{"id":"https://openalex.org/W2985701536","doi":"https://doi.org/10.1109/igarss.2019.8898415","title":"Ensemble Margin Based Semi-Supervised Random Forest for the Classification of Hyperspectral Image with Limited Training Data","display_name":"Ensemble Margin Based Semi-Supervised Random Forest for the Classification of Hyperspectral Image with Limited Training Data","publication_year":2019,"publication_date":"2019-07-01","ids":{"openalex":"https://openalex.org/W2985701536","doi":"https://doi.org/10.1109/igarss.2019.8898415","mag":"2985701536"},"language":"en","primary_location":{"id":"doi:10.1109/igarss.2019.8898415","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2019.8898415","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-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/A5068336830","display_name":"Wei Feng","orcid":"https://orcid.org/0000-0003-1907-2664"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"funder","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210128053","display_name":"Institute of Remote Sensing and Digital Earth","ror":"https://ror.org/02cjszf03","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210128053"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Wei Feng","raw_affiliation_strings":["Key laboratory of Digital Earth Science, Chinese Academy of Sciences, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Key laboratory of Digital Earth Science, Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210128053","https://openalex.org/I19820366"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5109619264","display_name":"Wenjiang Huang","orcid":"https://orcid.org/0009-0009-3343-7034"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"funder","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210128053","display_name":"Institute of Remote Sensing and Digital Earth","ror":"https://ror.org/02cjszf03","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210128053"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenjiang Huang","raw_affiliation_strings":["Key laboratory of Digital Earth Science, Chinese Academy of Sciences, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Key laboratory of Digital Earth Science, Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210128053","https://openalex.org/I19820366"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044570096","display_name":"Gabriel Dauphin","orcid":"https://orcid.org/0000-0002-0677-6702"},"institutions":[{"id":"https://openalex.org/I4210091279","display_name":"Universit\u00e9 Sorbonne Paris Nord","ror":"https://ror.org/0199hds37","country_code":"FR","type":"education","lineage":["https://openalex.org/I4210091279"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Gabriel Dauphin","raw_affiliation_strings":["Laboratory of Information Processing and Transmission, University Paris XIII, France"],"affiliations":[{"raw_affiliation_string":"Laboratory of Information Processing and Transmission, University Paris XIII, France","institution_ids":["https://openalex.org/I4210091279"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5000395252","display_name":"Junshi Xia","orcid":"https://orcid.org/0000-0002-5586-6536"},"institutions":[{"id":"https://openalex.org/I4210126580","display_name":"RIKEN Center for Advanced Intelligence Project","ror":"https://ror.org/03ckxwf91","country_code":"JP","type":"facility","lineage":["https://openalex.org/I4210110652","https://openalex.org/I4210126580"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Junshi Xia","raw_affiliation_strings":["Geoinformatics Unit, RIKEN Center for Advanced Intelligence Project, Tokyo, Japan"],"affiliations":[{"raw_affiliation_string":"Geoinformatics Unit, RIKEN Center for Advanced Intelligence Project, Tokyo, Japan","institution_ids":["https://openalex.org/I4210126580"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008256662","display_name":"Yinghui Quan","orcid":"https://orcid.org/0000-0001-6541-9441"},"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":"Yinghui Quan","raw_affiliation_strings":["Key Laboratory for Radar Signal Processing, Xidian University, Shaanxi, China"],"affiliations":[{"raw_affiliation_string":"Key Laboratory for Radar Signal Processing, Xidian University, Shaanxi, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100780503","display_name":"Huichun Ye","orcid":"https://orcid.org/0000-0001-7836-497X"},"institutions":[{"id":"https://openalex.org/I4210128053","display_name":"Institute of Remote Sensing and Digital Earth","ror":"https://ror.org/02cjszf03","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210128053"]},{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"funder","lineage":["https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Huichun Ye","raw_affiliation_strings":["Key laboratory of Digital Earth Science, Chinese Academy of Sciences, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Key laboratory of Digital Earth Science, Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210128053","https://openalex.org/I19820366"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5057149005","display_name":"Yingying Dong","orcid":"https://orcid.org/0000-0002-2865-5020"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"funder","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210128053","display_name":"Institute of Remote Sensing and Digital Earth","ror":"https://ror.org/02cjszf03","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210128053"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yingying Dong","raw_affiliation_strings":["Key laboratory of Digital Earth Science, Chinese Academy of Sciences, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Key laboratory of Digital Earth Science, Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210128053","https://openalex.org/I19820366"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5068336830"],"corresponding_institution_ids":["https://openalex.org/I19820366","https://openalex.org/I4210128053"],"apc_list":null,"apc_paid":null,"fwci":1.6069,"has_fulltext":false,"cited_by_count":13,"citation_normalized_percentile":{"value":0.86321925,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"971","last_page":"974"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9997000098228455,"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":0.9997000098228455,"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/T10057","display_name":"Face and Expression Recognition","score":0.9894000291824341,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T13890","display_name":"Remote Sensing and Land Use","score":0.9853000044822693,"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/margin","display_name":"Margin (machine learning)","score":0.8755173683166504},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.7846473455429077},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.7128753662109375},{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.6985548138618469},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6814196109771729},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6804857850074768},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6677389144897461},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.5238309502601624},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5150423049926758},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.48322829604148865},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4767273962497711},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.437813401222229},{"id":"https://openalex.org/keywords/co-training","display_name":"Co-training","score":0.43478095531463623},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.4256803095340729},{"id":"https://openalex.org/keywords/semi-supervised-learning","display_name":"Semi-supervised learning","score":0.2890419661998749}],"concepts":[{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.8755173683166504},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.7846473455429077},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.7128753662109375},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.6985548138618469},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6814196109771729},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6804857850074768},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6677389144897461},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.5238309502601624},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5150423049926758},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.48322829604148865},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4767273962497711},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.437813401222229},{"id":"https://openalex.org/C2776959682","wikidata":"https://www.wikidata.org/wiki/Q17005296","display_name":"Co-training","level":3,"score":0.43478095531463623},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.4256803095340729},{"id":"https://openalex.org/C58973888","wikidata":"https://www.wikidata.org/wiki/Q1041418","display_name":"Semi-supervised learning","level":2,"score":0.2890419661998749},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","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/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss.2019.8898415","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2019.8898415","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/15","display_name":"Life in Land","score":0.7099999785423279}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":9,"referenced_works":["https://openalex.org/W1974614303","https://openalex.org/W2292982615","https://openalex.org/W2310649008","https://openalex.org/W2522698497","https://openalex.org/W2525592260","https://openalex.org/W2589780155","https://openalex.org/W2761430711","https://openalex.org/W2803590506","https://openalex.org/W2911964244"],"related_works":["https://openalex.org/W2251208230","https://openalex.org/W2381566779","https://openalex.org/W2098708659","https://openalex.org/W264117909","https://openalex.org/W2215421138","https://openalex.org/W2797776314","https://openalex.org/W4303683898","https://openalex.org/W1505796919","https://openalex.org/W81045758","https://openalex.org/W277867124"],"abstract_inverted_index":{"In":[0],"this":[1,70],"paper,":[2],"we":[3],"propose":[4],"a":[5,59],"novel":[6],"ensemble":[7,36,74],"margin":[8,67,75],"based":[9],"semi-supervised":[10],"random":[11],"forest":[12],"(EMRF)":[13],"algorithm":[14],"for":[15],"the":[16,19,32,35,41,52,65,79,82,91,95],"classification":[17,46,56],"of":[18,34,58,69,76],"hyperspectral":[20],"image":[21],"with":[22,44],"limited":[23],"training":[24,53,60,92],"data.":[25],"The":[26,55,72],"proposed":[27],"method":[28],"tries":[29],"to":[30,90],"improve":[31],"effectiveness":[33],"model":[37],"via":[38],"adaptively":[39],"labeling":[40],"unlabeled":[42],"instances":[43],"high":[45],"probability":[47,57,81],"then":[48],"adding":[49],"them":[50],"into":[51,89],"set.":[54],"instance":[61,83],"is":[62],"reflected":[63],"by":[64],"unsupervised":[66],"value":[68],"instance.":[71],"higher":[73,80],"an":[77],"instance,":[78],"being":[84],"classified":[85],"correctly":[86],"and":[87],"added":[88],"set":[93],"in":[94],"next":[96],"iteration.":[97]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":4}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
