{"id":"https://openalex.org/W2899198451","doi":"https://doi.org/10.1109/tgrs.2018.2873966","title":"Scene Classification Using Hierarchical Wasserstein CNN","display_name":"Scene Classification Using Hierarchical Wasserstein CNN","publication_year":2018,"publication_date":"2018-10-30","ids":{"openalex":"https://openalex.org/W2899198451","doi":"https://doi.org/10.1109/tgrs.2018.2873966","mag":"2899198451"},"language":"en","primary_location":{"id":"doi:10.1109/tgrs.2018.2873966","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2018.2873966","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/A5049177850","display_name":"Yishu Liu","orcid":"https://orcid.org/0000-0003-3586-5197"},"institutions":[{"id":"https://openalex.org/I187400657","display_name":"South China Normal University","ror":"https://ror.org/01kq0pv72","country_code":"CN","type":"education","lineage":["https://openalex.org/I187400657"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yishu Liu","raw_affiliation_strings":["School of Geography, South China Normal University, Guangzhou, China"],"raw_orcid":"https://orcid.org/0000-0003-3586-5197","affiliations":[{"raw_affiliation_string":"School of Geography, South China Normal University, Guangzhou, China","institution_ids":["https://openalex.org/I187400657"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059479627","display_name":"Ching Y. Suen","orcid":"https://orcid.org/0000-0003-1209-7631"},"institutions":[{"id":"https://openalex.org/I60158472","display_name":"Concordia University","ror":"https://ror.org/0420zvk78","country_code":"CA","type":"education","lineage":["https://openalex.org/I60158472"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Ching Y. Suen","raw_affiliation_strings":["Center for Pattern Recognition and Machine Intelligence, Concordia University, Montreal, QC, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Center for Pattern Recognition and Machine Intelligence, Concordia University, Montreal, QC, Canada","institution_ids":["https://openalex.org/I60158472"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100711276","display_name":"Yingbin Liu","orcid":"https://orcid.org/0000-0002-4026-5535"},"institutions":[{"id":"https://openalex.org/I187400657","display_name":"South China Normal University","ror":"https://ror.org/01kq0pv72","country_code":"CN","type":"education","lineage":["https://openalex.org/I187400657"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yingbin Liu","raw_affiliation_strings":["School of Geography, South China Normal University, Guangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Geography, South China Normal University, Guangzhou, China","institution_ids":["https://openalex.org/I187400657"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101536219","display_name":"Liwang Ding","orcid":"https://orcid.org/0000-0003-4361-0389"},"institutions":[{"id":"https://openalex.org/I187400657","display_name":"South China Normal University","ror":"https://ror.org/01kq0pv72","country_code":"CN","type":"education","lineage":["https://openalex.org/I187400657"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liwang Ding","raw_affiliation_strings":["School of Geography, South China Normal University, Guangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Geography, South China Normal University, Guangzhou, China","institution_ids":["https://openalex.org/I187400657"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":2645,"currency":"USD","value_usd":2645},"apc_paid":null,"fwci":5.8969,"has_fulltext":false,"cited_by_count":71,"citation_normalized_percentile":{"value":0.96283096,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":"57","issue":"5","first_page":"2494","last_page":"2509"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9995999932289124,"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":0.9995999932289124,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9980999827384949,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.996399998664856,"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/computer-science","display_name":"Computer science","score":0.7521651983261108},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6886367201805115},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5316569805145264},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4976864159107208},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.4704046845436096},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.4482533931732178},{"id":"https://openalex.org/keywords/tree","display_name":"Tree (set theory)","score":0.4375618100166321},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.4346860349178314},{"id":"https://openalex.org/keywords/wasserstein-metric","display_name":"Wasserstein metric","score":0.4258750081062317},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.34439778327941895},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3391599953174591},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2224760353565216}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7521651983261108},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6886367201805115},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5316569805145264},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4976864159107208},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.4704046845436096},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.4482533931732178},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.4375618100166321},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.4346860349178314},{"id":"https://openalex.org/C2777634741","wikidata":"https://www.wikidata.org/wiki/Q768993","display_name":"Wasserstein metric","level":2,"score":0.4258750081062317},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.34439778327941895},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3391599953174591},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2224760353565216},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tgrs.2018.2873966","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2018.2873966","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":[{"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16","score":0.46000000834465027}],"awards":[{"id":"https://openalex.org/G8656900505","display_name":null,"funder_award_id":"61673184","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":76,"referenced_works":["https://openalex.org/W385466589","https://openalex.org/W562660536","https://openalex.org/W564800084","https://openalex.org/W1557595030","https://openalex.org/W1585160083","https://openalex.org/W1686810756","https://openalex.org/W1851597118","https://openalex.org/W1912954554","https://openalex.org/W1955857676","https://openalex.org/W1960777822","https://openalex.org/W1963882359","https://openalex.org/W1977556410","https://openalex.org/W1992392048","https://openalex.org/W1994166982","https://openalex.org/W1997975403","https://openalex.org/W2006228023","https://openalex.org/W2012514949","https://openalex.org/W2015386604","https://openalex.org/W2046190141","https://openalex.org/W2076845236","https://openalex.org/W2087461551","https://openalex.org/W2097117768","https://openalex.org/W2098676252","https://openalex.org/W2104657103","https://openalex.org/W2106097867","https://openalex.org/W2116339064","https://openalex.org/W2120881610","https://openalex.org/W2121947440","https://openalex.org/W2126885789","https://openalex.org/W2139500441","https://openalex.org/W2143668817","https://openalex.org/W2156036190","https://openalex.org/W2158131535","https://openalex.org/W2158535772","https://openalex.org/W2163605009","https://openalex.org/W2166660500","https://openalex.org/W2171235719","https://openalex.org/W2253590344","https://openalex.org/W2291068538","https://openalex.org/W2515866431","https://openalex.org/W2539033431","https://openalex.org/W2562145081","https://openalex.org/W2592962403","https://openalex.org/W2607558879","https://openalex.org/W2617297862","https://openalex.org/W2620179114","https://openalex.org/W2620429297","https://openalex.org/W2621526417","https://openalex.org/W2727875856","https://openalex.org/W2744582969","https://openalex.org/W2767089292","https://openalex.org/W2770429219","https://openalex.org/W2776984690","https://openalex.org/W2780334404","https://openalex.org/W2782266583","https://openalex.org/W2783165089","https://openalex.org/W2786225488","https://openalex.org/W2787070260","https://openalex.org/W2787112298","https://openalex.org/W2792857687","https://openalex.org/W2807744099","https://openalex.org/W2883982921","https://openalex.org/W2963016882","https://openalex.org/W2963995333","https://openalex.org/W3103856189","https://openalex.org/W3105577662","https://openalex.org/W4235505822","https://openalex.org/W4285719527","https://openalex.org/W4293579865","https://openalex.org/W6615672858","https://openalex.org/W6633511732","https://openalex.org/W6680598497","https://openalex.org/W6682962330","https://openalex.org/W6684191040","https://openalex.org/W6684811420","https://openalex.org/W6738317315"],"related_works":["https://openalex.org/W4295532600","https://openalex.org/W2063823869","https://openalex.org/W2047973478","https://openalex.org/W4293226380","https://openalex.org/W2067569035","https://openalex.org/W2090985514","https://openalex.org/W4287182230","https://openalex.org/W2113666009","https://openalex.org/W3160767070","https://openalex.org/W2885323543"],"abstract_inverted_index":{"In":[0,97,202],"multiclass":[1],"classification,":[2],"convolutional":[3],"neural":[4],"network":[5],"(CNN)":[6],"is":[7,53,168,178,196,211],"generally":[8],"coupled":[9,186],"with":[10,187],"the":[11,18,29,46,49,60,67,79,110,113,149,162,173,208,223],"cross-entropy":[12],"(CE)":[13],"loss,":[14,190],"which":[15,132],"only":[16],"penalizes":[17],"predicted":[19],"probability":[20],"corresponding":[21],"to":[22,58,109,142,198],"a":[23,41,54,71,84,129,181,243],"ground":[24],"truth":[25],"class":[26],"and":[27,92,215,241],"ignores":[28],"interclass":[30,209],"relationship.":[31],"We":[32,138,229],"argue":[33],"that":[34,118,147,160],"CNN":[35,185,194],"can":[36],"be":[37],"improved":[38],"by":[39,104],"using":[40],"better":[42],"loss":[43,145],"function.":[44],"On":[45],"other":[47],"hand,":[48],"Wasserstein":[50,193],"distance":[51,61],"(WD)":[52],"well-known":[55],"metric":[56],"used":[57],"measure":[59],"between":[62],"two":[63,120,234],"distributions.":[64],"Directly":[65],"solving":[66],"WD":[68,111,127,136],"problem":[69],"requires":[70],"prohibitively":[72],"large":[73],"amount":[74],"of":[75,86,151,225],"computation":[76],"time,":[77,115],"whereas":[78],"cheaper":[80],"iterative":[81],"algorithms":[82],"have":[83],"variety":[85],"shortcomings":[87,150],"such":[88],"as":[89,180],"computational":[90],"instability":[91],"difficulty":[93],"in":[94,122,222,246],"selecting":[95],"parameters.":[96],"this":[98,140,155,203],"paper,":[99],"we":[100,116,133],"address":[101],"these":[102],"issues":[103],"giving":[105],"an":[106,188],"analytical":[107],"solution":[108],"problem-for":[112],"first":[114],"find":[117],"for":[119,164],"distributions":[121],"hierarchically":[123],"organized":[124],"data":[125,239],"space,":[126],"has":[128],"closed-form":[130],"solution,":[131],"call":[134],"\u201chierarchical":[135],"(HWD).\u201d":[137],"use":[139],"theory":[141],"construct":[143],"novel":[144],"functions":[146],"overcome":[148],"CE":[152],"loss.":[153],"To":[154],"end,":[156],"multi-CNN":[157],"information":[158,216],"fusion":[159],"provides":[161,220],"basis":[163],"building":[165],"category":[166],"hierarchies":[167],"carried":[169],"out":[170],"first.":[171],"Then,":[172,184],"semantic":[174],"relationship":[175,210],"among":[176],"classes":[177],"modeled":[179],"binary":[182],"tree.":[183],"HWD-based":[189],"i.e.,":[191],"hierarchical":[192],"(HW-CNN),":[195],"trained":[197],"learn":[199],"deep":[200],"features.":[201],"way,":[204],"prior":[205],"knowledge":[206],"about":[207],"embedded":[212],"into":[213],"HW-CNN,":[214],"from":[217],"several":[218],"CNNs":[219],"guidance":[221],"process":[224],"training":[226],"individual":[227],"HW-CNNs.":[228],"conducted":[230],"extensive":[231],"experiments":[232],"over":[233],"publicly":[235],"available":[236],"remote":[237],"sensing":[238],"sets":[240],"achieved":[242],"state-of-the-art":[244],"performance":[245],"scene":[247],"classification":[248],"tasks.":[249]},"counts_by_year":[{"year":2025,"cited_by_count":8},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":11},{"year":2022,"cited_by_count":15},{"year":2021,"cited_by_count":11},{"year":2020,"cited_by_count":19},{"year":2019,"cited_by_count":4},{"year":2018,"cited_by_count":1}],"updated_date":"2026-08-21T09:56:20.448147","created_date":"2025-10-10T00:00:00"}