{"id":"https://openalex.org/W2807098087","doi":"https://doi.org/10.1587/transinf.2017edl8281","title":"Extreme Learning Machine with Superpixel-Guided Composite Kernels for SAR Image Classification","display_name":"Extreme Learning Machine with Superpixel-Guided Composite Kernels for SAR Image Classification","publication_year":2018,"publication_date":"2018-05-31","ids":{"openalex":"https://openalex.org/W2807098087","doi":"https://doi.org/10.1587/transinf.2017edl8281","mag":"2807098087"},"language":"en","primary_location":{"id":"doi:10.1587/transinf.2017edl8281","is_oa":true,"landing_page_url":"https://doi.org/10.1587/transinf.2017edl8281","pdf_url":"https://www.jstage.jst.go.jp/article/transinf/E101.D/6/E101.D_2017EDL8281/_pdf","source":{"id":"https://openalex.org/S2486202937","display_name":"IEICE Transactions on Information and Systems","issn_l":"0916-8532","issn":["0916-8532","1745-1361"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4320800604","host_organization_name":"Institute of Electronics, Information and Communication Engineers","host_organization_lineage":["https://openalex.org/P4320800604"],"host_organization_lineage_names":["Institute of Electronics, Information and Communication Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEICE Transactions on Information and Systems","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://www.jstage.jst.go.jp/article/transinf/E101.D/6/E101.D_2017EDL8281/_pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5007685826","display_name":"Dongdong Guan","orcid":"https://orcid.org/0000-0001-5025-8200"},"institutions":[{"id":"https://openalex.org/I170215575","display_name":"National University of Defense Technology","ror":"https://ror.org/05d2yfz11","country_code":"CN","type":"education","lineage":["https://openalex.org/I170215575"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dongdong GUAN","raw_affiliation_strings":["College of Electronic Science, National University of Defense Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Electronic Science, National University of Defense Technology","institution_ids":["https://openalex.org/I170215575"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5091191629","display_name":"Xiaoan Tang","orcid":"https://orcid.org/0000-0002-4624-3687"},"institutions":[{"id":"https://openalex.org/I170215575","display_name":"National University of Defense Technology","ror":"https://ror.org/05d2yfz11","country_code":"CN","type":"education","lineage":["https://openalex.org/I170215575"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaoan TANG","raw_affiliation_strings":["College of Electronic Science, National University of Defense Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Electronic Science, National University of Defense Technology","institution_ids":["https://openalex.org/I170215575"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100335821","display_name":"Li Wang","orcid":"https://orcid.org/0000-0001-6277-3234"},"institutions":[{"id":"https://openalex.org/I170215575","display_name":"National University of Defense Technology","ror":"https://ror.org/05d2yfz11","country_code":"CN","type":"education","lineage":["https://openalex.org/I170215575"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Li WANG","raw_affiliation_strings":["College of Electronic Science, National University of Defense Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Electronic Science, National University of Defense Technology","institution_ids":["https://openalex.org/I170215575"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5021079933","display_name":"Junda Zhang","orcid":"https://orcid.org/0009-0006-1159-5224"},"institutions":[{"id":"https://openalex.org/I170215575","display_name":"National University of Defense Technology","ror":"https://ror.org/05d2yfz11","country_code":"CN","type":"education","lineage":["https://openalex.org/I170215575"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Junda ZHANG","raw_affiliation_strings":["College of Electronic Science, National University of Defense Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Electronic Science, National University of Defense Technology","institution_ids":["https://openalex.org/I170215575"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I170215575"],"apc_list":null,"apc_paid":null,"fwci":0.4914,"has_fulltext":true,"cited_by_count":4,"citation_normalized_percentile":{"value":0.72441499,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":97},"biblio":{"volume":"E101.D","issue":"6","first_page":"1703","last_page":"1706"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12676","display_name":"Machine Learning and ELM","score":1.0,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12676","display_name":"Machine Learning and ELM","score":1.0,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9922000169754028,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9897000193595886,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.765799880027771},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7097721099853516},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.689525306224823},{"id":"https://openalex.org/keywords/synthetic-aperture-radar","display_name":"Synthetic aperture radar","score":0.659064531326294},{"id":"https://openalex.org/keywords/extreme-learning-machine","display_name":"Extreme learning machine","score":0.6237594485282898},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.5362638235092163},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.49030497670173645},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.4878232777118683},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.4650142788887024},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.41501206159591675},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3561614751815796},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.22381049394607544}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.765799880027771},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7097721099853516},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.689525306224823},{"id":"https://openalex.org/C87360688","wikidata":"https://www.wikidata.org/wiki/Q740686","display_name":"Synthetic aperture radar","level":2,"score":0.659064531326294},{"id":"https://openalex.org/C2780150128","wikidata":"https://www.wikidata.org/wiki/Q21948731","display_name":"Extreme learning machine","level":3,"score":0.6237594485282898},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.5362638235092163},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.49030497670173645},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.4878232777118683},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.4650142788887024},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.41501206159591675},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3561614751815796},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.22381049394607544}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1587/transinf.2017edl8281","is_oa":true,"landing_page_url":"https://doi.org/10.1587/transinf.2017edl8281","pdf_url":"https://www.jstage.jst.go.jp/article/transinf/E101.D/6/E101.D_2017EDL8281/_pdf","source":{"id":"https://openalex.org/S2486202937","display_name":"IEICE Transactions on Information and Systems","issn_l":"0916-8532","issn":["0916-8532","1745-1361"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4320800604","host_organization_name":"Institute of Electronics, Information and Communication Engineers","host_organization_lineage":["https://openalex.org/P4320800604"],"host_organization_lineage_names":["Institute of Electronics, Information and Communication Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEICE Transactions on Information and Systems","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1587/transinf.2017edl8281","is_oa":true,"landing_page_url":"https://doi.org/10.1587/transinf.2017edl8281","pdf_url":"https://www.jstage.jst.go.jp/article/transinf/E101.D/6/E101.D_2017EDL8281/_pdf","source":{"id":"https://openalex.org/S2486202937","display_name":"IEICE Transactions on Information and Systems","issn_l":"0916-8532","issn":["0916-8532","1745-1361"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4320800604","host_organization_name":"Institute of Electronics, Information and Communication Engineers","host_organization_lineage":["https://openalex.org/P4320800604"],"host_organization_lineage_names":["Institute of Electronics, Information and Communication Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEICE Transactions on Information and Systems","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320338291","display_name":"Sandia National Laboratories","ror":"https://ror.org/01apwpt12"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2807098087.pdf","grobid_xml":"https://content.openalex.org/works/W2807098087.grobid-xml"},"referenced_works_count":10,"referenced_works":["https://openalex.org/W2010015001","https://openalex.org/W2010491386","https://openalex.org/W2026131661","https://openalex.org/W2096045557","https://openalex.org/W2118246710","https://openalex.org/W2121348175","https://openalex.org/W2153635508","https://openalex.org/W2164330327","https://openalex.org/W2168880067","https://openalex.org/W2291071080"],"related_works":["https://openalex.org/W2067443264","https://openalex.org/W31566076","https://openalex.org/W4297902562","https://openalex.org/W2741186499","https://openalex.org/W2906710337","https://openalex.org/W4210966920","https://openalex.org/W2005234362","https://openalex.org/W2550009779","https://openalex.org/W2162970382","https://openalex.org/W1997235926"],"abstract_inverted_index":{"Synthetic":[0],"aperture":[1],"radar":[2],"(SAR)":[3],"image":[4,18,126,130],"classification":[5,25,141],"is":[6,55,73,81,101,115,136],"a":[7,23,47,67],"popular":[8],"yet":[9],"challenging":[10],"research":[11],"topic":[12],"in":[13,87],"the":[14,34,41,70,77,85,88,94,99,105,113,133],"field":[15],"of":[16,65,96],"SAR":[17,62,125,129],"interpretation.":[19],"This":[20],"paper":[21],"presents":[22],"new":[24],"method":[26],"based":[27],"on":[28,104,122],"extreme":[29],"learning":[30],"machine":[31],"(ELM)":[32],"and":[33,90,108,127],"superpixel-guided":[35],"composite":[36,97],"kernels":[37],"(SGCK).":[38],"By":[39],"introducing":[40],"generalized":[42],"likelihood":[43],"ratio":[44],"(GLR)":[45],"similarity,":[46],"modified":[48],"simple":[49],"linear":[50],"iterative":[51],"clustering":[52],"(SLIC)":[53],"algorithm":[54],"firstly":[56],"developed":[57],"to":[58,75,83,138],"generate":[59],"superpixel":[60,72],"for":[61],"image.":[63],"Instead":[64],"using":[66],"fixed-size":[68],"region,":[69],"shape-adaptive":[71],"used":[74],"exploit":[76],"spatial":[78,106],"information,":[79],"which":[80],"effective":[82],"classify":[84],"pixels":[86],"detailed":[89],"near-edge":[91],"regions.":[92],"Following":[93],"framework":[95,135],"kernels,":[98],"SGCK":[100,114],"constructed":[102],"base":[103],"information":[107],"backscatter":[109],"intensity":[110],"information.":[111],"Finally,":[112],"incorporated":[116],"an":[117],"ELM":[118],"classifier.":[119],"Experimental":[120],"results":[121],"both":[123],"simulated":[124],"real":[128],"demonstrate":[131],"that":[132],"proposed":[134],"superior":[137],"some":[139],"traditional":[140],"methods.":[142]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2020,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}