{"id":"https://openalex.org/W2012615666","doi":"https://doi.org/10.1109/tgrs.2014.2363356","title":"A Two-Component $K$\u2013Lognormal Mixture Model and Its Parameter Estimation Method","display_name":"A Two-Component $K$\u2013Lognormal Mixture Model and Its Parameter Estimation Method","publication_year":2014,"publication_date":"2014-10-31","ids":{"openalex":"https://openalex.org/W2012615666","doi":"https://doi.org/10.1109/tgrs.2014.2363356","mag":"2012615666"},"language":"en","primary_location":{"id":"doi:10.1109/tgrs.2014.2363356","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2014.2363356","pdf_url":null,"source":{"id":"https://openalex.org/S111326731","display_name":"IEEE Transactions on Geoscience and Remote Sensing","issn_l":"0196-2892","issn":["0196-2892","1558-0644"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Geoscience and Remote Sensing","raw_type":"journal-article"},"type":"article","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/A5036123085","display_name":"Xin Zhou","orcid":"https://orcid.org/0000-0003-1076-1033"},"institutions":[{"id":"https://openalex.org/I881766915","display_name":"Nanjing University","ror":"https://ror.org/01rxvg760","country_code":"CN","type":"education","lineage":["https://openalex.org/I881766915"]},{"id":"https://openalex.org/I9842412","display_name":"Nanjing University of Aeronautics and Astronautics","ror":"https://ror.org/01scyh794","country_code":"CN","type":"education","lineage":["https://openalex.org/I9842412"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xin Zhou","raw_affiliation_strings":["College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China","[Coll. of Autom. Eng., Nanjing Univ. of Aeronaut. & Astronaut., Nanjing, China]"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China","institution_ids":["https://openalex.org/I9842412"]},{"raw_affiliation_string":"[Coll. of Autom. Eng., Nanjing Univ. of Aeronaut. & Astronaut., Nanjing, China]","institution_ids":["https://openalex.org/I881766915"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061147953","display_name":"Rongkun Peng","orcid":null},"institutions":[{"id":"https://openalex.org/I9842412","display_name":"Nanjing University of Aeronautics and Astronautics","ror":"https://ror.org/01scyh794","country_code":"CN","type":"education","lineage":["https://openalex.org/I9842412"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Rongkun Peng","raw_affiliation_strings":["College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China","College of Automation Engineering, Nanjing University of Aeronautics and Astronautics Nanjing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China","institution_ids":["https://openalex.org/I9842412"]},{"raw_affiliation_string":"College of Automation Engineering, Nanjing University of Aeronautics and Astronautics Nanjing, China","institution_ids":["https://openalex.org/I9842412"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5079443528","display_name":"Congqing Wang","orcid":"https://orcid.org/0000-0003-2874-7678"},"institutions":[{"id":"https://openalex.org/I9842412","display_name":"Nanjing University of Aeronautics and Astronautics","ror":"https://ror.org/01scyh794","country_code":"CN","type":"education","lineage":["https://openalex.org/I9842412"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Congqing Wang","raw_affiliation_strings":["College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China","College of Automation Engineering, Nanjing University of Aeronautics and Astronautics Nanjing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China","institution_ids":["https://openalex.org/I9842412"]},{"raw_affiliation_string":"College of Automation Engineering, Nanjing University of Aeronautics and Astronautics Nanjing, China","institution_ids":["https://openalex.org/I9842412"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":2645,"currency":"USD","value_usd":2645},"apc_paid":null,"fwci":1.441,"has_fulltext":false,"cited_by_count":20,"citation_normalized_percentile":{"value":0.87072289,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"53","issue":"5","first_page":"2640","last_page":"2651"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9994999766349792,"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/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9994999766349792,"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/T10770","display_name":"Soil Geostatistics and Mapping","score":0.9876999855041504,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11871","display_name":"Advanced Statistical Methods and Models","score":0.9825000166893005,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/clutter","display_name":"Clutter","score":0.8334534168243408},{"id":"https://openalex.org/keywords/synthetic-aperture-radar","display_name":"Synthetic aperture radar","score":0.7254021167755127},{"id":"https://openalex.org/keywords/log-normal-distribution","display_name":"Log-normal distribution","score":0.6978665590286255},{"id":"https://openalex.org/keywords/mixture-model","display_name":"Mixture model","score":0.6600621938705444},{"id":"https://openalex.org/keywords/statistical-model","display_name":"Statistical model","score":0.6306406855583191},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6259279251098633},{"id":"https://openalex.org/keywords/expectation\u2013maximization-algorithm","display_name":"Expectation\u2013maximization algorithm","score":0.597130537033081},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.498992919921875},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.493522047996521},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.477695494890213},{"id":"https://openalex.org/keywords/synthetic-data","display_name":"Synthetic data","score":0.43880540132522583},{"id":"https://openalex.org/keywords/estimation-theory","display_name":"Estimation theory","score":0.43690380454063416},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4292847514152527},{"id":"https://openalex.org/keywords/radar","display_name":"Radar","score":0.2914605736732483},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.28180328011512756},{"id":"https://openalex.org/keywords/maximum-likelihood","display_name":"Maximum likelihood","score":0.26420074701309204},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.24546757340431213},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.23693856596946716}],"concepts":[{"id":"https://openalex.org/C132094186","wikidata":"https://www.wikidata.org/wiki/Q641585","display_name":"Clutter","level":3,"score":0.8334534168243408},{"id":"https://openalex.org/C87360688","wikidata":"https://www.wikidata.org/wiki/Q740686","display_name":"Synthetic aperture radar","level":2,"score":0.7254021167755127},{"id":"https://openalex.org/C151620405","wikidata":"https://www.wikidata.org/wiki/Q826116","display_name":"Log-normal distribution","level":2,"score":0.6978665590286255},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.6600621938705444},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.6306406855583191},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6259279251098633},{"id":"https://openalex.org/C182081679","wikidata":"https://www.wikidata.org/wiki/Q1275153","display_name":"Expectation\u2013maximization algorithm","level":3,"score":0.597130537033081},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.498992919921875},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.493522047996521},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.477695494890213},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.43880540132522583},{"id":"https://openalex.org/C167928553","wikidata":"https://www.wikidata.org/wiki/Q1376021","display_name":"Estimation theory","level":2,"score":0.43690380454063416},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4292847514152527},{"id":"https://openalex.org/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.2914605736732483},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.28180328011512756},{"id":"https://openalex.org/C49781872","wikidata":"https://www.wikidata.org/wiki/Q1045555","display_name":"Maximum likelihood","level":2,"score":0.26420074701309204},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.24546757340431213},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.23693856596946716},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tgrs.2014.2363356","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2014.2363356","pdf_url":null,"source":{"id":"https://openalex.org/S111326731","display_name":"IEEE Transactions on Geoscience and Remote Sensing","issn_l":"0196-2892","issn":["0196-2892","1558-0644"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Geoscience and Remote Sensing","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G7026968698","display_name":"\u57fa\u4e8e\u7edf\u8ba1\u6a21\u578b\u7684SAR\u56fe\u50cf\u76ee\u6807\u68c0\u6d4b\u548c\u8f6e\u5ed3\u63d0\u53d6\u7814\u7a76","funder_award_id":"61102138","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":37,"referenced_works":["https://openalex.org/W604181985","https://openalex.org/W1486632395","https://openalex.org/W1529200222","https://openalex.org/W1602107991","https://openalex.org/W1618045731","https://openalex.org/W1965430012","https://openalex.org/W1966476711","https://openalex.org/W1979968728","https://openalex.org/W1988520084","https://openalex.org/W1990300168","https://openalex.org/W1996176293","https://openalex.org/W1996950091","https://openalex.org/W2001826592","https://openalex.org/W2006407062","https://openalex.org/W2033495885","https://openalex.org/W2049633694","https://openalex.org/W2056107186","https://openalex.org/W2074319623","https://openalex.org/W2076063977","https://openalex.org/W2091451196","https://openalex.org/W2094842984","https://openalex.org/W2096433476","https://openalex.org/W2108474513","https://openalex.org/W2136449732","https://openalex.org/W2144615653","https://openalex.org/W2154610955","https://openalex.org/W2158347164","https://openalex.org/W2160216316","https://openalex.org/W2164453045","https://openalex.org/W2183998037","https://openalex.org/W2388826274","https://openalex.org/W3214568381","https://openalex.org/W6631584882","https://openalex.org/W6641869392","https://openalex.org/W6682713193","https://openalex.org/W7047110037","https://openalex.org/W7067970857"],"related_works":["https://openalex.org/W2473373438","https://openalex.org/W2368486525","https://openalex.org/W2077224612","https://openalex.org/W2153481672","https://openalex.org/W2153238387","https://openalex.org/W4312864369","https://openalex.org/W84255947","https://openalex.org/W2014842417","https://openalex.org/W2891133681","https://openalex.org/W2061347451"],"abstract_inverted_index":{"Statistical":[0],"models":[1,23],"are":[2,14,32],"used":[3],"for":[4,34,125,152],"describing":[5],"the":[6,15,28,35,66,69,73,88,102,106,113,120,127,130,139,142,147,154],"synthetic":[7],"aperture":[8],"radar":[9],"(SAR)":[10],"image":[11,19,30,39],"data":[12,31,75,108,111],"and":[13,51,78,93,109],"basis":[16],"of":[17,37,48,76,90,105,129,141],"SAR":[18,29,38,155],"interpretations.":[20,40],"Appropriate":[21],"statistical":[22,42,103],"that":[24],"can":[25],"accurately":[26],"describe":[27,87],"essential":[33],"performances":[36],"A":[41,115],"model,":[43],"which":[44],"is":[45,54,62,83,123],"a":[46,97],"mixture":[47,60,81,131,143],"K":[49],"distribution":[50],"lognormal":[52],"distribution,":[53],"proposed":[55,148],"in":[56,96,112],"this":[57],"paper.":[58],"This":[59,80],"model":[61,65,82,144],"able":[63,85],"to":[64,86,137],"clutter":[67,77,91,107],"data,":[68,71],"target":[70,94,110],"or":[72],"mixed":[74],"target.":[79],"also":[84],"proportions":[89],"region":[92,95],"scene":[98],"as":[99,101],"well":[100],"properties":[104],"scene.":[114],"maximum":[116],"likelihood":[117],"method":[118],"using":[119],"expectation-maximization":[121],"approach":[122],"derived":[124],"estimating":[126],"parameters":[128],"model.":[132],"Experiments":[133],"have":[134],"been":[135],"conducted":[136],"demonstrate":[138],"effectiveness":[140],"(together":[145],"with":[146],"parameter":[149],"estimation":[150],"method)":[151],"modeling":[153],"data.":[156]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":3},{"year":2018,"cited_by_count":2},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":2}],"updated_date":"2026-08-21T09:56:20.448147","created_date":"2025-10-10T00:00:00"}