{"id":"https://openalex.org/W2900469881","doi":"https://doi.org/10.1109/igarss.2018.8517585","title":"Analysis of Polarimetric Feature Combination Based on Polsar Image Classification Performance with Machine Learning Approach","display_name":"Analysis of Polarimetric Feature Combination Based on Polsar Image Classification Performance with Machine Learning Approach","publication_year":2018,"publication_date":"2018-07-01","ids":{"openalex":"https://openalex.org/W2900469881","doi":"https://doi.org/10.1109/igarss.2018.8517585","mag":"2900469881"},"language":"en","primary_location":{"id":"doi:10.1109/igarss.2018.8517585","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2018.8517585","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2018 - 2018 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/A5082881761","display_name":"Qiang Yin","orcid":"https://orcid.org/0000-0002-8413-4756"},"institutions":[{"id":"https://openalex.org/I75390827","display_name":"Beijing University of Chemical Technology","ror":"https://ror.org/00df5yc52","country_code":"CN","type":"education","lineage":["https://openalex.org/I75390827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qiang Yin","raw_affiliation_strings":["Beijing University of Chemical Technology, Dong, Lu, P.R. China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing University of Chemical Technology, Dong, Lu, P.R. China","institution_ids":["https://openalex.org/I75390827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112078435","display_name":"Wen Hong","orcid":"https://orcid.org/0000-0002-1025-9812"},"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/I4210110458","display_name":"Institute of Electronics","ror":"https://ror.org/01z143507","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210110458"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wen Hong","raw_affiliation_strings":["Institute of Electronics, Chinese Academy of Sciences, Xilu, P.R. China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Electronics, Chinese Academy of Sciences, Xilu, P.R. China","institution_ids":["https://openalex.org/I4210110458","https://openalex.org/I19820366"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100690860","display_name":"Fan Zhang","orcid":"https://orcid.org/0000-0002-2058-2373"},"institutions":[{"id":"https://openalex.org/I75390827","display_name":"Beijing University of Chemical Technology","ror":"https://ror.org/00df5yc52","country_code":"CN","type":"education","lineage":["https://openalex.org/I75390827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fan Zhang","raw_affiliation_strings":["Beijing University of Chemical Technology, Dong, Lu, P.R. China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing University of Chemical Technology, Dong, Lu, P.R. China","institution_ids":["https://openalex.org/I75390827"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5104090114","display_name":"\u00c9ric Pottier","orcid":null},"institutions":[{"id":"https://openalex.org/I56067802","display_name":"Universit\u00e9 de Rennes","ror":"https://ror.org/015m7wh34","country_code":"FR","type":"education","lineage":["https://openalex.org/I56067802"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Eric Pottier","raw_affiliation_strings":["I.E.T.R.-UMR, University of Rennes1, France"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"I.E.T.R.-UMR, University of Rennes1, France","institution_ids":["https://openalex.org/I56067802"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"8124","last_page":"8127"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10801","display_name":"Synthetic Aperture Radar (SAR) Applications and Techniques","score":0.9998999834060669,"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"}},"topics":[{"id":"https://openalex.org/T10801","display_name":"Synthetic Aperture Radar (SAR) Applications and Techniques","score":0.9998999834060669,"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/T10689","display_name":"Remote-Sensing Image Classification","score":0.9973999857902527,"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":0.9847000241279602,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/polarimetry","display_name":"Polarimetry","score":0.7382993698120117},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7221685647964478},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.6795177459716797},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6608102917671204},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6511653065681458},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.582429826259613},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.522305428981781},{"id":"https://openalex.org/keywords/subspace-topology","display_name":"Subspace topology","score":0.5164904594421387},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.509409487247467},{"id":"https://openalex.org/keywords/covariance-matrix","display_name":"Covariance matrix","score":0.4650861620903015},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.4106394946575165},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.37444883584976196},{"id":"https://openalex.org/keywords/scattering","display_name":"Scattering","score":0.29278111457824707},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.23919260501861572},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.16564622521400452},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.09558308124542236}],"concepts":[{"id":"https://openalex.org/C28493345","wikidata":"https://www.wikidata.org/wiki/Q899381","display_name":"Polarimetry","level":3,"score":0.7382993698120117},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7221685647964478},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.6795177459716797},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6608102917671204},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6511653065681458},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.582429826259613},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.522305428981781},{"id":"https://openalex.org/C32834561","wikidata":"https://www.wikidata.org/wiki/Q660730","display_name":"Subspace topology","level":2,"score":0.5164904594421387},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.509409487247467},{"id":"https://openalex.org/C185142706","wikidata":"https://www.wikidata.org/wiki/Q1134404","display_name":"Covariance matrix","level":2,"score":0.4650861620903015},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.4106394946575165},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.37444883584976196},{"id":"https://openalex.org/C191486275","wikidata":"https://www.wikidata.org/wiki/Q210028","display_name":"Scattering","level":2,"score":0.29278111457824707},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.23919260501861572},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.16564622521400452},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.09558308124542236},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","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},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/igarss.2018.8517585","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2018.8517585","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-article"},{"id":"pmh:oai:HAL:hal-02018918v1","is_oa":false,"landing_page_url":"https://univ-rennes.hal.science/hal-02018918","pdf_url":null,"source":{"id":"https://openalex.org/S4306402512","display_name":"HAL (Le Centre pour la Communication Scientifique Directe)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1294671590","host_organization_name":"Centre National de la Recherche Scientifique","host_organization_lineage":["https://openalex.org/I1294671590"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"38th IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Jul 2018, Valencia, Spain. &#x27E8;10.1109/igarss.2018.8517585&#x27E9;","raw_type":"info:eu-repo/semantics/conferenceObject"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":9,"referenced_works":["https://openalex.org/W1421632428","https://openalex.org/W2082706510","https://openalex.org/W2111758425","https://openalex.org/W2130082760","https://openalex.org/W2146873040","https://openalex.org/W2248623186","https://openalex.org/W2547983557","https://openalex.org/W2766130785","https://openalex.org/W6671223015"],"related_works":["https://openalex.org/W1980381208","https://openalex.org/W2026860918","https://openalex.org/W2035593284","https://openalex.org/W2364594919","https://openalex.org/W2612145225","https://openalex.org/W2225281849","https://openalex.org/W2167092671","https://openalex.org/W2161058488","https://openalex.org/W2158511632","https://openalex.org/W4390143830"],"abstract_inverted_index":{"The":[0,151],"polarimetric":[1,32,47,71,87,142],"features":[2,72,122,134,143],"of":[3,11,27,41,69,78,97,114],"PolSAR":[4,129],"images":[5],"includes":[6],"the":[7,25,39,60,70,76,79,85,94,121,127,139],"inherent":[8],"scattering":[9,33],"mechanisms":[10],"terrain":[12],"types,":[13],"which":[14],"is":[15,117],"important":[16],"for":[17,123],"classification":[18,80,95,124,146],"and":[19,102],"other":[20,154],"earth":[21],"observation":[22],"applications.":[23],"By":[24],"use":[26],"target":[28],"decomposition":[29],"methods,":[30],"many":[31],"components":[34],"can":[35,54],"be":[36,64,158],"obtained.":[37],"Besides,":[38],"elements":[40],"Coherency/Covariance":[42],"Matrix,":[43],"as":[44,46,50,75],"well":[45],"descriptors":[48],"such":[49],"SPAN,":[51],"SERD/DERD":[52],"etc.,":[53],"also":[55],"provide":[56],"characteristic":[57],"information.":[58],"However,":[59],"computation":[61],"cost":[62],"will":[63,157],"very":[65],"high":[66],"if":[67],"all":[68],"are":[73,90],"employed":[74],"input":[77],"process.":[81],"In":[82],"this":[83],"paper,":[84],"effective":[86],"feature":[88],"combination":[89],"studied":[91],"based":[92],"on":[93,112,153],"performance":[96],"SVM":[98],"(Support":[99],"Vector":[100],"Machine)":[101],"NRS":[103],"(Nearest-Regularized":[104],"Subspace)":[105],"machine":[106],"learning":[107],"approaches.":[108],"A":[109],"fast":[110],"strategy":[111],"basis":[113],"correlation":[115],"coefficient":[116],"used":[118],"to":[119,149],"select":[120],"experiments.":[125],"For":[126],"airborne":[128],"data":[130,155],"in":[131],"Flevoland,":[132],"10":[133],"have":[135],"been":[136],"selected":[137],"from":[138],"total":[140],"107":[141],"with":[144],"good":[145],"accuracy":[147],"up":[148],"93.6%.":[150],"experiments":[152],"sets":[156],"shown.":[159]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":4}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
