{"id":"https://openalex.org/W3024707412","doi":"https://doi.org/10.1109/tgrs.2020.2988982","title":"Semi-Supervised PolSAR Image Classification Based on Improved Tri-Training With a Minimum Spanning Tree","display_name":"Semi-Supervised PolSAR Image Classification Based on Improved Tri-Training With a Minimum Spanning Tree","publication_year":2020,"publication_date":"2020-05-15","ids":{"openalex":"https://openalex.org/W3024707412","doi":"https://doi.org/10.1109/tgrs.2020.2988982","mag":"3024707412"},"language":"en","primary_location":{"id":"doi:10.1109/tgrs.2020.2988982","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2020.2988982","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/A5065660222","display_name":"Shuang Wang","orcid":"https://orcid.org/0000-0003-4940-1211"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shuang Wang","raw_affiliation_strings":["Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University, Xi\u2019an, China","Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University, Xi'an, China"],"raw_orcid":"https://orcid.org/0000-0003-4940-1211","affiliations":[{"raw_affiliation_string":"Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University, Xi\u2019an, China","institution_ids":["https://openalex.org/I149594827"]},{"raw_affiliation_string":"Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University, Xi'an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040240404","display_name":"Yanhe Guo","orcid":"https://orcid.org/0009-0003-6144-5070"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yanhe Guo","raw_affiliation_strings":["Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University, Xi\u2019an, China","Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University, Xi'an, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University, Xi\u2019an, China","institution_ids":["https://openalex.org/I149594827"]},{"raw_affiliation_string":"Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University, Xi'an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Wenqiang Hua","orcid":"https://orcid.org/0000-0003-2611-6194"},"institutions":[{"id":"https://openalex.org/I4210136859","display_name":"Xi\u2019an University of Posts and Telecommunications","ror":"https://ror.org/04jn0td46","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210136859"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenqiang Hua","raw_affiliation_strings":["Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing, School of Computer Science and Technology, Xi\u2019an University of Posts and Telecommunications, Xi\u2019an, China","Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing, School of Computer Science and Technology, Xi'an University of Posts and Telecommunications, Xi'an, China"],"raw_orcid":"https://orcid.org/0000-0003-2611-6194","affiliations":[{"raw_affiliation_string":"Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing, School of Computer Science and Technology, Xi\u2019an University of Posts and Telecommunications, Xi\u2019an, China","institution_ids":["https://openalex.org/I4210136859"]},{"raw_affiliation_string":"Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing, School of Computer Science and Technology, Xi'an University of Posts and Telecommunications, Xi'an, China","institution_ids":["https://openalex.org/I4210136859"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052747154","display_name":"Xinan Liu","orcid":"https://orcid.org/0000-0002-9837-7329"},"institutions":[{"id":"https://openalex.org/I66946132","display_name":"University of Maryland, College Park","ror":"https://ror.org/047s2c258","country_code":"US","type":"education","lineage":["https://openalex.org/I66946132"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xinan Liu","raw_affiliation_strings":["University of Maryland, College Park, MD, USA"],"raw_orcid":"https://orcid.org/0000-0002-9837-7329","affiliations":[{"raw_affiliation_string":"University of Maryland, College Park, MD, USA","institution_ids":["https://openalex.org/I66946132"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5036677404","display_name":"Song Guo-xin","orcid":null},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guoxin Song","raw_affiliation_strings":["Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University, Xi\u2019an, China","Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University, Xi'an, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University, Xi\u2019an, China","institution_ids":["https://openalex.org/I149594827"]},{"raw_affiliation_string":"Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University, Xi'an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043022387","display_name":"Biao Hou","orcid":"https://orcid.org/0000-0002-1996-186X"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Biao Hou","raw_affiliation_strings":["Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University, Xi\u2019an, China","Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University, Xi'an, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University, Xi\u2019an, China","institution_ids":["https://openalex.org/I149594827"]},{"raw_affiliation_string":"Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University, Xi'an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5050630882","display_name":"Licheng Jiao","orcid":"https://orcid.org/0000-0003-3354-9617"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Licheng Jiao","raw_affiliation_strings":["Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University, Xi\u2019an, China","Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University, Xi'an, China"],"raw_orcid":"https://orcid.org/0000-0003-3354-9617","affiliations":[{"raw_affiliation_string":"Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University, Xi\u2019an, China","institution_ids":["https://openalex.org/I149594827"]},{"raw_affiliation_string":"Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University, Xi'an, China","institution_ids":["https://openalex.org/I149594827"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":51.0032,"has_fulltext":false,"cited_by_count":47,"citation_normalized_percentile":{"value":0.99661635,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":"58","issue":"12","first_page":"8583","last_page":"8597"},"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/T11038","display_name":"Advanced SAR Imaging Techniques","score":0.9959999918937683,"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/T11312","display_name":"Soil Moisture and Remote Sensing","score":0.9936000108718872,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7030652761459351},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.668393611907959},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6381008625030518},{"id":"https://openalex.org/keywords/synthetic-aperture-radar","display_name":"Synthetic aperture radar","score":0.5577297806739807},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.48837488889694214},{"id":"https://openalex.org/keywords/minimum-spanning-tree","display_name":"Minimum spanning tree","score":0.4869168996810913},{"id":"https://openalex.org/keywords/terrain","display_name":"Terrain","score":0.4657863676548004},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.42880815267562866},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.36133772134780884},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.13586261868476868},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.07716569304466248}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7030652761459351},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.668393611907959},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6381008625030518},{"id":"https://openalex.org/C87360688","wikidata":"https://www.wikidata.org/wiki/Q740686","display_name":"Synthetic aperture radar","level":2,"score":0.5577297806739807},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.48837488889694214},{"id":"https://openalex.org/C13743678","wikidata":"https://www.wikidata.org/wiki/Q240464","display_name":"Minimum spanning tree","level":2,"score":0.4869168996810913},{"id":"https://openalex.org/C161840515","wikidata":"https://www.wikidata.org/wiki/Q186131","display_name":"Terrain","level":2,"score":0.4657863676548004},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.42880815267562866},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.36133772134780884},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.13586261868476868},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.07716569304466248},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tgrs.2020.2988982","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2020.2988982","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/G1413607938","display_name":null,"funder_award_id":"61173092","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G1725003993","display_name":null,"funder_award_id":"61621005","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G570056697","display_name":null,"funder_award_id":"61771379","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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":48,"referenced_works":["https://openalex.org/W16073958","https://openalex.org/W1484290300","https://openalex.org/W1588783521","https://openalex.org/W1820232756","https://openalex.org/W1969674753","https://openalex.org/W1972691633","https://openalex.org/W1978792684","https://openalex.org/W2008509935","https://openalex.org/W2017337590","https://openalex.org/W2036043464","https://openalex.org/W2048679005","https://openalex.org/W2052190325","https://openalex.org/W2063837453","https://openalex.org/W2078985447","https://openalex.org/W2097272115","https://openalex.org/W2109334766","https://openalex.org/W2130762895","https://openalex.org/W2133556223","https://openalex.org/W2133989913","https://openalex.org/W2141424348","https://openalex.org/W2142134428","https://openalex.org/W2154830216","https://openalex.org/W2167633585","https://openalex.org/W2228433117","https://openalex.org/W2252304777","https://openalex.org/W2403436963","https://openalex.org/W2471849660","https://openalex.org/W2526081939","https://openalex.org/W2546808853","https://openalex.org/W2604554702","https://openalex.org/W2607257436","https://openalex.org/W2740687828","https://openalex.org/W2743268876","https://openalex.org/W2761781479","https://openalex.org/W2791890028","https://openalex.org/W2797779281","https://openalex.org/W2803796174","https://openalex.org/W2804363015","https://openalex.org/W2963870211","https://openalex.org/W2998216295","https://openalex.org/W4285719527","https://openalex.org/W4390900678","https://openalex.org/W6600607092","https://openalex.org/W6628852233","https://openalex.org/W6635188891","https://openalex.org/W6689204966","https://openalex.org/W6751408282","https://openalex.org/W7066667914"],"related_works":["https://openalex.org/W1992962589","https://openalex.org/W3032871857","https://openalex.org/W1743191351","https://openalex.org/W3104633800","https://openalex.org/W3023567978","https://openalex.org/W3044778482","https://openalex.org/W3040494141","https://openalex.org/W4384026392","https://openalex.org/W4210966920","https://openalex.org/W2550009779"],"abstract_inverted_index":{"In":[0],"this":[1,61],"article,":[2],"the":[3,38,51,75,88,94,99,110,116,120,133,137,149,162],"terrain":[4],"classifications":[5],"of":[6,44,72,74,101,144,159],"polarimetric":[7,45,76],"synthetic":[8],"aperture":[9],"radar":[10],"(PolSAR)":[11],"images":[12],"are":[13,35,48,83],"studied.":[14],"A":[15],"novel":[16],"semi-supervised":[17],"method":[18,139,164],"based":[19],"on":[20],"improved":[21,111],"Tri-training":[22,95],"combined":[23],"with":[24,109,155],"a":[25,41,105,156,166],"neighborhood":[26],"minimum":[27],"spanning":[28],"tree":[29],"(NMST)":[30],"is":[31,58,63,123],"proposed.":[32],"Several":[33],"strategies":[34],"included":[36],"in":[37,93],"method:":[39],"1)":[40],"high-dimensional":[42],"vector":[43,62],"features":[46],"that":[47,113,154],"obtained":[49],"from":[50],"coherency":[52,117],"matrix":[53,118],"and":[54,68,103,119],"diverse":[55],"target":[56],"decompositions":[57],"constructed;":[59],"2)":[60],"divided":[64],"into":[65],"three":[66,81,89],"subvectors":[67,82],"each":[69],"subvector":[70],"consists":[71],"one-third":[73],"features,":[77],"randomly":[78],"selected.":[79],"The":[80],"used":[84],"to":[85,97,125,131,147],"separately":[86],"train":[87],"different":[90],"base":[91],"classifiers":[92],"algorithm":[96],"increase":[98,132],"diversity":[100],"classification;":[102],"3)":[104],"help-training":[106],"sample":[107],"selection":[108],"NMST":[112],"uses":[114],"both":[115],"spatial":[121],"information":[122],"adopted":[124],"select":[126],"highly":[127],"reliable":[128],"unlabeled":[129,145],"samples":[130,146],"training":[134],"sets.":[135],"Thus,":[136],"proposed":[138,163],"can":[140],"effectively":[141],"take":[142],"advantage":[143],"improve":[148],"classification.":[150],"Experimental":[151],"results":[152],"show":[153],"small":[157],"number":[158],"labeled":[160],"samples,":[161],"achieves":[165],"much":[167],"better":[168],"performance":[169],"than":[170],"existing":[171],"classification":[172],"methods.":[173]},"counts_by_year":[{"year":2025,"cited_by_count":8},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":13},{"year":2022,"cited_by_count":12},{"year":2021,"cited_by_count":7}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}