{"id":"https://openalex.org/W2802827820","doi":"https://doi.org/10.1109/lgrs.2018.2822821","title":"Inversion of Rough Surface Parameters From SAR Images Using Simulation-Trained Convolutional Neural Networks","display_name":"Inversion of Rough Surface Parameters From SAR Images Using Simulation-Trained Convolutional Neural Networks","publication_year":2018,"publication_date":"2018-04-25","ids":{"openalex":"https://openalex.org/W2802827820","doi":"https://doi.org/10.1109/lgrs.2018.2822821","mag":"2802827820"},"language":"en","primary_location":{"id":"doi:10.1109/lgrs.2018.2822821","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2018.2822821","pdf_url":null,"source":{"id":"https://openalex.org/S126920919","display_name":"IEEE Geoscience and Remote Sensing Letters","issn_l":"1545-598X","issn":["1545-598X","1558-0571"],"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 Geoscience and Remote Sensing Letters","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/A5100459487","display_name":"Tao Song","orcid":"https://orcid.org/0000-0002-4995-3537"},"institutions":[{"id":"https://openalex.org/I66867065","display_name":"East China Normal University","ror":"https://ror.org/02n96ep67","country_code":"CN","type":"education","lineage":["https://openalex.org/I66867065"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Tao Song","raw_affiliation_strings":["School of Information Science and Technology, East China Normal University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"School of Information Science and Technology, East China Normal University, Shanghai, China","institution_ids":["https://openalex.org/I66867065"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056571138","display_name":"Lei Kuang","orcid":"https://orcid.org/0000-0001-5398-9332"},"institutions":[{"id":"https://openalex.org/I66867065","display_name":"East China Normal University","ror":"https://ror.org/02n96ep67","country_code":"CN","type":"education","lineage":["https://openalex.org/I66867065"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lei Kuang","raw_affiliation_strings":["School of Information Science and Technology, East China Normal University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"School of Information Science and Technology, East China Normal University, Shanghai, China","institution_ids":["https://openalex.org/I66867065"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043273140","display_name":"Lingyan Han","orcid":null},"institutions":[{"id":"https://openalex.org/I66867065","display_name":"East China Normal University","ror":"https://ror.org/02n96ep67","country_code":"CN","type":"education","lineage":["https://openalex.org/I66867065"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lingyan Han","raw_affiliation_strings":["School of Information Science and Technology, East China Normal University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"School of Information Science and Technology, East China Normal University, Shanghai, China","institution_ids":["https://openalex.org/I66867065"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100692598","display_name":"Yuheng Wang","orcid":"https://orcid.org/0000-0002-1786-5970"},"institutions":[{"id":"https://openalex.org/I90610280","display_name":"South China University of Technology","ror":"https://ror.org/0530pts50","country_code":"CN","type":"education","lineage":["https://openalex.org/I90610280"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuheng Wang","raw_affiliation_strings":["Guangdong Provincial Engineering Research Centre on Solid-State Lighting and its Informationisation, South China University of Technology, Guangzhou, China"],"affiliations":[{"raw_affiliation_string":"Guangdong Provincial Engineering Research Centre on Solid-State Lighting and its Informationisation, South China University of Technology, Guangzhou, China","institution_ids":["https://openalex.org/I90610280"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100345153","display_name":"Qing Liu","orcid":"https://orcid.org/0000-0001-5286-4423"},"institutions":[{"id":"https://openalex.org/I170897317","display_name":"Duke University","ror":"https://ror.org/00py81415","country_code":"US","type":"education","lineage":["https://openalex.org/I170897317"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Qing Huo Liu","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA","institution_ids":["https://openalex.org/I170897317"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5100459487"],"corresponding_institution_ids":["https://openalex.org/I66867065"],"apc_list":null,"apc_paid":null,"fwci":1.7411,"has_fulltext":false,"cited_by_count":29,"citation_normalized_percentile":{"value":0.82338205,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":99},"biblio":{"volume":"15","issue":"7","first_page":"1130","last_page":"1134"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11312","display_name":"Soil Moisture and Remote Sensing","score":0.9987000226974487,"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"}},"topics":[{"id":"https://openalex.org/T11312","display_name":"Soil Moisture and Remote Sensing","score":0.9987000226974487,"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/T11698","display_name":"Underwater Acoustics Research","score":0.998199999332428,"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/T11609","display_name":"Geophysical Methods and Applications","score":0.9980999827384949,"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/convolutional-neural-network","display_name":"Convolutional neural network","score":0.770328164100647},{"id":"https://openalex.org/keywords/overfitting","display_name":"Overfitting","score":0.7196062803268433},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6042094230651855},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5947935581207275},{"id":"https://openalex.org/keywords/microwave-imaging","display_name":"Microwave imaging","score":0.5316586494445801},{"id":"https://openalex.org/keywords/inversion","display_name":"Inversion (geology)","score":0.5099900960922241},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5043705701828003},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.46780160069465637},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4490243196487427},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.44354158639907837},{"id":"https://openalex.org/keywords/scattering","display_name":"Scattering","score":0.43436241149902344},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.383831650018692},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3829731345176697},{"id":"https://openalex.org/keywords/microwave","display_name":"Microwave","score":0.3524540066719055},{"id":"https://openalex.org/keywords/optics","display_name":"Optics","score":0.19037804007530212},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.18473416566848755},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.1277296543121338}],"concepts":[{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.770328164100647},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.7196062803268433},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6042094230651855},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5947935581207275},{"id":"https://openalex.org/C2779885931","wikidata":"https://www.wikidata.org/wiki/Q17010029","display_name":"Microwave imaging","level":3,"score":0.5316586494445801},{"id":"https://openalex.org/C1893757","wikidata":"https://www.wikidata.org/wiki/Q3653001","display_name":"Inversion (geology)","level":3,"score":0.5099900960922241},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5043705701828003},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.46780160069465637},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4490243196487427},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.44354158639907837},{"id":"https://openalex.org/C191486275","wikidata":"https://www.wikidata.org/wiki/Q210028","display_name":"Scattering","level":2,"score":0.43436241149902344},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.383831650018692},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3829731345176697},{"id":"https://openalex.org/C44838205","wikidata":"https://www.wikidata.org/wiki/Q127995","display_name":"Microwave","level":2,"score":0.3524540066719055},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.19037804007530212},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.18473416566848755},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.1277296543121338},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","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":1,"locations":[{"id":"doi:10.1109/lgrs.2018.2822821","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2018.2822821","pdf_url":null,"source":{"id":"https://openalex.org/S126920919","display_name":"IEEE Geoscience and Remote Sensing Letters","issn_l":"1545-598X","issn":["1545-598X","1558-0571"],"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 Geoscience and Remote Sensing Letters","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1767616326","display_name":null,"funder_award_id":"61571190","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3907107687","display_name":null,"funder_award_id":"61201070","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":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W1591333722","https://openalex.org/W1972162124","https://openalex.org/W1983002506","https://openalex.org/W2015386604","https://openalex.org/W2066916495","https://openalex.org/W2087402357","https://openalex.org/W2088841962","https://openalex.org/W2095508611","https://openalex.org/W2100495367","https://openalex.org/W2112796928","https://openalex.org/W2118516319","https://openalex.org/W2127500168","https://openalex.org/W2163605009","https://openalex.org/W2245000483","https://openalex.org/W2311852715","https://openalex.org/W2410591237","https://openalex.org/W2521365688","https://openalex.org/W2559324447","https://openalex.org/W2599262206","https://openalex.org/W6684191040"],"related_works":["https://openalex.org/W4362597605","https://openalex.org/W1574414179","https://openalex.org/W4297676672","https://openalex.org/W3009056573","https://openalex.org/W2922073769","https://openalex.org/W4281702477","https://openalex.org/W2490526372","https://openalex.org/W4376166922","https://openalex.org/W4378510483","https://openalex.org/W4221142204"],"abstract_inverted_index":{"This":[0],"letter":[1],"investigates":[2],"the":[3,15,31,69,76,116,123],"inversion":[4,125,143],"of":[5,88,91,126,137],"rough":[6,52,127,140],"surface":[7,128,141],"parameters":[8],"(the":[9],"root":[10],"mean":[11],"square":[12],"height":[13],"and":[14,65,79,93,96,104],"correlation":[16],"length)":[17],"from":[18,51,129,144],"microwave":[19,58,145],"images":[20,59],"by":[21],"using":[22,37,118],"deep":[23,32,119],"convolutional":[24,92],"neural":[25,120],"networks":[26,121],"(CNNs).":[27],"Training":[28],"data":[29],"for":[30,101,109,122,139],"CNN":[33,42,86,138],"are":[34,82],"simulated":[35],"numerically":[36],"computational":[38],"electromagnetic":[39,130],"method.":[40],"As":[41],"is":[43,54],"powerful":[44],"in":[45],"extracting":[46],"image":[47],"features,":[48],"scattering":[49,131],"field":[50],"surfaces":[53],"first":[55],"converted":[56],"to":[57,73],"via":[60],"interpolated":[61],"fast":[62],"Fourier":[63],"transform":[64],"then":[66],"fed":[67],"into":[68],"CNN.":[70],"In":[71],"order":[72],"reduce":[74],"overfitting,":[75],"regularization":[77],"technique":[78],"dropout":[80],"layer":[81],"used.":[83],"The":[84,112],"proposed":[85],"consists":[87],"five":[89],"pairs":[90],"maxpooling":[94],"layers":[95,100,108],"two":[97,105],"additional":[98],"convolution":[99],"feature":[102],"extraction":[103],"fully":[106],"connected":[107],"parameter":[110,124,142],"regression.":[111],"experimental":[113],"results":[114],"demonstrated":[115],"feasibility":[117],"fields.":[132],"It":[133],"suggests":[134],"potential":[135],"application":[136],"sensing":[146],"data.":[147]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":7},{"year":2022,"cited_by_count":7},{"year":2021,"cited_by_count":5},{"year":2020,"cited_by_count":4},{"year":2019,"cited_by_count":4}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}
