020.9323751","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2463320017","display_name":null,"funder_award_id":"61971279","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":9,"referenced_works":["https://openalex.org/W1963932623","https://openalex.org/W2004465977","https://openalex.org/W2027805700","https://openalex.org/W2100495367","https://openalex.org/W2129812935","https://openalex.org/W2160547390","https://openalex.org/W2543892465","https://openalex.org/W2890848540","https://openalex.org/W3099438410"],"related_works":["https://openalex.org/W1984421104","https://openalex.org/W2512565647","https://openalex.org/W2001772920","https://openalex.org/W2393746448","https://openalex.org/W2905418897","https://openalex.org/W2534878021","https://openalex.org/W2398887903","https://openalex.org/W1985034083","https://openalex.org/W3093470103","https://openalex.org/W1998640076"],"abstract_inverted_index":{"In":[0,83],"this":[1],"paper,":[2],"a":[3,33,43,54,61],"fusion":[4],"of":[5,111],"linear":[6,26,62],"and":[7,17,28,75,98,141],"nonlinear":[8,34,48,56],"classification":[9,57,63,78],"errors":[10,79],"is":[11,18,64,117],"introduced":[12,65],"into":[13,42,66],"kernel":[14],"dictionary":[15,27,139],"learning":[16,140],"applied":[19],"for":[20,47],"SAR":[21,40,96],"target":[22],"recognition.":[23],"Different":[24],"from":[25],"classifier":[29],"learning,":[30],"we":[31,85],"utilize":[32],"mapping":[35],"function":[36,69,90],"to":[37,91,106,119],"map":[38],"the":[39,67,72,76,88,93,100,108,112,121,128,133],"data":[41],"higher":[44],"dimensional":[45],"space":[46],"reconstruction.":[49],"Inspired":[50],"by":[51],"neural":[52],"networks,":[53],"multilayer":[55],"structure":[58],"combined":[59],"with":[60],"objective":[68],"such":[70],"that":[71,132],"reconstruction":[73],"error":[74],"two":[77],"are":[80],"optimized":[81],"simultaneously.":[82],"addition,":[84],"also":[86],"use":[87],"Gaussian":[89],"filter":[92],"noise":[94],"in":[95],"images":[97],"perform":[99],"principal":[101],"component":[102],"analysis":[103],"(PCA)":[104],"algorithm":[105],"extract":[107],"main":[109],"components":[110],"samples.":[113],"An":[114],"optimization":[115],"method":[116,135],"developed":[118],"solve":[120],"resulting":[122],"problem.":[123],"Experimental":[124],"results":[125],"performed":[126],"on":[127],"MSTAR":[129],"dataset":[130],"demonstrate":[131],"proposed":[134],"outperforms":[136],"some":[137],"representative":[138],"sparse":[142],"representation":[143],"schemes.":[144]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
