{"id":"https://openalex.org/W2547578938","doi":"https://doi.org/10.1109/igarss.2016.7729017","title":"Very deep learning for ship discrimination in Synthetic Aperture Radar imagery","display_name":"Very deep learning for ship discrimination in Synthetic Aperture Radar imagery","publication_year":2016,"publication_date":"2016-07-01","ids":{"openalex":"https://openalex.org/W2547578938","doi":"https://doi.org/10.1109/igarss.2016.7729017","mag":"2547578938"},"language":"en","primary_location":{"id":"doi:10.1109/igarss.2016.7729017","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2016.7729017","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://figshare.com/articles/conference_contribution/Very_deep_learning_for_ship_discrimination_in_Synthetic_Aperture_Radar_imagery/23112140","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5074537518","display_name":"C. P. Schwegmann","orcid":"https://orcid.org/0000-0002-4953-7322"},"institutions":[{"id":"https://openalex.org/I69552723","display_name":"University of Pretoria","ror":"https://ror.org/00g0p6g84","country_code":"ZA","type":"education","lineage":["https://openalex.org/I69552723"]}],"countries":["ZA"],"is_corresponding":false,"raw_author_name":"C.P. Schwegmann","raw_affiliation_strings":["Department of Electrical, Electronic and Computer Engineering, University of Pretoria, South Africa","CSIR, Meraka Institute, Pretoria, South Africa"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical, Electronic and Computer Engineering, University of Pretoria, South Africa","institution_ids":["https://openalex.org/I69552723"]},{"raw_affiliation_string":"CSIR, Meraka Institute, Pretoria, South Africa","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5031004986","display_name":"W. Kleynhans","orcid":"https://orcid.org/0000-0002-7046-7023"},"institutions":[{"id":"https://openalex.org/I69552723","display_name":"University of Pretoria","ror":"https://ror.org/00g0p6g84","country_code":"ZA","type":"education","lineage":["https://openalex.org/I69552723"]}],"countries":["ZA"],"is_corresponding":false,"raw_author_name":"W. Kleynhans","raw_affiliation_strings":["Department of Electrical, Electronic and Computer Engineering, University of Pretoria, South Africa","CSIR, Meraka Institute, Pretoria, South Africa"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical, Electronic and Computer Engineering, University of Pretoria, South Africa","institution_ids":["https://openalex.org/I69552723"]},{"raw_affiliation_string":"CSIR, Meraka Institute, Pretoria, South Africa","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014524509","display_name":"B.P. Salmon","orcid":"https://orcid.org/0000-0001-5722-3414"},"institutions":[{"id":"https://openalex.org/I129801699","display_name":"University of Tasmania","ror":"https://ror.org/01nfmeh72","country_code":"AU","type":"education","lineage":["https://openalex.org/I129801699"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"B.P. Salmon","raw_affiliation_strings":["School of Engineering and ICT, University of Tasmania, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Engineering and ICT, University of Tasmania, Australia","institution_ids":["https://openalex.org/I129801699"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052530563","display_name":"L. W. Mdakane","orcid":"https://orcid.org/0000-0001-8541-8243"},"institutions":[{"id":"https://openalex.org/I69552723","display_name":"University of Pretoria","ror":"https://ror.org/00g0p6g84","country_code":"ZA","type":"education","lineage":["https://openalex.org/I69552723"]}],"countries":["ZA"],"is_corresponding":false,"raw_author_name":"L.W. Mdakane","raw_affiliation_strings":["Department of Electrical, Electronic and Computer Engineering, University of Pretoria, South Africa","CSIR, Meraka Institute, Pretoria, South Africa"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical, Electronic and Computer Engineering, University of Pretoria, South Africa","institution_ids":["https://openalex.org/I69552723"]},{"raw_affiliation_string":"CSIR, Meraka Institute, Pretoria, South Africa","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5067849861","display_name":"Rory Meyer","orcid":"https://orcid.org/0000-0002-0440-215X"},"institutions":[{"id":"https://openalex.org/I69552723","display_name":"University of Pretoria","ror":"https://ror.org/00g0p6g84","country_code":"ZA","type":"education","lineage":["https://openalex.org/I69552723"]}],"countries":["ZA"],"is_corresponding":false,"raw_author_name":"R.G.V. Meyer","raw_affiliation_strings":["Department of Electrical, Electronic and Computer Engineering, University of Pretoria, South Africa","CSIR, Meraka Institute, Pretoria, South Africa"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical, Electronic and Computer Engineering, University of Pretoria, South Africa","institution_ids":["https://openalex.org/I69552723"]},{"raw_affiliation_string":"CSIR, Meraka Institute, Pretoria, South Africa","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":19.846,"has_fulltext":false,"cited_by_count":93,"citation_normalized_percentile":{"value":0.99913364,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"104","last_page":"107"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13282","display_name":"Automated Road and Building Extraction","score":0.9980000257492065,"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":0.9980000257492065,"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":0.9970999956130981,"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/T11698","display_name":"Underwater Acoustics Research","score":0.9896000027656555,"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/synthetic-aperture-radar","display_name":"Synthetic aperture radar","score":0.865105152130127},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.767781674861908},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6588459014892578},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.600448727607727},{"id":"https://openalex.org/keywords/radar","display_name":"Radar","score":0.504562258720398},{"id":"https://openalex.org/keywords/radar-imaging","display_name":"Radar imaging","score":0.44357335567474365},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.4237050712108612},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.38715800642967224},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3432662785053253},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.33038195967674255},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.11208155751228333}],"concepts":[{"id":"https://openalex.org/C87360688","wikidata":"https://www.wikidata.org/wiki/Q740686","display_name":"Synthetic aperture radar","level":2,"score":0.865105152130127},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.767781674861908},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6588459014892578},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.600448727607727},{"id":"https://openalex.org/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.504562258720398},{"id":"https://openalex.org/C10929652","wikidata":"https://www.wikidata.org/wiki/Q7279985","display_name":"Radar imaging","level":3,"score":0.44357335567474365},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.4237050712108612},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.38715800642967224},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3432662785053253},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.33038195967674255},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.11208155751228333}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/igarss.2016.7729017","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2016.7729017","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","raw_type":"proceedings-article"},{"id":"pmh:oai:ecite.utas.edu.au:117096","is_oa":false,"landing_page_url":"http://ecite.utas.edu.au/117096","pdf_url":null,"source":{"id":"https://openalex.org/S4306401569","display_name":"eCite Digital Repository (University of Tasmania)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I129801699","host_organization_name":"University of Tasmania","host_organization_lineage":["https://openalex.org/I129801699"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Non Refereed Conference Paper"},{"id":"pmh:oai:figshare.com:article/23112140","is_oa":true,"landing_page_url":"https://figshare.com/articles/conference_contribution/Very_deep_learning_for_ship_discrimination_in_Synthetic_Aperture_Radar_imagery/23112140","pdf_url":null,"source":{"id":"https://openalex.org/S4377196282","display_name":"Figshare","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210132348","host_organization_name":"Figshare (United Kingdom)","host_organization_lineage":["https://openalex.org/I4210132348"],"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":null,"raw_type":"Conference contribution"}],"best_oa_location":{"id":"pmh:oai:figshare.com:article/23112140","is_oa":true,"landing_page_url":"https://figshare.com/articles/conference_contribution/Very_deep_learning_for_ship_discrimination_in_Synthetic_Aperture_Radar_imagery/23112140","pdf_url":null,"source":{"id":"https://openalex.org/S4377196282","display_name":"Figshare","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210132348","host_organization_name":"Figshare (United Kingdom)","host_organization_lineage":["https://openalex.org/I4210132348"],"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":null,"raw_type":"Conference contribution"},"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.4099999964237213,"id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W1026270304","https://openalex.org/W1522301498","https://openalex.org/W1955830868","https://openalex.org/W2089588733","https://openalex.org/W2112796928","https://openalex.org/W2165616753","https://openalex.org/W2950621961","https://openalex.org/W2964121744","https://openalex.org/W3097983326","https://openalex.org/W3118608800","https://openalex.org/W4230946174","https://openalex.org/W6626481562","https://openalex.org/W6631190155","https://openalex.org/W6787972765"],"related_works":["https://openalex.org/W2545123933","https://openalex.org/W2585813813","https://openalex.org/W3110962985","https://openalex.org/W2042726296","https://openalex.org/W2081203575","https://openalex.org/W2096748030","https://openalex.org/W3016428515","https://openalex.org/W2160730947","https://openalex.org/W2747205507","https://openalex.org/W2612690400"],"abstract_inverted_index":{"Efficient":[0],"and":[1,33,145],"effective":[2],"ship":[3,76,111,116,132,159],"discrimination":[4,112,133,160],"across":[5,97],"multiple":[6],"Synthetic":[7],"Aperture":[8],"Radar":[9],"sensors":[10],"is":[11,31,82],"becoming":[12],"more":[13,21,128],"important":[14],"as":[15,109],"access":[16],"to":[17,55,88,169],"SAR":[18,115,123,143],"data":[19],"becomes":[20],"widespread.":[22],"A":[23,78],"flexible":[24,79],"means":[25],"of":[26,131,151],"separating":[27],"ships":[28],"from":[29],"sea":[30],"ideal":[32],"can":[34,66],"be":[35,67],"accomplished":[36],"using":[37,69],"machine":[38],"learning.":[39],"Newer,":[40],"advanced":[41],"deep":[42,63,105],"learning":[43],"techniques":[44],"offer":[45],"a":[46,52,103,110,121,141],"unique":[47],"solution":[48],"but":[49],"traditionally":[50],"require":[51],"large":[53],"dataset":[54,124,144],"train":[56],"effectively.":[57],"Highway":[58,85],"Networks":[59,86],"allow":[60],"for":[61,114,127],"very":[62,104],"networks":[64],"that":[65,125],"trained":[68],"the":[70,147,170],"smaller":[71],"datasets":[72],"typical":[73],"in":[74],"SAR-based":[75],"detection.":[77,117],"network":[80],"configuration":[81,108],"possible":[83],"within":[84],"due":[87],"an":[89],"adaptive":[90],"gating":[91],"mechanism":[92],"which":[93],"prevents":[94],"gradient":[95],"decay":[96],"many":[98],"layers.":[99],"This":[100],"paper":[101],"presents":[102,120],"High":[106],"Network":[107],"stage":[113],"It":[118],"also":[119,162],"three-class":[122],"allows":[126],"meaningful":[129],"analysis":[130],"performances.":[134],"The":[135,157],"proposed":[136,158],"method":[137,161],"was":[138],"tested":[139,154],"on":[140],"this":[142],"had":[146],"highest":[148],"mean":[149],"accuracy":[150],"all":[152],"methods":[153,172],"at":[155],"96.67%.":[156],"provides":[163],"improved":[164],"false":[165],"positive":[166],"classification":[167],"compared":[168],"other":[171],"tested.":[173]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":9},{"year":2023,"cited_by_count":8},{"year":2022,"cited_by_count":8},{"year":2021,"cited_by_count":14},{"year":2020,"cited_by_count":15},{"year":2019,"cited_by_count":14},{"year":2018,"cited_by_count":10},{"year":2017,"cited_by_count":12}],"updated_date":"2026-08-05T07:39:15.569665","created_date":"2025-10-10T00:00:00"}