{"id":"https://openalex.org/W4415707016","doi":"https://doi.org/10.1109/lgrs.2025.3626867","title":"Robust Optimal Transport With Exact Marginal Relaxation for Remote Sensing Scene Classification","display_name":"Robust Optimal Transport With Exact Marginal Relaxation for Remote Sensing Scene Classification","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4415707016","doi":"https://doi.org/10.1109/lgrs.2025.3626867"},"language":null,"primary_location":{"id":"doi:10.1109/lgrs.2025.3626867","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2025.3626867","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/A5102022088","display_name":"Shikun Chen","orcid":"https://orcid.org/0000-0002-0242-3133"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shikun Chen","raw_affiliation_strings":["Department of Electronic Engineering, Tsinghua University, Beijing, China","Department of Electronic Engineering, Tsinghua University, Beijing, P. R. China"],"raw_orcid":"https://orcid.org/0000-0002-0242-3133","affiliations":[{"raw_affiliation_string":"Department of Electronic Engineering, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]},{"raw_affiliation_string":"Department of Electronic Engineering, Tsinghua University, Beijing, P. R. China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100664416","display_name":"Chao Liu","orcid":"https://orcid.org/0000-0003-0696-3943"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chao Liu","raw_affiliation_strings":["Department of Electronic Engineering, Tsinghua University, Beijing, China","Department of Electronic Engineering, Tsinghua University, Beijing, P. R. China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electronic Engineering, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]},{"raw_affiliation_string":"Department of Electronic Engineering, Tsinghua University, Beijing, P. R. China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5107061611","display_name":"Jian Yang","orcid":"https://orcid.org/0000-0002-0036-9233"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jian Yang","raw_affiliation_strings":["Department of Electronic Engineering, Tsinghua University, Beijing, China","Department of Electronic Engineering, Tsinghua University, Beijing, P. R. China"],"raw_orcid":"https://orcid.org/0000-0002-0036-9233","affiliations":[{"raw_affiliation_string":"Department of Electronic Engineering, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]},{"raw_affiliation_string":"Department of Electronic Engineering, Tsinghua University, Beijing, P. R. China","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I99065089"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.39843446,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"22","issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.527999997138977,"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":0.527999997138977,"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/T13282","display_name":"Automated Road and Building Extraction","score":0.1867000013589859,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean 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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.04479999840259552,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6528000235557556},{"id":"https://openalex.org/keywords/relaxation","display_name":"Relaxation (psychology)","score":0.6273999810218811},{"id":"https://openalex.org/keywords/divergence","display_name":"Divergence (linguistics)","score":0.5716000199317932},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.536899983882904},{"id":"https://openalex.org/keywords/marginal-distribution","display_name":"Marginal distribution","score":0.5095000267028809},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.4449000060558319},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.44269999861717224},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.3756999969482422}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7257999777793884},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6528000235557556},{"id":"https://openalex.org/C2776029896","wikidata":"https://www.wikidata.org/wiki/Q3935810","display_name":"Relaxation (psychology)","level":2,"score":0.6273999810218811},{"id":"https://openalex.org/C207390915","wikidata":"https://www.wikidata.org/wiki/Q1230525","display_name":"Divergence (linguistics)","level":2,"score":0.5716000199317932},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.536899983882904},{"id":"https://openalex.org/C165216359","wikidata":"https://www.wikidata.org/wiki/Q670653","display_name":"Marginal distribution","level":3,"score":0.5095000267028809},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.49459999799728394},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.4449000060558319},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.44269999861717224},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4101000130176544},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.39419999718666077},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3928000032901764},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.38999998569488525},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.3756999969482422},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.36640000343322754},{"id":"https://openalex.org/C2780554755","wikidata":"https://www.wikidata.org/wiki/Q955260","display_name":"Relaxation technique","level":3,"score":0.3508000075817108},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.3499999940395355},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.31929999589920044},{"id":"https://openalex.org/C66887028","wikidata":"https://www.wikidata.org/wiki/Q382444","display_name":"Marginal cost","level":2,"score":0.3188000023365021},{"id":"https://openalex.org/C138551086","wikidata":"https://www.wikidata.org/wiki/Q842271","display_name":"Marginal utility","level":2,"score":0.31839999556541443},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3109000027179718},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3077000081539154},{"id":"https://openalex.org/C171752962","wikidata":"https://www.wikidata.org/wiki/Q255166","display_name":"Kullback\u2013Leibler divergence","level":2,"score":0.305400013923645},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.28940001130104065},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.28200000524520874},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.26910001039505005}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lgrs.2025.3626867","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2025.3626867","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":[],"awards":[{"id":"https://openalex.org/G4586786464","display_name":null,"funder_award_id":"62171023","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5655009983","display_name":null,"funder_award_id":"62222102","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":10,"referenced_works":["https://openalex.org/W1980038761","https://openalex.org/W2515866431","https://openalex.org/W2592962403","https://openalex.org/W4384521492","https://openalex.org/W4386066477","https://openalex.org/W4389104766","https://openalex.org/W4391974596","https://openalex.org/W4393305465","https://openalex.org/W4400680992","https://openalex.org/W4407625892"],"related_works":[],"abstract_inverted_index":{"Significant":[0],"efforts":[1],"have":[2],"been":[3,68],"made":[4],"to":[5,43,70,93,124,166,187,199],"develop":[6],"techniques":[7,33],"for":[8,192],"extracting":[9],"useful":[10],"information":[11],"from":[12,89],"remote":[13,56],"sensing":[14,57],"(RS)":[15],"images":[16],"in":[17,134],"view":[18],"of":[19,120,139,144,161,169],"their":[20],"favorable":[21],"properties":[22],"such":[23],"as":[24],"wide":[25],"coverage,":[26],"good":[27],"timeliness":[28],"and":[29,51,97,195],"large":[30,48],"content.":[31],"Such":[32],"often":[34],"involve":[35],"scene":[36,58],"classification,":[37],"which":[38,127],"provides":[39],"an":[40],"effective":[41],"way":[42],"interpret":[44],"RS":[45],"data.":[46],"However,":[47,158],"intra-class":[49],"variances":[50],"high":[52],"inter-class":[53],"similarities":[54],"make":[55],"classification":[59,78],"(RSSC)":[60],"a":[61,99,177],"tough":[62],"task.":[63],"Recently,":[64],"deep":[65],"learning":[66],"has":[67],"brought":[69],"bear":[71],"upon":[72],"RSSC.":[73,200],"Deep":[74],"models":[75],"with":[76],"ever-improving":[77],"accuracy":[79],"are":[80,155],"continually":[81],"being":[82],"put":[83],"forward.":[84],"This":[85],"letter":[86],"shifts":[87],"focus":[88],"network":[90],"structure":[91],"optimization":[92],"the":[94,109,117,125,159,167,189,205,213],"loss":[95],"function":[96],"takes":[98],"principled":[100],"approach":[101],"based":[102],"on":[103,212],"optimal":[104,184,190],"transport":[105,185],"(OT).":[106],"OT":[107,145,162],"measures":[108],"divergence":[110],"between":[111],"two":[112],"probability":[113],"distributions":[114],"by":[115],"computing":[116],"minimum":[118],"cost":[119],"converting":[121],"one":[122],"distribution":[123],"other,":[126],"can":[128,147],"help":[129],"discriminate":[130],"different":[131],"data":[132],"clusters":[133],"high-dimensional":[135],"feature":[136],"spaces.":[137],"Relaxation":[138],"marginal":[140,170,181,193],"constraints":[141],"during":[142],"computation":[143],"objective":[146,163],"accommodate":[148],"certain":[149],"potentially":[150],"desirable":[151],"transportation":[152],"plans":[153],"that":[154,204],"otherwise":[156],"inadmissible.":[157],"effect":[160],"is":[164],"sensitive":[165],"extent":[168],"relaxation.":[171],"In":[172],"this":[173],"work,":[174],"we":[175],"propose":[176],"method,":[178],"named":[179],"exact":[180],"relaxation":[182,194],"robust":[183],"(EMRROT),":[186],"estimate":[188],"threshold":[191],"investigate":[196],"its":[197],"application":[198],"Experimental":[201],"results":[202],"show":[203],"proposed":[206],"EMRROT":[207],"achieves":[208],"highly":[209],"competitive":[210],"performances":[211],"benchmark":[214],"RSSC":[215],"datasets.":[216]},"counts_by_year":[],"updated_date":"2025-11-08T23:21:52.890332","created_date":"2025-10-30T00:00:00"}
