{"id":"https://openalex.org/W4303980720","doi":"https://doi.org/10.3390/rs14194983","title":"Multiscale Normalization Attention Network for Water Body Extraction from Remote Sensing Imagery","display_name":"Multiscale Normalization Attention Network for Water Body Extraction from Remote Sensing Imagery","publication_year":2022,"publication_date":"2022-10-07","ids":{"openalex":"https://openalex.org/W4303980720","doi":"https://doi.org/10.3390/rs14194983"},"language":"en","primary_location":{"id":"doi:10.3390/rs14194983","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs14194983","pdf_url":"https://www.mdpi.com/2072-4292/14/19/4983/pdf?version=1665417167","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/2072-4292/14/19/4983/pdf?version=1665417167","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5071995587","display_name":"Xin Lyu","orcid":"https://orcid.org/0000-0003-1862-2070"},"institutions":[{"id":"https://openalex.org/I163340411","display_name":"Hohai University","ror":"https://ror.org/01wd4xt90","country_code":"CN","type":"education","lineage":["https://openalex.org/I163340411"]},{"id":"https://openalex.org/I4210155611","display_name":"Ministry of Water Resources of the People's Republic of China","ror":"https://ror.org/04e698d63","country_code":"CN","type":"government","lineage":["https://openalex.org/I4210155611"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xin Lyu","raw_affiliation_strings":["College of Computer and Information, Hohai University, Nanjing 211100, China","Key Laboratory of Water Big Data Technology of Ministry of Water Resources, Hohai University, Nanjing 211100, China"],"affiliations":[{"raw_affiliation_string":"College of Computer and Information, Hohai University, Nanjing 211100, China","institution_ids":["https://openalex.org/I163340411"]},{"raw_affiliation_string":"Key Laboratory of Water Big Data Technology of Ministry of Water Resources, Hohai University, Nanjing 211100, China","institution_ids":["https://openalex.org/I163340411","https://openalex.org/I4210155611"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053687089","display_name":"Yiwei Fang","orcid":"https://orcid.org/0000-0002-1334-7092"},"institutions":[{"id":"https://openalex.org/I163340411","display_name":"Hohai University","ror":"https://ror.org/01wd4xt90","country_code":"CN","type":"education","lineage":["https://openalex.org/I163340411"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yiwei Fang","raw_affiliation_strings":["College of Computer and Information, Hohai University, Nanjing 211100, China"],"affiliations":[{"raw_affiliation_string":"College of Computer and Information, Hohai University, Nanjing 211100, China","institution_ids":["https://openalex.org/I163340411"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5075763094","display_name":"Baogen Tong","orcid":"https://orcid.org/0000-0001-9047-7707"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Baogen Tong","raw_affiliation_strings":["Tongshan Water Conservancy Bureau, Xuzhou 221116, China"],"affiliations":[{"raw_affiliation_string":"Tongshan Water Conservancy Bureau, Xuzhou 221116, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060642323","display_name":"Xin Li","orcid":"https://orcid.org/0000-0003-0576-3181"},"institutions":[{"id":"https://openalex.org/I163340411","display_name":"Hohai University","ror":"https://ror.org/01wd4xt90","country_code":"CN","type":"education","lineage":["https://openalex.org/I163340411"]},{"id":"https://openalex.org/I4210155611","display_name":"Ministry of Water Resources of the People's Republic of China","ror":"https://ror.org/04e698d63","country_code":"CN","type":"government","lineage":["https://openalex.org/I4210155611"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xin Li","raw_affiliation_strings":["College of Computer and Information, Hohai University, Nanjing 211100, China","Key Laboratory of Water Big Data Technology of Ministry of Water Resources, Hohai University, Nanjing 211100, China"],"affiliations":[{"raw_affiliation_string":"College of Computer and Information, Hohai University, Nanjing 211100, China","institution_ids":["https://openalex.org/I163340411"]},{"raw_affiliation_string":"Key Laboratory of Water Big Data Technology of Ministry of Water Resources, Hohai University, Nanjing 211100, China","institution_ids":["https://openalex.org/I163340411","https://openalex.org/I4210155611"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5063558254","display_name":"Tao Zeng","orcid":"https://orcid.org/0000-0001-8006-3287"},"institutions":[{"id":"https://openalex.org/I163340411","display_name":"Hohai University","ror":"https://ror.org/01wd4xt90","country_code":"CN","type":"education","lineage":["https://openalex.org/I163340411"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tao Zeng","raw_affiliation_strings":["College of Computer and Information, Hohai University, Nanjing 211100, China"],"affiliations":[{"raw_affiliation_string":"College of Computer and Information, Hohai University, Nanjing 211100, China","institution_ids":["https://openalex.org/I163340411"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5053687089","https://openalex.org/A5060642323"],"corresponding_institution_ids":["https://openalex.org/I163340411","https://openalex.org/I4210155611"],"apc_list":{"value":2500,"currency":"CHF","value_usd":2707},"apc_paid":{"value":2500,"currency":"CHF","value_usd":2707},"fwci":1.4735,"has_fulltext":true,"cited_by_count":13,"citation_normalized_percentile":{"value":0.81070428,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"14","issue":"19","first_page":"4983","last_page":"4983"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10930","display_name":"Flood Risk Assessment and Management","score":0.9991000294685364,"subfield":{"id":"https://openalex.org/subfields/2306","display_name":"Global and Planetary Change"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10930","display_name":"Flood Risk Assessment and Management","score":0.9991000294685364,"subfield":{"id":"https://openalex.org/subfields/2306","display_name":"Global and Planetary Change"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.9890000224113464,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13282","display_name":"Automated Road and Building Extraction","score":0.9843000173568726,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8090101480484009},{"id":"https://openalex.org/keywords/normalization","display_name":"Normalization (sociology)","score":0.6976893544197083},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6516926288604736},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5628833770751953},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5484592914581299},{"id":"https://openalex.org/keywords/pooling","display_name":"Pooling","score":0.5153852701187134},{"id":"https://openalex.org/keywords/merge","display_name":"Merge (version control)","score":0.496446430683136},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.4766032099723816},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4625600576400757},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.46173274517059326},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.45003458857536316},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.32979846000671387},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.12491995096206665}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8090101480484009},{"id":"https://openalex.org/C136886441","wikidata":"https://www.wikidata.org/wiki/Q926129","display_name":"Normalization (sociology)","level":2,"score":0.6976893544197083},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6516926288604736},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5628833770751953},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5484592914581299},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.5153852701187134},{"id":"https://openalex.org/C197129107","wikidata":"https://www.wikidata.org/wiki/Q1921621","display_name":"Merge (version control)","level":2,"score":0.496446430683136},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.4766032099723816},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4625600576400757},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.46173274517059326},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.45003458857536316},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.32979846000671387},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.12491995096206665},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C19165224","wikidata":"https://www.wikidata.org/wiki/Q23404","display_name":"Anthropology","level":1,"score":0.0},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.3390/rs14194983","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs14194983","pdf_url":"https://www.mdpi.com/2072-4292/14/19/4983/pdf?version=1665417167","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:26b726fbb3414d7ca7115c327dff09c6","is_oa":true,"landing_page_url":"https://doaj.org/article/26b726fbb3414d7ca7115c327dff09c6","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","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Remote Sensing, Vol 14, Iss 19, p 4983 (2022)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/2072-4292/14/19/4983/","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/rs14194983","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 14; Issue 19; Pages: 4983","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/rs14194983","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs14194983","pdf_url":"https://www.mdpi.com/2072-4292/14/19/4983/pdf?version=1665417167","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":[{"id":"https://metadata.un.org/sdg/6","display_name":"Clean water and sanitation","score":0.8500000238418579}],"awards":[{"id":"https://openalex.org/G1961011363","display_name":null,"funder_award_id":"B210202080","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"},{"id":"https://openalex.org/G8951484681","display_name":null,"funder_award_id":"Grant","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"}],"funders":[{"id":"https://openalex.org/F4320321605","display_name":"Government of Jiangsu Province","ror":"https://ror.org/004svx814"},{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4303980720.pdf","grobid_xml":"https://content.openalex.org/works/W4303980720.grobid-xml"},"referenced_works_count":53,"referenced_works":["https://openalex.org/W845365781","https://openalex.org/W1836465849","https://openalex.org/W1901129140","https://openalex.org/W1930528368","https://openalex.org/W2000330483","https://openalex.org/W2036841511","https://openalex.org/W2077509829","https://openalex.org/W2101678239","https://openalex.org/W2194775991","https://openalex.org/W2265610844","https://openalex.org/W2395611524","https://openalex.org/W2560023338","https://openalex.org/W2563705555","https://openalex.org/W2793116851","https://openalex.org/W2799213142","https://openalex.org/W2888358068","https://openalex.org/W2925148117","https://openalex.org/W2963307106","https://openalex.org/W2963881378","https://openalex.org/W2964309882","https://openalex.org/W2974382310","https://openalex.org/W2995766874","https://openalex.org/W3013368467","https://openalex.org/W3014606712","https://openalex.org/W3015724987","https://openalex.org/W3016664505","https://openalex.org/W3017153481","https://openalex.org/W3021074965","https://openalex.org/W3043740003","https://openalex.org/W3083291461","https://openalex.org/W3103092912","https://openalex.org/W3117652191","https://openalex.org/W3128040179","https://openalex.org/W3130455691","https://openalex.org/W3137032663","https://openalex.org/W3137308428","https://openalex.org/W3137572916","https://openalex.org/W3138136606","https://openalex.org/W3157967435","https://openalex.org/W3163489199","https://openalex.org/W3175350465","https://openalex.org/W3177791215","https://openalex.org/W3189528951","https://openalex.org/W3207005322","https://openalex.org/W3207769241","https://openalex.org/W4200386693","https://openalex.org/W4206815672","https://openalex.org/W4210670171","https://openalex.org/W4221144214","https://openalex.org/W4292370475","https://openalex.org/W6640054144","https://openalex.org/W6772565011","https://openalex.org/W6797325473"],"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/W3211385606","https://openalex.org/W4309346246"],"abstract_inverted_index":{"Extracting":[0],"water":[1,30,51,79,98],"bodies":[2,80],"is":[3,40],"an":[4,106],"important":[5],"task":[6],"in":[7,21,28,46,81],"remote":[8],"sensing":[9],"imagery":[10],"(RSI)":[11],"interpretation.":[12],"Deep":[13],"convolution":[14],"neural":[15],"networks":[16],"(DCNNs)":[17],"show":[18],"great":[19],"potential":[20],"feature":[22,103,134,141],"learning;":[23],"they":[24],"are":[25],"widely":[26],"used":[27],"the":[29,36,47,62,118,156,161,170,190,193],"body":[31,99],"interpretation":[32],"of":[33,38,50,85,192],"RSI.":[34],"However,":[35],"accuracy":[37],"DCNNs":[39],"still":[41],"unsatisfactory":[42],"due":[43],"to":[44,95,116,138,198],"differences":[45],"many":[48],"hetero-features":[49],"bodies,":[52],"such":[53],"as":[54],"spectrum,":[55],"geometry,":[56],"and":[57,101,143,160,183],"spatial":[58,109],"size.":[59],"To":[60],"address":[61],"problem":[63],"mentioned":[64],"above,":[65],"this":[66],"paper":[67],"proposes":[68],"a":[69,87,129],"multiscale":[70,88,97],"normalization":[71,89],"attention":[72,90],"network":[73],"(MSNANet)":[74],"which":[75,124],"can":[76],"accurately":[77],"extract":[78],"complicated":[82],"scenarios.":[83],"First":[84],"all,":[86],"(MSNA)":[91],"module":[92,113,131],"was":[93,114,136,196],"designed":[94],"merge":[96],"features":[100],"highlight":[102],"representation.":[104],"Then,":[105],"optimized":[107],"atrous":[108],"pyramid":[110],"pooling":[111],"(OASPP)":[112],"developed":[115],"refine":[117],"representation":[119],"by":[120],"leveraging":[121],"context":[122],"information,":[123],"improves":[125],"segmentation":[126],"performance.":[127],"Furthermore,":[128],"head":[130],"(FEH)":[132],"for":[133],"enhancing":[135],"devised":[137],"realize":[139],"high-level":[140],"enhancement":[142],"reduce":[144],"training":[145],"time.":[146],"The":[147,166],"extensive":[148],"experiments":[149],"were":[150],"carried":[151],"out":[152],"on":[153,177],"two":[154],"benchmarks:":[155],"Surface":[157],"Water":[158],"dataset":[159],"Qinghai\u2013Tibet":[162],"Plateau":[163],"Lake":[164],"dataset.":[165],"results":[167],"indicate":[168],"that":[169],"proposed":[171,194],"model":[172],"outperforms":[173],"current":[174],"mainstream":[175],"models":[176],"OA":[178],"(overall":[179],"accuracy),":[180],"f1-score,":[181],"kappa,":[182],"MIoU":[184],"(mean":[185],"intersection":[186],"over":[187],"union).":[188],"Moreover,":[189],"effectiveness":[191],"modules":[195],"proven":[197],"be":[199],"favorable":[200],"through":[201],"ablation":[202],"study.":[203]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":8},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":1}],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2025-10-10T00:00:00"}