{"id":"https://openalex.org/W2901803942","doi":"https://doi.org/10.1109/icpr.2018.8545325","title":"An Incremental Multi-view Active Learning Algorithm for PolSAR Data Classification","display_name":"An Incremental Multi-view Active Learning Algorithm for PolSAR Data Classification","publication_year":2018,"publication_date":"2018-08-01","ids":{"openalex":"https://openalex.org/W2901803942","doi":"https://doi.org/10.1109/icpr.2018.8545325","mag":"2901803942"},"language":"en","primary_location":{"id":"doi:10.1109/icpr.2018.8545325","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr.2018.8545325","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 24th International Conference on Pattern Recognition (ICPR)","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/A5049695170","display_name":"Xiangli Nie","orcid":"https://orcid.org/0000-0001-5208-4670"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xiangli Nie","raw_affiliation_strings":["Beijing Key Laboratory of Research and Application for Robotic Intelligence of Hand-Eye-Brain Interaction, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Key Laboratory of Research and Application for Robotic Intelligence of Hand-Eye-Brain Interaction, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088762168","display_name":"Yongkang Luo","orcid":"https://orcid.org/0000-0003-0651-8794"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210094879","display_name":"Shandong Institute of Automation","ror":"https://ror.org/00qdtba35","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210094879","https://openalex.org/I4210142748"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yongkang Luo","raw_affiliation_strings":["Institute of Automation, Chinese Academy of Sciences, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Automation, Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I19820366","https://openalex.org/I4210094879"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026688050","display_name":"Hong Qiao","orcid":"https://orcid.org/0000-0001-6384-3687"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hong Qiao","raw_affiliation_strings":["Beijing Key Laboratory of Research and Application for Robotic Intelligence of Hand-Eye-Brain Interaction, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Key Laboratory of Research and Application for Robotic Intelligence of Hand-Eye-Brain Interaction, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100335192","display_name":"Bo Zhang","orcid":"https://orcid.org/0000-0001-7355-0941"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bo Zhang","raw_affiliation_strings":["Institute of Applied Mathematics, Chinese Academy of Sciences, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Applied Mathematics, Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I19820366"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5067046312","display_name":"Zhong\u2010Ping Jiang","orcid":"https://orcid.org/0000-0002-4868-9359"},"institutions":[{"id":"https://openalex.org/I57206974","display_name":"New York University","ror":"https://ror.org/0190ak572","country_code":"US","type":"education","lineage":["https://openalex.org/I57206974"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zhong-Ping Jiang","raw_affiliation_strings":["Tandon School of Engineering, New York University, Brooklyn, New York, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tandon School of Engineering, New York University, Brooklyn, New York, USA","institution_ids":["https://openalex.org/I57206974"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"7","issue":null,"first_page":"2251","last_page":"2255"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12072","display_name":"Machine Learning and Algorithms","score":0.9948999881744385,"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/T12072","display_name":"Machine Learning and Algorithms","score":0.9948999881744385,"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":0.9909999966621399,"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/T12676","display_name":"Machine Learning and ELM","score":0.9635999798774719,"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":0.7854654788970947},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6141560673713684},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6071335077285767},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5235340595245361},{"id":"https://openalex.org/keywords/synthetic-aperture-radar","display_name":"Synthetic aperture radar","score":0.5136680006980896},{"id":"https://openalex.org/keywords/categorization","display_name":"Categorization","score":0.45749861001968384},{"id":"https://openalex.org/keywords/statistical-classification","display_name":"Statistical classification","score":0.45698148012161255},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.41782838106155396},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.4132188558578491},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.34383687376976013},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.19559720158576965}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7854654788970947},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6141560673713684},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6071335077285767},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5235340595245361},{"id":"https://openalex.org/C87360688","wikidata":"https://www.wikidata.org/wiki/Q740686","display_name":"Synthetic aperture radar","level":2,"score":0.5136680006980896},{"id":"https://openalex.org/C94124525","wikidata":"https://www.wikidata.org/wiki/Q912550","display_name":"Categorization","level":2,"score":0.45749861001968384},{"id":"https://openalex.org/C110083411","wikidata":"https://www.wikidata.org/wiki/Q1744628","display_name":"Statistical classification","level":2,"score":0.45698148012161255},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.41782838106155396},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.4132188558578491},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.34383687376976013},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.19559720158576965}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icpr.2018.8545325","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr.2018.8545325","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 24th International Conference on Pattern Recognition (ICPR)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W1421632428","https://openalex.org/W1670132599","https://openalex.org/W1985194020","https://openalex.org/W1986710832","https://openalex.org/W2001337397","https://openalex.org/W2008826820","https://openalex.org/W2048578702","https://openalex.org/W2052190325","https://openalex.org/W2057571734","https://openalex.org/W2059644815","https://openalex.org/W2074866567","https://openalex.org/W2148825261","https://openalex.org/W2150388078","https://openalex.org/W2160218441","https://openalex.org/W2248623186","https://openalex.org/W2326253368","https://openalex.org/W2576338689","https://openalex.org/W2754863596","https://openalex.org/W2761412452","https://openalex.org/W3009009611","https://openalex.org/W6681723013","https://openalex.org/W6682114326","https://openalex.org/W6683584131"],"related_works":["https://openalex.org/W4210966920","https://openalex.org/W2156233651","https://openalex.org/W2005234362","https://openalex.org/W2550009779","https://openalex.org/W1997235926","https://openalex.org/W1975547468","https://openalex.org/W2804627982","https://openalex.org/W2001679188","https://openalex.org/W4205493345","https://openalex.org/W2196068029"],"abstract_inverted_index":{"The":[0],"fast":[1],"and":[2,19,57,115],"accurate":[3],"classification":[4,141],"of":[5,61,80,86,98,111,134],"polarimetric":[6],"synthetic":[7],"aperture":[8],"radar":[9],"(PolSAR)":[10],"data":[11,38,81,123],"in":[12],"dynamically":[13],"changing":[14],"environments":[15],"is":[16],"an":[17,27],"important":[18],"challenging":[20],"task.":[21],"In":[22,64],"this":[23,91],"paper,":[24],"we":[25],"propose":[26],"Incremental":[28],"Multi-view":[29],"Passive-Aggressive":[30],"Active":[31],"learning":[32],"algorithm,":[33],"named":[34],"IMPAA,":[35],"for":[36],"PolSAR":[37,62,122],"classification.":[39],"This":[40],"algorithm":[41,68,92],"can":[42,69,129],"deal":[43],"with":[44,144],"online":[45,140],"two-view":[46],"multi-class":[47],"categorization":[48],"problem":[49],"by":[50],"exploiting":[51],"the":[52,55,66,71,78,84,95,105,109,126],"relationship":[53],"between":[54],"polarimetric-color":[56],"texture":[58],"feature":[59],"sets":[60],"data.":[63],"addition,":[65],"IMPAA":[67],"handle":[70],"dynamic":[72],"large-scale":[73],"datasets":[74],"where":[75],"not":[76],"only":[77,93],"amount":[79],"but":[82],"also":[83],"number":[85],"classes":[87],"gradually":[88],"increases.":[89],"Moreover,":[90],"queries":[94],"class":[96],"labels":[97,136],"some":[99],"informative":[100],"incoming":[101],"samples":[102],"to":[103,137],"update":[104],"classifier":[106],"based":[107],"on":[108,120],"disagreement":[110],"different":[112],"views'":[113],"predictors":[114],"a":[116,131],"randomized":[117],"rule.":[118],"Experiments":[119],"real":[121],"demonstrate":[124],"that":[125],"proposed":[127],"method":[128],"use":[130],"smaller":[132],"fraction":[133],"queried":[135],"achieve":[138],"low":[139],"errors":[142],"compared":[143],"previously":[145],"known":[146],"methods.":[147]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1}],"updated_date":"2026-07-22T07:51:19.307946","created_date":"2025-10-10T00:00:00"}
