{"id":"https://openalex.org/W2490087565","doi":"https://doi.org/10.1109/tgrs.2016.2590145","title":"Region-Based Change Detection for Polarimetric SAR Images Using Wishart Mixture Models","display_name":"Region-Based Change Detection for Polarimetric SAR Images Using Wishart Mixture Models","publication_year":2016,"publication_date":"2016-08-05","ids":{"openalex":"https://openalex.org/W2490087565","doi":"https://doi.org/10.1109/tgrs.2016.2590145","mag":"2490087565"},"language":"en","primary_location":{"id":"doi:10.1109/tgrs.2016.2590145","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2016.2590145","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/A5069540177","display_name":"Wen Yang","orcid":"https://orcid.org/0000-0002-3263-8768"},"institutions":[{"id":"https://openalex.org/I4210118728","display_name":"State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing","ror":"https://ror.org/02bpap860","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210118728"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Wen Yang","raw_affiliation_strings":["Electronic Information School, State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan, China"],"affiliations":[{"raw_affiliation_string":"Electronic Information School, State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan, China","institution_ids":["https://openalex.org/I4210118728"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033111505","display_name":"Xiangli Yang","orcid":"https://orcid.org/0000-0001-8562-5576"},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiangli Yang","raw_affiliation_strings":["Electronic Information School, Wuhan University, Wuhan, China"],"affiliations":[{"raw_affiliation_string":"Electronic Information School, Wuhan University, Wuhan, China","institution_ids":["https://openalex.org/I37461747"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016394154","display_name":"Tianheng Yan","orcid":null},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tianheng Yan","raw_affiliation_strings":["Electronic Information School, Wuhan University, Wuhan, China"],"affiliations":[{"raw_affiliation_string":"Electronic Information School, Wuhan University, Wuhan, China","institution_ids":["https://openalex.org/I37461747"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011086593","display_name":"Hui Song","orcid":"https://orcid.org/0000-0002-9429-7997"},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hui Song","raw_affiliation_strings":["Electronic Information School, Wuhan University, Wuhan, China"],"affiliations":[{"raw_affiliation_string":"Electronic Information School, Wuhan University, Wuhan, China","institution_ids":["https://openalex.org/I37461747"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5073032922","display_name":"Gui-Song Xia","orcid":"https://orcid.org/0000-0001-7660-6090"},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Gui-Song Xia","raw_affiliation_strings":["Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan, China"],"affiliations":[{"raw_affiliation_string":"Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan, China","institution_ids":["https://openalex.org/I37461747"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5069540177"],"corresponding_institution_ids":["https://openalex.org/I4210118728"],"apc_list":null,"apc_paid":null,"fwci":7.4279,"has_fulltext":false,"cited_by_count":46,"citation_normalized_percentile":{"value":0.97479474,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":"54","issue":"11","first_page":"6746","last_page":"6756"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9998000264167786,"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"}},"topics":[{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9998000264167786,"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/T10801","display_name":"Synthetic Aperture Radar (SAR) Applications and Techniques","score":0.9962999820709229,"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/T12157","display_name":"Geochemistry and Geologic Mapping","score":0.9689000248908997,"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/wishart-distribution","display_name":"Wishart distribution","score":0.7947906255722046},{"id":"https://openalex.org/keywords/change-detection","display_name":"Change detection","score":0.7594397664070129},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6570574045181274},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6479370594024658},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6195591688156128},{"id":"https://openalex.org/keywords/smoothing","display_name":"Smoothing","score":0.595471203327179},{"id":"https://openalex.org/keywords/markov-random-field","display_name":"Markov random field","score":0.5656715631484985},{"id":"https://openalex.org/keywords/thresholding","display_name":"Thresholding","score":0.5058209896087646},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.5025215148925781},{"id":"https://openalex.org/keywords/divergence","display_name":"Divergence (linguistics)","score":0.48660600185394287},{"id":"https://openalex.org/keywords/synthetic-aperture-radar","display_name":"Synthetic aperture radar","score":0.4659819006919861},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4540596008300781},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.43679705262184143},{"id":"https://openalex.org/keywords/speckle-noise","display_name":"Speckle noise","score":0.4125574231147766},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.38192126154899597},{"id":"https://openalex.org/keywords/speckle-pattern","display_name":"Speckle pattern","score":0.37777042388916016},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.2560039162635803},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.254014790058136},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.11139357089996338}],"concepts":[{"id":"https://openalex.org/C33962027","wikidata":"https://www.wikidata.org/wiki/Q1930697","display_name":"Wishart distribution","level":3,"score":0.7947906255722046},{"id":"https://openalex.org/C203595873","wikidata":"https://www.wikidata.org/wiki/Q25389927","display_name":"Change detection","level":2,"score":0.7594397664070129},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6570574045181274},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6479370594024658},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6195591688156128},{"id":"https://openalex.org/C3770464","wikidata":"https://www.wikidata.org/wiki/Q775963","display_name":"Smoothing","level":2,"score":0.595471203327179},{"id":"https://openalex.org/C2778045648","wikidata":"https://www.wikidata.org/wiki/Q176827","display_name":"Markov random field","level":4,"score":0.5656715631484985},{"id":"https://openalex.org/C191178318","wikidata":"https://www.wikidata.org/wiki/Q2256906","display_name":"Thresholding","level":3,"score":0.5058209896087646},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.5025215148925781},{"id":"https://openalex.org/C207390915","wikidata":"https://www.wikidata.org/wiki/Q1230525","display_name":"Divergence (linguistics)","level":2,"score":0.48660600185394287},{"id":"https://openalex.org/C87360688","wikidata":"https://www.wikidata.org/wiki/Q740686","display_name":"Synthetic aperture radar","level":2,"score":0.4659819006919861},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4540596008300781},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.43679705262184143},{"id":"https://openalex.org/C180940675","wikidata":"https://www.wikidata.org/wiki/Q7575045","display_name":"Speckle noise","level":3,"score":0.4125574231147766},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.38192126154899597},{"id":"https://openalex.org/C102290492","wikidata":"https://www.wikidata.org/wiki/Q7575045","display_name":"Speckle pattern","level":2,"score":0.37777042388916016},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.2560039162635803},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.254014790058136},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.11139357089996338},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tgrs.2016.2590145","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2016.2590145","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":[{"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities","score":0.41999998688697815}],"awards":[{"id":"https://openalex.org/G1421895506","display_name":null,"funder_award_id":"61271401","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8376165696","display_name":null,"funder_award_id":"61331016","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"},{"id":"https://openalex.org/F4320321851","display_name":"Fudan University","ror":"https://ror.org/013q1eq08"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":60,"referenced_works":["https://openalex.org/W12623448","https://openalex.org/W30635995","https://openalex.org/W66352275","https://openalex.org/W114546079","https://openalex.org/W1421632428","https://openalex.org/W1497984769","https://openalex.org/W1530000988","https://openalex.org/W1544440891","https://openalex.org/W1562601073","https://openalex.org/W1567885833","https://openalex.org/W1593043479","https://openalex.org/W1599474586","https://openalex.org/W1807380328","https://openalex.org/W1966155668","https://openalex.org/W1976193075","https://openalex.org/W1985194020","https://openalex.org/W2006002971","https://openalex.org/W2008049496","https://openalex.org/W2008078539","https://openalex.org/W2018243050","https://openalex.org/W2020880513","https://openalex.org/W2027091505","https://openalex.org/W2033903783","https://openalex.org/W2034073840","https://openalex.org/W2040886766","https://openalex.org/W2041122055","https://openalex.org/W2041972612","https://openalex.org/W2044976920","https://openalex.org/W2057047626","https://openalex.org/W2069045878","https://openalex.org/W2071681365","https://openalex.org/W2076063977","https://openalex.org/W2079043399","https://openalex.org/W2080647739","https://openalex.org/W2083857818","https://openalex.org/W2085567910","https://openalex.org/W2086016807","https://openalex.org/W2091542043","https://openalex.org/W2100109597","https://openalex.org/W2105548357","https://openalex.org/W2106052468","https://openalex.org/W2109900939","https://openalex.org/W2110519070","https://openalex.org/W2112728479","https://openalex.org/W2113069932","https://openalex.org/W2118246710","https://openalex.org/W2121348175","https://openalex.org/W2126176832","https://openalex.org/W2127193590","https://openalex.org/W2137102674","https://openalex.org/W2143516773","https://openalex.org/W2153238387","https://openalex.org/W2159377629","https://openalex.org/W2159983350","https://openalex.org/W2169127445","https://openalex.org/W2324183516","https://openalex.org/W3100146037","https://openalex.org/W4237764598","https://openalex.org/W6638365234","https://openalex.org/W6684468765"],"related_works":["https://openalex.org/W2374674032","https://openalex.org/W1964022742","https://openalex.org/W1995905720","https://openalex.org/W2027315098","https://openalex.org/W2376252298","https://openalex.org/W2117367882","https://openalex.org/W1844344045","https://openalex.org/W1836777847","https://openalex.org/W2771640644","https://openalex.org/W2054181726"],"abstract_inverted_index":{"The":[0,90,158,189],"change":[1,43,62,77,174],"detection":[2,44,63,78,175],"of":[3,19,45,85,95],"polarimetric":[4],"synthetic":[5],"aperture":[6],"radar":[7],"(PolSAR)":[8],"images":[9,82,97,114],"is":[10,49,58,161,192],"a":[11,75,139],"longstanding":[12],"and":[13,55,73,105,200],"challenging":[14],"task,":[15],"not":[16],"only":[17],"because":[18],"the":[20,27,42,50,56,59,71,93,103,106,123,128,173,180,209],"speckle":[21],"issue":[22],"but":[23],"also":[24],"due":[25],"to":[26,132,208],"complex":[28],"texture,":[29],"which":[30],"generally":[31],"appears":[32],"highly":[33],"heterogeneous.":[34],"There":[35],"are":[36,115,130,150,177],"two":[37,112],"widely":[38],"used":[39,131],"approaches":[40],"for":[41,80,153,170],"PolSAR":[46,81,96,113,196],"images:":[47],"one":[48],"post":[51],"classification":[52],"comparison":[53],"algorithm,":[54,126],"other":[57],"directly":[60],"unsupervised":[61],"algorithm.":[64],"In":[65],"this":[66],"paper,":[67],"we":[68],"focus":[69],"on":[70,194],"latter":[72],"propose":[74],"region-based":[76],"method":[79,183],"by":[83,146,179],"means":[84],"Wishart":[86],"mixture":[87],"models":[88],"(WMMs).":[89],"WMMs":[91,129],"fit":[92],"distribution":[94,143],"with":[98,184],"less":[99],"errors":[100],"both":[101],"in":[102],"homogeneous":[104],"extremely":[107],"heterogeneous":[108],"area.":[109],"More":[110],"precisely,":[111],"first":[116],"segmented":[117],"into":[118],"compact":[119],"local":[120,135,155],"regions":[121],"using":[122],"customized":[124],"simple-linear-iterative-clustering":[125],"while":[127],"model":[133],"each":[134],"region.":[136],"To":[137],"generate":[138],"difference":[140],"map,":[141],"statistical":[142],"differences":[144],"measured":[145],"information":[147],"theoretic":[148],"divergence":[149,160],"then":[151],"computed":[152],"corresponding":[154],"region":[156],"pairs.":[157],"Cauchy-Schwarz":[159],"adopted":[162],"as":[163],"its":[164,204],"analytic":[165],"expression":[166],"can":[167],"be":[168],"derived":[169],"WMMs.":[171],"Finally,":[172],"results":[176],"obtained":[178],"Kittler-Illingworth":[181],"thresholding":[182],"Markov":[185],"random":[186],"field-based":[187],"smoothing.":[188],"proposed":[190],"scheme":[191],"tested":[193],"different":[195],"data":[197],"sets.":[198],"Qualitative":[199],"quantitative":[201],"evaluations":[202],"show":[203],"superior":[205],"performance":[206],"comparing":[207],"traditional":[210],"pixel-level":[211],"approach.":[212]},"counts_by_year":[{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":7},{"year":2019,"cited_by_count":9},{"year":2018,"cited_by_count":7},{"year":2017,"cited_by_count":7}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
