{"id":"https://openalex.org/W3118358609","doi":"https://doi.org/10.3390/rs13010119","title":"Combining Deep Semantic Segmentation Network and Graph Convolutional Neural Network for Semantic Segmentation of Remote Sensing Imagery","display_name":"Combining Deep Semantic Segmentation Network and Graph Convolutional Neural Network for Semantic Segmentation of Remote Sensing Imagery","publication_year":2020,"publication_date":"2020-12-31","ids":{"openalex":"https://openalex.org/W3118358609","doi":"https://doi.org/10.3390/rs13010119","mag":"3118358609"},"language":"en","primary_location":{"id":"doi:10.3390/rs13010119","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs13010119","pdf_url":"https://www.mdpi.com/2072-4292/13/1/119/pdf?version=1609493061","source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Remote Sensing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2072-4292/13/1/119/pdf?version=1609493061","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5043629817","display_name":"Song Ouyang","orcid":"https://orcid.org/0000-0002-2234-1792"},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Song Ouyang","raw_affiliation_strings":["School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China"],"affiliations":[{"raw_affiliation_string":"School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China","institution_ids":["https://openalex.org/I37461747"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100606147","display_name":"Yansheng Li","orcid":"https://orcid.org/0000-0001-8203-1246"},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yansheng Li","raw_affiliation_strings":["School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China"],"affiliations":[{"raw_affiliation_string":"School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China","institution_ids":["https://openalex.org/I37461747"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5100606147"],"corresponding_institution_ids":["https://openalex.org/I37461747"],"apc_list":{"value":2500,"currency":"CHF","value_usd":2707},"apc_paid":{"value":2500,"currency":"CHF","value_usd":2707},"fwci":9.5586,"has_fulltext":false,"cited_by_count":80,"citation_normalized_percentile":{"value":0.98135314,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":100},"biblio":{"volume":"13","issue":"1","first_page":"119","last_page":"119"},"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9987999796867371,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.998199999332428,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7907096147537231},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6890800595283508},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6433660387992859},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6104380488395691},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.582633912563324},{"id":"https://openalex.org/keywords/pooling","display_name":"Pooling","score":0.5335261225700378},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5224986672401428},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.4552430808544159},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.45044389367103577},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.42756032943725586},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.41464561223983765},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.1325455605983734},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.07861512899398804}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7907096147537231},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6890800595283508},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6433660387992859},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6104380488395691},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.582633912563324},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.5335261225700378},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5224986672401428},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.4552430808544159},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.45044389367103577},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.42756032943725586},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.41464561223983765},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.1325455605983734},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.07861512899398804},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.3390/rs13010119","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs13010119","pdf_url":"https://www.mdpi.com/2072-4292/13/1/119/pdf?version=1609493061","source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Remote Sensing","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:5c7f1306d8334b7caef4e294128c088f","is_oa":true,"landing_page_url":"https://doaj.org/article/5c7f1306d8334b7caef4e294128c088f","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Remote Sensing, Vol 13, Iss 1, p 119 (2020)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/2072-4292/13/1/119/","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/rs13010119","pdf_url":null,"source":{"id":"https://openalex.org/S4306400947","display_name":"MDPI (MDPI AG)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210097602","host_organization_name":"Multidisciplinary Digital Publishing Institute (Switzerland)","host_organization_lineage":["https://openalex.org/I4210097602"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Remote Sensing; Volume 13; Issue 1; Pages: 119","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/rs13010119","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs13010119","pdf_url":"https://www.mdpi.com/2072-4292/13/1/119/pdf?version=1609493061","source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Remote Sensing","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11","score":0.6299999952316284}],"awards":[{"id":"https://openalex.org/G220218006","display_name":null,"funder_award_id":"2016M590716 and 2017T100581","funder_id":"https://openalex.org/F4320321543","funder_display_name":"China Postdoctoral Science Foundation"},{"id":"https://openalex.org/G2708589882","display_name":null,"funder_award_id":"41971284","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"},{"id":"https://openalex.org/F4320321543","display_name":"China Postdoctoral Science Foundation","ror":"https://ror.org/0426zh255"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3118358609.pdf","grobid_xml":"https://content.openalex.org/works/W3118358609.grobid-xml"},"referenced_works_count":60,"referenced_works":["https://openalex.org/W1745334888","https://openalex.org/W1901129140","https://openalex.org/W1903029394","https://openalex.org/W1910657905","https://openalex.org/W2008847349","https://openalex.org/W2116341502","https://openalex.org/W2118246710","https://openalex.org/W2194775991","https://openalex.org/W2241460726","https://openalex.org/W2274609130","https://openalex.org/W2406128552","https://openalex.org/W2412782625","https://openalex.org/W2469938794","https://openalex.org/W2480078828","https://openalex.org/W2519887557","https://openalex.org/W2527276685","https://openalex.org/W2548390752","https://openalex.org/W2560023338","https://openalex.org/W2563705555","https://openalex.org/W2603601739","https://openalex.org/W2616727008","https://openalex.org/W2618530766","https://openalex.org/W2751694392","https://openalex.org/W2752782242","https://openalex.org/W2763822693","https://openalex.org/W2764034829","https://openalex.org/W2782522152","https://openalex.org/W2799215407","https://openalex.org/W2799924016","https://openalex.org/W2804199516","https://openalex.org/W2804860796","https://openalex.org/W2807974043","https://openalex.org/W2808376087","https://openalex.org/W2884585870","https://openalex.org/W2889835342","https://openalex.org/W2892702037","https://openalex.org/W2915971115","https://openalex.org/W2919115771","https://openalex.org/W2923554407","https://openalex.org/W2940726923","https://openalex.org/W2954300568","https://openalex.org/W2963150697","https://openalex.org/W2963659230","https://openalex.org/W2963794428","https://openalex.org/W2964309882","https://openalex.org/W2971452902","https://openalex.org/W2987440397","https://openalex.org/W2990045899","https://openalex.org/W2995766874","https://openalex.org/W3021074965","https://openalex.org/W3025926153","https://openalex.org/W3080368312","https://openalex.org/W3085102703","https://openalex.org/W3091553753","https://openalex.org/W3093905312","https://openalex.org/W3104341624","https://openalex.org/W3125274220","https://openalex.org/W3131872868","https://openalex.org/W6630044934","https://openalex.org/W6750469568"],"related_works":["https://openalex.org/W2953234277","https://openalex.org/W2626256601","https://openalex.org/W147410782","https://openalex.org/W2900413183","https://openalex.org/W4390975304","https://openalex.org/W3022252430","https://openalex.org/W4287804464","https://openalex.org/W3103989898","https://openalex.org/W4401096132","https://openalex.org/W2990636717"],"abstract_inverted_index":{"Although":[0],"the":[1,24,43,68,76,83,94,112,123,138,143,149,159,162,169,176,184,189,193,210,217,222,232,235,252,268,272,276,282,287],"deep":[2,32],"semantic":[3,16,108],"segmentation":[4],"network":[5,87,127],"(DSSN)":[6],"has":[7,51],"been":[8],"widely":[9],"used":[10],"in":[11,102,251],"remote":[12],"sensing":[13],"(RS)":[14],"image":[15,107],"segmentation,":[17],"it":[18],"still":[19],"does":[20],"not":[21],"fully":[22],"mind":[23],"spatial":[25,44,173,198,213],"relationship":[26,79,214],"cues":[27],"between":[28,46,200],"objects":[29,47,238],"when":[30,278],"extracting":[31],"visual":[33],"features":[34,186,196],"through":[35],"convolutional":[36,85],"filters":[37],"and":[38,75,98,137,161,172,197,219,245,275],"pooling":[39],"layers.":[40],"In":[41,65,225],"fact,":[42],"distribution":[45],"from":[48,228],"different":[49],"classes":[50],"a":[52,89,119,242],"strong":[53,69],"correlation":[54],"characteristic.":[55],"For":[56],"example,":[57],"buildings":[58],"tend":[59],"to":[60,63,133,141,182,187,202,241],"be":[61],"close":[62],"roads.":[64],"view":[66],"of":[67,73,82,96,175,212,221,237,289],"appearance":[70,113],"extraction":[71,114],"ability":[72],"DSSN":[74,97,121,273],"powerful":[77],"topological":[78],"modeling":[80,229],"capability":[81],"graph":[84,150,154,163,190],"neural":[86],"(GCN),":[88],"DSSN-GCN":[90,265,277],"framework,":[91],"which":[92,129,285],"combines":[93,195],"advantages":[95],"GCN,":[99,148],"is":[100,151,165,180,206],"proposed":[101],"this":[103,116],"paper":[104,117],"for":[105],"RS":[106],"segmentation.":[109],"To":[110],"lift":[111],"ability,":[115],"proposes":[118],"new":[120],"called":[122],"attention":[124,139],"residual":[125,131],"U-shaped":[126],"(AttResUNet),":[128],"leverages":[130],"blocks":[132],"encode":[134],"feature":[135],"maps":[136],"module":[140],"refine":[142],"features.":[144],"As":[145],"far":[146],"as":[147],"built,":[152],"where":[153],"nodes":[155,201],"are":[156,239,247],"denoted":[157],"by":[158,167],"superpixels":[160],"weight":[164],"calculated":[166],"considering":[168],"spectral":[170],"information":[171,174],"nodes.":[177,191],"The":[178],"AttResUNet":[179,280],"trained":[181],"extract":[183],"high-level":[185],"initialize":[188],"Then":[192],"GCN":[194,230],"relationships":[199],"conduct":[203],"classification.":[204],"It":[205],"worth":[207],"noting":[208],"that":[209,264],"usage":[211],"knowledge":[215],"boosts":[216],"performance":[218],"robustness":[220],"classification":[223,254],"module.":[224],"addition,":[226],"benefiting":[227],"on":[231,258],"superpixel":[233],"level,":[234],"boundaries":[236],"restored":[240],"certain":[243],"extent":[244],"there":[246],"less":[248],"pixel-level":[249],"noises":[250],"final":[253],"result.":[255],"Extensive":[256],"experiments":[257],"two":[259],"publicly":[260],"open":[261],"datasets":[262],"show":[263],"model":[266],"outperforms":[267],"competitive":[269],"baseline":[270],"(i.e.,":[271],"model)":[274],"adopting":[279],"achieves":[281],"best":[283],"performance,":[284],"demonstrates":[286],"advance":[288],"our":[290],"method.":[291]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":17},{"year":2024,"cited_by_count":12},{"year":2023,"cited_by_count":17},{"year":2022,"cited_by_count":21},{"year":2021,"cited_by_count":12}],"updated_date":"2026-04-01T17:29:45.350535","created_date":"2025-10-10T00:00:00"}