{"id":"https://openalex.org/W3015086263","doi":"https://doi.org/10.3390/rs12071117","title":"Numerical Investigations on Wave Remote Sensing from Synthetic X-Band Radar Sea Clutter Images by Using Deep Convolutional Neural Networks","display_name":"Numerical Investigations on Wave Remote Sensing from Synthetic X-Band Radar Sea Clutter Images by Using Deep Convolutional Neural Networks","publication_year":2020,"publication_date":"2020-04-01","ids":{"openalex":"https://openalex.org/W3015086263","doi":"https://doi.org/10.3390/rs12071117","mag":"3015086263"},"language":"en","primary_location":{"id":"doi:10.3390/rs12071117","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs12071117","pdf_url":"https://www.mdpi.com/2072-4292/12/7/1117/pdf?version=1585733382","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/2072-4292/12/7/1117/pdf?version=1585733382","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5014961587","display_name":"Wenyang Duan","orcid":"https://orcid.org/0000-0002-7811-4986"},"institutions":[{"id":"https://openalex.org/I151727225","display_name":"Harbin Engineering University","ror":"https://ror.org/03x80pn82","country_code":"CN","type":"education","lineage":["https://openalex.org/I151727225"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenyang Duan","raw_affiliation_strings":["College of Shipbuilding Engineering, Harbin Engineering University, Harbin 150001, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Shipbuilding Engineering, Harbin Engineering University, Harbin 150001, China","institution_ids":["https://openalex.org/I151727225"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101650558","display_name":"Ke Yang","orcid":"https://orcid.org/0000-0002-1934-5864"},"institutions":[{"id":"https://openalex.org/I151727225","display_name":"Harbin Engineering University","ror":"https://ror.org/03x80pn82","country_code":"CN","type":"education","lineage":["https://openalex.org/I151727225"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ke Yang","raw_affiliation_strings":["College of Shipbuilding Engineering, Harbin Engineering University, Harbin 150001, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Shipbuilding Engineering, Harbin Engineering University, Harbin 150001, China","institution_ids":["https://openalex.org/I151727225"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101895600","display_name":"Limin Huang","orcid":"https://orcid.org/0000-0002-7944-2754"},"institutions":[{"id":"https://openalex.org/I151727225","display_name":"Harbin Engineering University","ror":"https://ror.org/03x80pn82","country_code":"CN","type":"education","lineage":["https://openalex.org/I151727225"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Limin Huang","raw_affiliation_strings":["College of Shipbuilding Engineering, Harbin Engineering University, Harbin 150001, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Shipbuilding Engineering, Harbin Engineering University, Harbin 150001, China","institution_ids":["https://openalex.org/I151727225"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5112518498","display_name":"Xuewen Ma","orcid":null},"institutions":[{"id":"https://openalex.org/I151727225","display_name":"Harbin Engineering University","ror":"https://ror.org/03x80pn82","country_code":"CN","type":"education","lineage":["https://openalex.org/I151727225"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xuewen Ma","raw_affiliation_strings":["College of Shipbuilding Engineering, Harbin Engineering University, Harbin 150001, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Shipbuilding Engineering, Harbin Engineering University, Harbin 150001, China","institution_ids":["https://openalex.org/I151727225"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5101895600"],"corresponding_institution_ids":["https://openalex.org/I151727225"],"apc_list":{"value":2500,"currency":"CHF","value_usd":2707},"apc_paid":{"value":2500,"currency":"CHF","value_usd":2707},"fwci":2.5292,"has_fulltext":true,"cited_by_count":40,"citation_normalized_percentile":{"value":0.87750944,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":93,"max":99},"biblio":{"volume":"12","issue":"7","first_page":"1117","last_page":"1117"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11061","display_name":"Ocean Waves and Remote Sensing","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1910","display_name":"Oceanography"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11061","display_name":"Ocean Waves and Remote Sensing","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1910","display_name":"Oceanography"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10891","display_name":"Radar Systems and Signal Processing","score":0.9991000294685364,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace 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/T11698","display_name":"Underwater Acoustics Research","score":0.9966999888420105,"subfield":{"id":"https://openalex.org/subfields/1910","display_name":"Oceanography"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/clutter","display_name":"Clutter","score":0.6927043199539185},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6587924957275391},{"id":"https://openalex.org/keywords/inversion","display_name":"Inversion (geology)","score":0.6587843894958496},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6555212140083313},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.584816575050354},{"id":"https://openalex.org/keywords/radar","display_name":"Radar","score":0.5194470286369324},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5035554766654968},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.41321444511413574},{"id":"https://openalex.org/keywords/radar-imaging","display_name":"Radar imaging","score":0.4114353060722351},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3244209885597229},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.3037876486778259},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.14252978563308716}],"concepts":[{"id":"https://openalex.org/C132094186","wikidata":"https://www.wikidata.org/wiki/Q641585","display_name":"Clutter","level":3,"score":0.6927043199539185},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6587924957275391},{"id":"https://openalex.org/C1893757","wikidata":"https://www.wikidata.org/wiki/Q3653001","display_name":"Inversion (geology)","level":3,"score":0.6587843894958496},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6555212140083313},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.584816575050354},{"id":"https://openalex.org/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.5194470286369324},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5035554766654968},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.41321444511413574},{"id":"https://openalex.org/C10929652","wikidata":"https://www.wikidata.org/wiki/Q7279985","display_name":"Radar imaging","level":3,"score":0.4114353060722351},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3244209885597229},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.3037876486778259},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.14252978563308716},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C109007969","wikidata":"https://www.wikidata.org/wiki/Q749565","display_name":"Structural basin","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.3390/rs12071117","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs12071117","pdf_url":"https://www.mdpi.com/2072-4292/12/7/1117/pdf?version=1585733382","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:5a8b147ad121489c813204163172d4c6","is_oa":true,"landing_page_url":"https://doaj.org/article/5a8b147ad121489c813204163172d4c6","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"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 12, Iss 7, p 1117 (2020)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.3390/rs12071117","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs12071117","pdf_url":"https://www.mdpi.com/2072-4292/12/7/1117/pdf?version=1585733382","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":"Life below water","id":"https://metadata.un.org/sdg/14","score":0.8600000143051147}],"awards":[{"id":"https://openalex.org/G4610287116","display_name":null,"funder_award_id":"No. 51490671","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8394101424","display_name":null,"funder_award_id":"No. 51809066","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":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3015086263.pdf","grobid_xml":"https://content.openalex.org/works/W3015086263.grobid-xml"},"referenced_works_count":21,"referenced_works":["https://openalex.org/W1562870170","https://openalex.org/W1982125435","https://openalex.org/W2018058208","https://openalex.org/W2064883310","https://openalex.org/W2087105482","https://openalex.org/W2093096424","https://openalex.org/W2097117768","https://openalex.org/W2112796928","https://openalex.org/W2125441934","https://openalex.org/W2144354855","https://openalex.org/W2154579312","https://openalex.org/W2163605009","https://openalex.org/W2342253746","https://openalex.org/W2374198409","https://openalex.org/W2588595876","https://openalex.org/W2617899471","https://openalex.org/W2808558805","https://openalex.org/W2860124386","https://openalex.org/W2883119505","https://openalex.org/W3098883884","https://openalex.org/W6687483927"],"related_works":["https://openalex.org/W2130674020","https://openalex.org/W2093748878","https://openalex.org/W2333771223","https://openalex.org/W2120056845","https://openalex.org/W1981531423","https://openalex.org/W1679012645","https://openalex.org/W2070318884","https://openalex.org/W4313855562","https://openalex.org/W2091422131","https://openalex.org/W2742737769"],"abstract_inverted_index":{"X-band":[0,135],"marine":[1],"radar":[2,20,136],"is":[3,90,97],"an":[4],"effective":[5],"tool":[6],"for":[7,15,141],"sea":[8,21],"wave":[9,17,58,126,146],"remote":[10],"sensing.":[11],"Conventional":[12],"physical-based":[13,64],"methods":[14,156],"acquiring":[16],"parameters":[18,200],"from":[19],"clutter":[22],"images":[23],"use":[24],"three-dimensional":[25],"Fourier":[26],"transform":[27],"and":[28,39,50,124,154,159],"spectral":[29,121,152],"analysis.":[30],"They":[31],"are":[32,106],"limited":[33],"by":[34,61,70],"some":[35],"assumptions,":[36],"empirical":[37],"formulas":[38],"the":[40,45,63,71,100,119,129,132,150,160,168,185,196,202],"calibration":[41],"process":[42],"while":[43],"obtaining":[44],"modulation":[46],"transfer":[47],"function":[48],"(MTF)":[49],"signal-to-noise":[51],"ratio":[52],"(SNR).":[53],"Therefore,":[54],"further":[55],"improvement":[56],"of":[57,73,145,149,174,187,198,205],"inversion":[59,84,110,179,190,203],"accuracy":[60,166,204],"using":[62],"method":[65,85,162],"presents":[66],"a":[67,82,142,164,188],"challenge.":[68],"Inspired":[69],"capability":[72],"convolutional":[74],"neural":[75],"networks":[76],"(CNN)":[77],"in":[78,99],"image":[79,137],"characteristic":[80],"processing,":[81],"deep-learning":[83,189],"based":[86,114],"on":[87,115,167,177,192,201],"deep":[88],"CNN":[89,116],"proposed.":[91],"No":[92],"intermediate":[93],"step":[94],"or":[95],"parameter":[96,109],"needed":[98],"CNN-based":[101,155,161,178,206],"method,":[102],"therefore":[103],"fewer":[104],"errors":[105],"introduced.":[107],"Wave":[108],"models":[111,180,207],"were":[112,139,157],"constructed":[113],"to":[117,183],"inverse":[118],"wave\u2019s":[120],"peak":[122],"period":[123],"significant":[125],"height.":[127],"In":[128],"present":[130],"paper,":[131],"numerically":[133],"simulated":[134],"data":[138,170],"used":[140],"numerical":[143],"investigation":[144],"parameters.":[147],"Results":[148],"conventional":[151],"analysis":[153],"compared":[158],"had":[163],"higher":[165],"same":[169],"set.":[171],"The":[172],"influence":[173],"training":[175,193],"strategy":[176],"was":[181,208],"studied":[182],"analyze":[184],"dependence":[186],"model":[191],"data.":[194],"Additionally,":[195],"effects":[197],"target":[199],"also":[209],"studied.":[210]},"counts_by_year":[{"year":2026,"cited_by_count":5},{"year":2025,"cited_by_count":11},{"year":2024,"cited_by_count":10},{"year":2023,"cited_by_count":7},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":2}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}
