{"id":"https://openalex.org/W4389104751","doi":"https://doi.org/10.1109/jstars.2023.3336927","title":"VS-LSDet: A Multiscale Ship Detector for Spaceborne SAR Images Based on Visual Saliency and Lightweight CNN","display_name":"VS-LSDet: A Multiscale Ship Detector for Spaceborne SAR Images Based on Visual Saliency and Lightweight CNN","publication_year":2023,"publication_date":"2023-11-28","ids":{"openalex":"https://openalex.org/W4389104751","doi":"https://doi.org/10.1109/jstars.2023.3336927"},"language":"en","primary_location":{"id":"doi:10.1109/jstars.2023.3336927","is_oa":true,"landing_page_url":"https://doi.org/10.1109/jstars.2023.3336927","pdf_url":"https://ieeexplore.ieee.org/ielx7/4609443/4609444/10331919.pdf","source":{"id":"https://openalex.org/S117727964","display_name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","issn_l":"1939-1404","issn":["1939-1404","2151-1535"],"is_oa":true,"is_in_doaj":true,"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 Journal of Selected Topics in Applied Earth Observations and Remote Sensing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/4609443/4609444/10331919.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100648591","display_name":"Hang Yu","orcid":"https://orcid.org/0000-0002-8869-6166"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Hang Yu","raw_affiliation_strings":["School of Aerospace Science and Technology, Xidian University, Xi&#x0027;an, China"],"affiliations":[{"raw_affiliation_string":"School of Aerospace Science and Technology, Xidian University, Xi&#x0027;an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5031642864","display_name":"Shihang Yang","orcid":"https://orcid.org/0009-0004-8579-1418"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shihang Yang","raw_affiliation_strings":["School of Aerospace Science and Technology, Xidian University, Xi&#x0027;an, China"],"affiliations":[{"raw_affiliation_string":"School of Aerospace Science and Technology, Xidian University, Xi&#x0027;an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049798236","display_name":"Suiping Zhou","orcid":"https://orcid.org/0000-0003-0914-066X"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Suiping Zhou","raw_affiliation_strings":["School of Aerospace Science and Technology, Xidian University, Xi&#x0027;an, China"],"affiliations":[{"raw_affiliation_string":"School of Aerospace Science and Technology, Xidian University, Xi&#x0027;an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101631122","display_name":"Yibo Sun","orcid":"https://orcid.org/0009-0005-8662-8935"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yibo Sun","raw_affiliation_strings":["School of Aerospace Science and Technology, Xidian University, Xi&#x0027;an, China"],"affiliations":[{"raw_affiliation_string":"School of Aerospace Science and Technology, Xidian University, Xi&#x0027;an, China","institution_ids":["https://openalex.org/I149594827"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5100648591"],"corresponding_institution_ids":["https://openalex.org/I149594827"],"apc_list":{"value":1250,"currency":"USD","value_usd":1250},"apc_paid":{"value":1250,"currency":"USD","value_usd":1250},"fwci":2.6267,"has_fulltext":true,"cited_by_count":22,"citation_normalized_percentile":{"value":0.91878524,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":"17","issue":null,"first_page":"1137","last_page":"1154"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9994999766349792,"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"}},"topics":[{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9994999766349792,"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/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.9950000047683716,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9936000108718872,"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":0.8266576528549194},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7474980354309082},{"id":"https://openalex.org/keywords/synthetic-aperture-radar","display_name":"Synthetic aperture radar","score":0.6797552704811096},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6575128436088562},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6398764848709106},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.6156483292579651},{"id":"https://openalex.org/keywords/preprocessor","display_name":"Preprocessor","score":0.5998122692108154},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.556810736656189},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.5534638166427612},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.5333507061004639},{"id":"https://openalex.org/keywords/speckle-pattern","display_name":"Speckle pattern","score":0.5106936097145081},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5085166692733765},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.4932815432548523},{"id":"https://openalex.org/keywords/speckle-noise","display_name":"Speckle noise","score":0.4907335042953491},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.4846823513507843},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4594680964946747},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.4584159553050995},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.4342218339443207},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.24343213438987732},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.1991007924079895}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8266576528549194},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7474980354309082},{"id":"https://openalex.org/C87360688","wikidata":"https://www.wikidata.org/wiki/Q740686","display_name":"Synthetic aperture radar","level":2,"score":0.6797552704811096},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6575128436088562},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6398764848709106},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.6156483292579651},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.5998122692108154},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.556810736656189},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.5534638166427612},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.5333507061004639},{"id":"https://openalex.org/C102290492","wikidata":"https://www.wikidata.org/wiki/Q7575045","display_name":"Speckle pattern","level":2,"score":0.5106936097145081},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5085166692733765},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.4932815432548523},{"id":"https://openalex.org/C180940675","wikidata":"https://www.wikidata.org/wiki/Q7575045","display_name":"Speckle noise","level":3,"score":0.4907335042953491},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.4846823513507843},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4594680964946747},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.4584159553050995},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.4342218339443207},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.24343213438987732},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.1991007924079895},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","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},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"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/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/jstars.2023.3336927","is_oa":true,"landing_page_url":"https://doi.org/10.1109/jstars.2023.3336927","pdf_url":"https://ieeexplore.ieee.org/ielx7/4609443/4609444/10331919.pdf","source":{"id":"https://openalex.org/S117727964","display_name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","issn_l":"1939-1404","issn":["1939-1404","2151-1535"],"is_oa":true,"is_in_doaj":true,"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 Journal of Selected Topics in Applied Earth Observations and Remote Sensing","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:2fbba2bb91d3446f83cc14b0cc50f16e","is_oa":true,"landing_page_url":"https://doaj.org/article/2fbba2bb91d3446f83cc14b0cc50f16e","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":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 1137-1154 (2024)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/jstars.2023.3336927","is_oa":true,"landing_page_url":"https://doi.org/10.1109/jstars.2023.3336927","pdf_url":"https://ieeexplore.ieee.org/ielx7/4609443/4609444/10331919.pdf","source":{"id":"https://openalex.org/S117727964","display_name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","issn_l":"1939-1404","issn":["1939-1404","2151-1535"],"is_oa":true,"is_in_doaj":true,"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 Journal of Selected Topics in Applied Earth Observations and Remote Sensing","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G7762174793","display_name":null,"funder_award_id":"123456","funder_id":"https://openalex.org/F4320306111","funder_display_name":"U.S. Department of Commerce"}],"funders":[{"id":"https://openalex.org/F4320306111","display_name":"U.S. Department of Commerce","ror":"https://ror.org/04chq2495"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4389104751.pdf","grobid_xml":"https://content.openalex.org/works/W4389104751.grobid-xml"},"referenced_works_count":59,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W1923947552","https://openalex.org/W2090991102","https://openalex.org/W2102605133","https://openalex.org/W2109255472","https://openalex.org/W2110519070","https://openalex.org/W2114428281","https://openalex.org/W2146103513","https://openalex.org/W2158347164","https://openalex.org/W2160445017","https://openalex.org/W2193145675","https://openalex.org/W2412782625","https://openalex.org/W2531409750","https://openalex.org/W2565639579","https://openalex.org/W2570343428","https://openalex.org/W2619158701","https://openalex.org/W2752782242","https://openalex.org/W2765879893","https://openalex.org/W2883780447","https://openalex.org/W2884561390","https://openalex.org/W2884585870","https://openalex.org/W2890700095","https://openalex.org/W2962772649","https://openalex.org/W2963150697","https://openalex.org/W2963604034","https://openalex.org/W2963794428","https://openalex.org/W2982770724","https://openalex.org/W2989604896","https://openalex.org/W2997506757","https://openalex.org/W3012573144","https://openalex.org/W3032837604","https://openalex.org/W3033996275","https://openalex.org/W3034552520","https://openalex.org/W3035396860","https://openalex.org/W3035414587","https://openalex.org/W3041525128","https://openalex.org/W3042011474","https://openalex.org/W3089780760","https://openalex.org/W3106250896","https://openalex.org/W3117374221","https://openalex.org/W3167706095","https://openalex.org/W3168997536","https://openalex.org/W3177052299","https://openalex.org/W3184439416","https://openalex.org/W3195183808","https://openalex.org/W3200733355","https://openalex.org/W4207076822","https://openalex.org/W4213156196","https://openalex.org/W4224239645","https://openalex.org/W4292553542","https://openalex.org/W4293584584","https://openalex.org/W4297549407","https://openalex.org/W4312277507","https://openalex.org/W4322730913","https://openalex.org/W4385422915","https://openalex.org/W4386076325","https://openalex.org/W6638667902","https://openalex.org/W6750227808","https://openalex.org/W6798838024"],"related_works":["https://openalex.org/W2065648684","https://openalex.org/W2009383287","https://openalex.org/W2042914788","https://openalex.org/W2182190754","https://openalex.org/W4321264664","https://openalex.org/W2121688719","https://openalex.org/W2016481886","https://openalex.org/W2055824452","https://openalex.org/W2727313114","https://openalex.org/W1989852278"],"abstract_inverted_index":{"Recently,":[0],"deep":[1],"learning-based":[2],"methods":[3,18,39],"for":[4],"synthetic":[5],"aperture":[6],"radar":[7],"(SAR)":[8],"ship":[9,58,63,209,216],"detection":[10,24,217,222],"have":[11],"made":[12],"remarkable":[13],"advancements.":[14],"However,":[15],"most":[16],"existing":[17],"primarily":[19],"focus":[20],"on":[21,206,229],"achieving":[22],"high":[23],"accuracy":[25,223],"by":[26,125],"employing":[27],"complex":[28],"models,":[29],"leading":[30],"to":[31,88,130,139,160,214],"an":[32],"increase":[33],"in":[34,100,201],"computational":[35],"costs.":[36],"Additionally,":[37],"some":[38],"do":[40],"not":[41],"adequately":[42],"consider":[43],"the":[44,91,95,101,132,141,145,167,186,199,230,233],"impact":[45,96],"of":[46,97,117,144,236],"speckle":[47,98],"noise":[48,99],"interference.":[49],"To":[50],"address":[51],"these":[52],"challenges,":[53],"we":[54,149],"propose":[55,150],"a":[56,76,85,104,151,171],"multiscale":[57],"detector,":[59],"called":[60,107],"visual":[61,67,77],"saliency-lightweight":[62],"detector":[64],"(VS-LSDet),":[65],"utilizing":[66],"saliency":[68,78],"and":[69,93,135,182,244],"lightweight":[70,105],"convolutional":[71],"neural":[72],"network":[73],"(CNN).":[74],"First,":[75],"enhancement":[79],"module":[80,174],"(VSEM)":[81],"is":[82,111,176,238],"proposed":[83],"as":[84,121],"preprocessing":[86],"step":[87],"visually":[89],"highlight":[90],"ships":[92],"weaken":[94],"image.":[102],"Second,":[103],"backbone":[106],"ghost-shuffle":[108,118],"net":[109],"(GSNet)":[110],"designed.":[112],"We":[113],"introduce":[114],"two":[115],"types":[116],"blocks":[119,124],"(GSBlocks)":[120],"basic":[122],"convolution":[123,128,154],"introducing":[126],"ghost":[127],"(GSConv)":[129],"reduce":[131],"model":[133,226],"complexity,":[134],"channel":[136,183],"shuffle":[137],"operation":[138],"enhance":[140],"representation":[142],"ability":[143],"feature":[146,187,202],"map.":[147,188,203],"Then,":[148],"multi-shape":[152],"dilated":[153],"block":[155],"(MDCB)":[156],"incorporated":[157],"into":[158],"GSNet":[159],"enlarge":[161],"its":[162],"receptive":[163],"fields,":[164],"further":[165],"improving":[166],"detector's":[168],"performance.":[169],"Finally,":[170],"hybrid":[172],"attention":[173],"(HyAM)":[175],"proposed,":[177],"it":[178],"leverages":[179],"both":[180],"spatial":[181],"information":[184],"within":[185],"HyAM":[189],"can":[190],"emphasize":[191],"ship-related":[192],"features":[193,197],"while":[194],"suppressing":[195],"irrelevant":[196],"from":[198],"background":[200],"Experimental":[204],"results":[205],"public":[207],"SAR":[208],"datasets":[210],"demonstrate":[211],"that,":[212],"compared":[213],"other":[215],"methods,":[218],"VS-LSDet":[219,237],"achieves":[220],"higher":[221],"with":[224,240],"lower":[225],"complexity.":[227],"Specifically,":[228],"SSDD":[231],"dataset,":[232],"AP":[234],"value":[235],"97.51%,":[239],"2.53":[241],"M":[242],"parameters":[243],"6.21":[245],"GFLOPs.":[246]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":7},{"year":2024,"cited_by_count":12}],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2025-10-10T00:00:00"}
