{"id":"https://openalex.org/W3013487364","doi":"https://doi.org/10.1145/3373509.3373535","title":"Accurate Semantic Segmentation in Remote Sensing Image","display_name":"Accurate Semantic Segmentation in Remote Sensing Image","publication_year":2019,"publication_date":"2019-10-23","ids":{"openalex":"https://openalex.org/W3013487364","doi":"https://doi.org/10.1145/3373509.3373535","mag":"3013487364"},"language":"en","primary_location":{"id":"doi:10.1145/3373509.3373535","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3373509.3373535","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2019 8th International Conference on Computing and Pattern Recognition","raw_type":"proceedings-article"},"type":"conference-paper","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/A5100439217","display_name":"Shuqi Wang","orcid":"https://orcid.org/0009-0009-3605-9331"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shuqi Wang","raw_affiliation_strings":["BUPT, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"BUPT, Beijing, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100634906","display_name":"Chuang Zhang","orcid":"https://orcid.org/0000-0001-6685-7048"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chuang Zhang","raw_affiliation_strings":["BUPT, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"BUPT, Beijing, China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5077902436","display_name":"Ming Wu","orcid":"https://orcid.org/0000-0001-8390-5398"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ming Wu","raw_affiliation_strings":["BUPT, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"BUPT, Beijing, China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"173","last_page":"178"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13282","display_name":"Automated Road and Building Extraction","score":0.9997000098228455,"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"}},"topics":[{"id":"https://openalex.org/T13282","display_name":"Automated Road and Building Extraction","score":0.9997000098228455,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9993000030517578,"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/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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8355221748352051},{"id":"https://openalex.org/keywords/pyramid","display_name":"Pyramid (geometry)","score":0.7186610698699951},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6742323040962219},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.644484281539917},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5199238657951355},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.5083341002464294},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4861404597759247},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4402436316013336},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.43908628821372986},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4372374713420868},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.4231458306312561},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.421467661857605},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.4209182560443878},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.41987162828445435},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.41606059670448303},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.41402170062065125},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.364687442779541},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.2228335738182068},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.08204725384712219}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8355221748352051},{"id":"https://openalex.org/C142575187","wikidata":"https://www.wikidata.org/wiki/Q3358290","display_name":"Pyramid (geometry)","level":2,"score":0.7186610698699951},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6742323040962219},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.644484281539917},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5199238657951355},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.5083341002464294},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4861404597759247},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4402436316013336},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.43908628821372986},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4372374713420868},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.4231458306312561},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.421467661857605},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.4209182560443878},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.41987162828445435},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.41606059670448303},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.41402170062065125},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.364687442779541},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2228335738182068},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.08204725384712219},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","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/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3373509.3373535","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3373509.3373535","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2019 8th International Conference on Computing and Pattern Recognition","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities","score":0.5099999904632568}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W1903029394","https://openalex.org/W2031489346","https://openalex.org/W2179352600","https://openalex.org/W2194775991","https://openalex.org/W2286929393","https://openalex.org/W2340897893","https://openalex.org/W2412782625","https://openalex.org/W2416190443","https://openalex.org/W2480078828","https://openalex.org/W2560023338","https://openalex.org/W2623331213","https://openalex.org/W2771404974","https://openalex.org/W2774320778","https://openalex.org/W2787091153","https://openalex.org/W2799166040","https://openalex.org/W2806581075","https://openalex.org/W2837491902","https://openalex.org/W2900570535","https://openalex.org/W2902166991","https://openalex.org/W2963108253","https://openalex.org/W2963153291","https://openalex.org/W2963727650","https://openalex.org/W2963881378","https://openalex.org/W2997148986","https://openalex.org/W3105636206"],"related_works":["https://openalex.org/W4390516098","https://openalex.org/W4249847449","https://openalex.org/W2181948922","https://openalex.org/W2384362569","https://openalex.org/W44395729","https://openalex.org/W2142795561","https://openalex.org/W4205302943","https://openalex.org/W2561132942","https://openalex.org/W2765338038","https://openalex.org/W3155418658"],"abstract_inverted_index":{"Thanks":[0],"to":[1,135,170],"recent":[2],"development":[3],"in":[4,14,23,42,62,172],"CNNs":[5],"and":[6,30,58,88,94,119,149],"deep":[7],"learning,":[8],"solid":[9],"improvements":[10],"have":[11,73],"been":[12],"made":[13],"semantic":[15,24],"segmentation,":[16],"however,":[17],"most":[18],"of":[19,49,116],"the":[20,37,123,166],"previous":[21],"works":[22],"segmentation":[25],"are":[26,55],"for":[27,154,158],"automatic":[28],"driving":[29],"do":[31],"not":[32],"fully":[33],"taken":[34],"into":[35],"account":[36],"specific":[38],"difficulties":[39,51],"that":[40,53],"exist":[41],"high":[43],"resolution":[44,115],"remote":[45,63],"sensing":[46,64],"imagery.":[47,65],"One":[48],"such":[50],"is":[52],"objects":[54],"small,":[56],"crowded":[57],"intra-class":[59],"scale":[60],"difference":[61],"To":[66],"tackle":[67],"with":[68,85],"this":[69],"challenging":[70],"task,":[71],"we":[72],"proposed":[74,101,124],"a":[75],"novel":[76],"architecture":[77],"which":[78],"adopts":[79],"encoder-decoder":[80],"structure,":[81],"multi-scale":[82],"dilated":[83],"convolution":[84,90],"spatial":[86],"attention":[87,96],"separable":[89],"(Global":[91],"Attention":[92,103,125],"Pyramid)":[93],"channel":[95],"decoder":[97],"(Attention":[98],"Decoder).":[99],"The":[100],"Global":[102],"Pyramid":[104],"module":[105,127],"solves":[106,128],"these":[107,129],"problems":[108,130],"by":[109,131],"enlarging":[110],"receptive":[111],"field":[112],"without":[113],"reducing":[114],"feature":[117],"maps":[118],"pixel-level":[120],"attention.":[121],"And":[122,161],"Decoder":[126],"providing":[132],"global":[133],"context":[134],"select":[136],"category":[137],"localization":[138],"details.":[139],"We":[140],"tested":[141],"our":[142,162],"network":[143,164],"on":[144],"two":[145],"satellite":[146],"imagery":[147],"datasets":[148,156],"acquired":[150],"remarkably":[151],"good":[152],"results":[153],"both":[155],"especially":[157],"small":[159],"objects.":[160],"new":[163],"improves":[165],"performance":[167],"from":[168],"0.6341":[169],"0.6510":[171],"DEEPGLOBE":[173],"road":[174],"extraction":[175],"dataset.":[176]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}