{"id":"https://openalex.org/W2000563363","doi":"https://doi.org/10.1109/lgrs.2014.2349272","title":"Imbalanced Hyperspectral Image Classification Based on Maximum Margin","display_name":"Imbalanced Hyperspectral Image Classification Based on Maximum Margin","publication_year":2014,"publication_date":"2014-09-08","ids":{"openalex":"https://openalex.org/W2000563363","doi":"https://doi.org/10.1109/lgrs.2014.2349272","mag":"2000563363"},"language":"en","primary_location":{"id":"doi:10.1109/lgrs.2014.2349272","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2014.2349272","pdf_url":null,"source":{"id":"https://openalex.org/S126920919","display_name":"IEEE Geoscience and Remote Sensing Letters","issn_l":"1545-598X","issn":["1545-598X","1558-0571"],"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 Geoscience and Remote Sensing Letters","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/A5106406829","display_name":"Tao Sun","orcid":"https://orcid.org/0000-0002-6618-1081"},"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":true,"raw_author_name":"Tao Sun","raw_affiliation_strings":["Key Laboratory of Intelligent Perception and Image Understanding, Ministry of Education of China, Xidian University, Xi'an, China","[Key Lab. of Intell. Perception & Image Understanding, Xidian Univ., Xi'an, China]"],"affiliations":[{"raw_affiliation_string":"Key Laboratory of Intelligent Perception and Image Understanding, Ministry of Education of China, Xidian University, Xi'an, China","institution_ids":["https://openalex.org/I149594827"]},{"raw_affiliation_string":"[Key Lab. of Intell. Perception & Image Understanding, Xidian Univ., Xi'an, China]","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","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, Ministry of Education of China, Xidian University, Xi'an, China","[Key Lab. of Intell. Perception & Image Understanding, Xidian Univ., Xi'an, China]"],"affiliations":[{"raw_affiliation_string":"Key Laboratory of Intelligent Perception and Image Understanding, Ministry of Education of China, Xidian University, Xi'an, China","institution_ids":["https://openalex.org/I149594827"]},{"raw_affiliation_string":"[Key Lab. of Intell. Perception & Image Understanding, Xidian Univ., Xi'an, China]","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100538868","display_name":"Jie Feng","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":"Jie Feng","raw_affiliation_strings":["Key Laboratory of Intelligent Perception and Image Understanding, Ministry of Education of China, Xidian University, Xi'an, China","[Key Lab. of Intell. Perception & Image Understanding, Xidian Univ., Xi'an, China]"],"affiliations":[{"raw_affiliation_string":"Key Laboratory of Intelligent Perception and Image Understanding, Ministry of Education of China, Xidian University, Xi'an, China","institution_ids":["https://openalex.org/I149594827"]},{"raw_affiliation_string":"[Key Lab. of Intell. Perception & Image Understanding, Xidian Univ., Xi'an, China]","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100453075","display_name":"Fang Liu","orcid":"https://orcid.org/0000-0002-5669-9354"},"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":"Fang Liu","raw_affiliation_strings":["School of Computer Science and Technology, Xidian University, Xi'an, China","(School of Computer Science and Technology, Xidian University, Xi'an, China)"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Xidian University, Xi'an, China","institution_ids":["https://openalex.org/I149594827"]},{"raw_affiliation_string":"(School of Computer Science and Technology, Xidian University, Xi'an, China)","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5049776440","display_name":"Xiangrong Zhang","orcid":"https://orcid.org/0000-0003-0379-2042"},"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":"Xiangrong Zhang","raw_affiliation_strings":["Key Laboratory of Intelligent Perception and Image Understanding, Ministry of Education of China, Xidian University, Xi'an, China","[Key Lab. of Intell. Perception & Image Understanding, Xidian Univ., Xi'an, China]"],"affiliations":[{"raw_affiliation_string":"Key Laboratory of Intelligent Perception and Image Understanding, Ministry of Education of China, Xidian University, Xi'an, China","institution_ids":["https://openalex.org/I149594827"]},{"raw_affiliation_string":"[Key Lab. of Intell. Perception & Image Understanding, Xidian Univ., Xi'an, China]","institution_ids":["https://openalex.org/I149594827"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5106406829"],"corresponding_institution_ids":["https://openalex.org/I149594827"],"apc_list":null,"apc_paid":null,"fwci":3.409,"has_fulltext":false,"cited_by_count":40,"citation_normalized_percentile":{"value":0.92973405,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"12","issue":"3","first_page":"522","last_page":"526"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.995199978351593,"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.995199978351593,"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/T10689","display_name":"Remote-Sensing Image Classification","score":0.9927999973297119,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.9836999773979187,"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/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.8789011240005493},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.7927553653717041},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7433059215545654},{"id":"https://openalex.org/keywords/margin","display_name":"Margin (machine learning)","score":0.7127084732055664},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.704899251461029},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.6861265301704407},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6373136639595032},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.63152015209198},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.6070488691329956},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.5582681894302368},{"id":"https://openalex.org/keywords/land-cover","display_name":"Land cover","score":0.44995787739753723},{"id":"https://openalex.org/keywords/statistical-classification","display_name":"Statistical classification","score":0.41117554903030396},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.36345645785331726},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3401505649089813},{"id":"https://openalex.org/keywords/land-use","display_name":"Land use","score":0.07551079988479614}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.8789011240005493},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.7927553653717041},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7433059215545654},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.7127084732055664},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.704899251461029},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.6861265301704407},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6373136639595032},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.63152015209198},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.6070488691329956},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.5582681894302368},{"id":"https://openalex.org/C2780648208","wikidata":"https://www.wikidata.org/wiki/Q3001793","display_name":"Land cover","level":3,"score":0.44995787739753723},{"id":"https://openalex.org/C110083411","wikidata":"https://www.wikidata.org/wiki/Q1744628","display_name":"Statistical classification","level":2,"score":0.41117554903030396},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.36345645785331726},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3401505649089813},{"id":"https://openalex.org/C4792198","wikidata":"https://www.wikidata.org/wiki/Q1165944","display_name":"Land use","level":2,"score":0.07551079988479614},{"id":"https://openalex.org/C147176958","wikidata":"https://www.wikidata.org/wiki/Q77590","display_name":"Civil engineering","level":1,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lgrs.2014.2349272","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2014.2349272","pdf_url":null,"source":{"id":"https://openalex.org/S126920919","display_name":"IEEE Geoscience and Remote Sensing Letters","issn_l":"1545-598X","issn":["1545-598X","1558-0571"],"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 Geoscience and Remote Sensing Letters","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/15","display_name":"Life in Land","score":0.4300000071525574}],"awards":[{"id":"https://openalex.org/G1138436293","display_name":null,"funder_award_id":"NCET-10-0668","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3738235899","display_name":null,"funder_award_id":"61072108","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":21,"referenced_works":["https://openalex.org/W85350352","https://openalex.org/W155526034","https://openalex.org/W1915109052","https://openalex.org/W1987859285","https://openalex.org/W2010789233","https://openalex.org/W2043945532","https://openalex.org/W2067983477","https://openalex.org/W2083911205","https://openalex.org/W2103614420","https://openalex.org/W2119191234","https://openalex.org/W2128965734","https://openalex.org/W2131864940","https://openalex.org/W2136251662","https://openalex.org/W2148143831","https://openalex.org/W2162698522","https://openalex.org/W2170014674","https://openalex.org/W2979832949","https://openalex.org/W4300420063","https://openalex.org/W6603460400","https://openalex.org/W6640140148","https://openalex.org/W6684821008"],"related_works":["https://openalex.org/W2249734142","https://openalex.org/W3043252291","https://openalex.org/W2041636156","https://openalex.org/W102514241","https://openalex.org/W2271716409","https://openalex.org/W1984878695","https://openalex.org/W2765346732","https://openalex.org/W2000563363","https://openalex.org/W4310989423","https://openalex.org/W4377230297"],"abstract_inverted_index":{"Hyperspectral":[0],"remote":[1],"sensing":[2],"images":[3],"own":[4],"rich":[5],"spectral":[6],"information":[7],"to":[8,42,47,116,133],"distinguish":[9],"different":[10,73],"land-cover":[11],"classes.":[12,28],"Sometimes,":[13],"it":[14],"may":[15],"encounter":[16],"the":[17,45,48,69,79,91,101,118,122,125,128,135,154],"case":[18],"that":[19,146],"some":[20],"classes":[21,49,71],"have":[22],"much":[23],"fewer":[24],"pixels":[25,46],"than":[26,153],"other":[27],"In":[29,98],"this":[30,99],"case,":[31],"traditional":[32],"classification":[33,119,151,160],"methods":[34,161],"are":[35,40,83],"not":[36,89],"appropriate":[37],"because":[38],"they":[39],"prone":[41],"assign":[43],"all":[44],"with":[50,75],"a":[51,64,95,105],"large":[52],"number":[53],"of":[54,85,124],"pixels.":[55],"For":[56],"such":[57],"an":[58,113],"imbalanced":[59,140,159],"problem,":[60],"ensemble":[61,81,114,136],"learning":[62,137],"is":[63,110,131],"good":[65],"method":[66,148],"by":[67],"partitioning":[68],"majority":[70],"into":[72],"groups":[74],"small":[76],"sizes.":[77],"However,":[78],"existing":[80],"schemes":[82],"independent":[84],"classifiers,":[86],"which":[87],"will":[88],"get":[90],"best":[92],"performance":[93],"for":[94,139,162],"certain":[96],"classifier.":[97],"letter,":[100],"selected":[102],"classifier,":[103],"i.e.,":[104,127],"support":[106],"vector":[107],"machine":[108],"(SVM),":[109],"considered":[111],"in":[112],"procedure":[115,138],"improve":[117],"accuracy.":[120],"Specifically,":[121],"criterion":[123],"SVM,":[126],"maximum":[129],"margin,":[130],"adopted":[132],"guide":[134],"hyperspectral":[141,163],"image":[142],"classification.":[143],"Experiments":[144],"state":[145],"our":[147],"obtains":[149],"higher":[150],"accuracy":[152],"SVM":[155],"and":[156],"several":[157],"representative":[158],"images.":[164]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":5},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":5},{"year":2018,"cited_by_count":7},{"year":2017,"cited_by_count":4},{"year":2016,"cited_by_count":3},{"year":2015,"cited_by_count":1}],"updated_date":"2026-04-01T17:29:45.350535","created_date":"2025-10-10T00:00:00"}
