{"id":"https://openalex.org/W4416664437","doi":"https://doi.org/10.1109/jstars.2025.3630995","title":"MSCF-Net: A Lightweight Multiscale SAR Image Oriented Ship Detection Algorithm in Complex Scenes","display_name":"MSCF-Net: A Lightweight Multiscale SAR Image Oriented Ship Detection Algorithm in Complex Scenes","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4416664437","doi":"https://doi.org/10.1109/jstars.2025.3630995"},"language":"en","primary_location":{"id":"doi:10.1109/jstars.2025.3630995","is_oa":true,"landing_page_url":"https://doi.org/10.1109/jstars.2025.3630995","pdf_url":null,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","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://doi.org/10.1109/jstars.2025.3630995","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Zihua Chen","orcid":"https://orcid.org/0009-0002-1535-0612"},"institutions":[{"id":"https://openalex.org/I135714990","display_name":"North University of China","ror":"https://ror.org/047bp1713","country_code":"CN","type":"education","lineage":["https://openalex.org/I135714990"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zihua Chen","raw_affiliation_strings":["State Key Laboratory of Optoelectronic Dynamic Testing Technology and Instruments in Extreme Environments, North University of China, Taiyuan, China"],"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Optoelectronic Dynamic Testing Technology and Instruments in Extreme Environments, North University of China, Taiyuan, China","institution_ids":["https://openalex.org/I135714990"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101615901","display_name":"Peng Zhang","orcid":"https://orcid.org/0000-0002-7593-1534"},"institutions":[{"id":"https://openalex.org/I135714990","display_name":"North University of China","ror":"https://ror.org/047bp1713","country_code":"CN","type":"education","lineage":["https://openalex.org/I135714990"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Peng Zhang","raw_affiliation_strings":["State Key Laboratory of Optoelectronic Dynamic Testing Technology and Instruments in Extreme Environments, North University of China, Taiyuan, China"],"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Optoelectronic Dynamic Testing Technology and Instruments in Extreme Environments, North University of China, Taiyuan, China","institution_ids":["https://openalex.org/I135714990"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047351206","display_name":"Peng Liu","orcid":"https://orcid.org/0000-0001-9107-6673"},"institutions":[{"id":"https://openalex.org/I135714990","display_name":"North University of China","ror":"https://ror.org/047bp1713","country_code":"CN","type":"education","lineage":["https://openalex.org/I135714990"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Peng Liu","raw_affiliation_strings":["School of Electrical and Control Engineering, North University of China, Taiyuan, China"],"affiliations":[{"raw_affiliation_string":"School of Electrical and Control Engineering, North University of China, Taiyuan, China","institution_ids":["https://openalex.org/I135714990"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100785818","display_name":"Mengwei Li","orcid":"https://orcid.org/0000-0002-6703-2651"},"institutions":[{"id":"https://openalex.org/I135714990","display_name":"North University of China","ror":"https://ror.org/047bp1713","country_code":"CN","type":"education","lineage":["https://openalex.org/I135714990"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Mengwei Li","raw_affiliation_strings":["State Key Laboratory of Optoelectronic Dynamic Testing Technology and Instruments in Extreme Environments, North University of China, Taiyuan, China"],"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Optoelectronic Dynamic Testing Technology and Instruments in Extreme Environments, North University of China, Taiyuan, China","institution_ids":["https://openalex.org/I135714990"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I135714990"],"apc_list":{"value":1250,"currency":"USD","value_usd":1250},"apc_paid":{"value":1250,"currency":"USD","value_usd":1250},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.36289495,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"18","issue":null,"first_page":"28856","last_page":"28872"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.7407000064849854,"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":0.7407000064849854,"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/T11038","display_name":"Advanced SAR Imaging Techniques","score":0.16619999706745148,"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/T10801","display_name":"Synthetic Aperture Radar (SAR) Applications and Techniques","score":0.02800000086426735,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/synthetic-aperture-radar","display_name":"Synthetic aperture radar","score":0.7170000076293945},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.5864999890327454},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.5253999829292297},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5023999810218811},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.46869999170303345},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.42419999837875366},{"id":"https://openalex.org/keywords/pyramid","display_name":"Pyramid (geometry)","score":0.3944000005722046},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.38989999890327454},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.383899986743927},{"id":"https://openalex.org/keywords/image-fusion","display_name":"Image fusion","score":0.3801000118255615}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8140000104904175},{"id":"https://openalex.org/C87360688","wikidata":"https://www.wikidata.org/wiki/Q740686","display_name":"Synthetic aperture radar","level":2,"score":0.7170000076293945},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7069000005722046},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5864999890327454},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.578499972820282},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.5253999829292297},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5023999810218811},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.46869999170303345},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.42419999837875366},{"id":"https://openalex.org/C142575187","wikidata":"https://www.wikidata.org/wiki/Q3358290","display_name":"Pyramid (geometry)","level":2,"score":0.3944000005722046},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.38989999890327454},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.383899986743927},{"id":"https://openalex.org/C69744172","wikidata":"https://www.wikidata.org/wiki/Q860822","display_name":"Image fusion","level":3,"score":0.3801000118255615},{"id":"https://openalex.org/C205711294","wikidata":"https://www.wikidata.org/wiki/Q176953","display_name":"Rendering (computer graphics)","level":2,"score":0.3578000068664551},{"id":"https://openalex.org/C10929652","wikidata":"https://www.wikidata.org/wiki/Q7279985","display_name":"Radar imaging","level":3,"score":0.326200008392334},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.3091999888420105},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.30230000615119934},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.3003999888896942},{"id":"https://openalex.org/C16345878","wikidata":"https://www.wikidata.org/wiki/Q107472979","display_name":"Orientation (vector space)","level":2,"score":0.2937000095844269},{"id":"https://openalex.org/C132094186","wikidata":"https://www.wikidata.org/wiki/Q641585","display_name":"Clutter","level":3,"score":0.28060001134872437},{"id":"https://openalex.org/C32022120","wikidata":"https://www.wikidata.org/wiki/Q797225","display_name":"Interference (communication)","level":3,"score":0.2712000012397766},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.2705000042915344},{"id":"https://openalex.org/C117623542","wikidata":"https://www.wikidata.org/wiki/Q621974","display_name":"Automatic target recognition","level":3,"score":0.2705000042915344},{"id":"https://openalex.org/C203595873","wikidata":"https://www.wikidata.org/wiki/Q25389927","display_name":"Change detection","level":2,"score":0.27000001072883606},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.2687999904155731},{"id":"https://openalex.org/C205372480","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"Image resolution","level":2,"score":0.2687999904155731},{"id":"https://openalex.org/C109094680","wikidata":"https://www.wikidata.org/wiki/Q6060432","display_name":"Inverse synthetic aperture radar","level":4,"score":0.262800008058548},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.26269999146461487},{"id":"https://openalex.org/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.2574000060558319},{"id":"https://openalex.org/C161840515","wikidata":"https://www.wikidata.org/wiki/Q186131","display_name":"Terrain","level":2,"score":0.2540999948978424},{"id":"https://openalex.org/C165838908","wikidata":"https://www.wikidata.org/wiki/Q736777","display_name":"Calibration","level":2,"score":0.2506999969482422}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/jstars.2025.3630995","is_oa":true,"landing_page_url":"https://doi.org/10.1109/jstars.2025.3630995","pdf_url":null,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","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:f3d9cac12f30456f9ea0c8eba35e3fd9","is_oa":true,"landing_page_url":"https://doaj.org/article/f3d9cac12f30456f9ea0c8eba35e3fd9","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 18, Pp 28856-28872 (2025)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/jstars.2025.3630995","is_oa":true,"landing_page_url":"https://doi.org/10.1109/jstars.2025.3630995","pdf_url":null,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","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/G928701077","display_name":null,"funder_award_id":"62373247","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":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"To":[0],"address":[1],"the":[2,23,46,66,137,164,181],"challenges":[3],"in":[4,73,223],"synthetic":[5],"aperture":[6],"radar":[7],"(SAR)":[8],"ship":[9,75],"detection,":[10,76],"including":[11],"complex":[12],"target":[13,96,122],"back-grounds,":[14],"multi-scale":[15,33,48,58,80],"ships,":[16],"and":[17,57,101,120,160,166,177,204],"diverse":[18,121],"orientations,":[19],"while":[20,93],"also":[21],"meeting":[22],"lightweight":[24,32,41],"requirements":[25],"for":[26,221],"satellite-based":[27],"appli-cations,":[28],"we":[29,38,77,124],"propose":[30,78,125],"a":[31,40,53,79,126,192],"feature":[34,49,70,82,103],"fusion":[35,71,83],"network\u2014MSCF-Net.":[36],"First,":[37],"design":[39],"DRC2F":[42],"module":[43],"that":[44,152],"enhances":[45],"network's":[47],"extr-action":[50],"capability":[51],"through":[52,98],"two-stage":[54],"residual":[55],"mechanism":[56],"depth-wise":[59],"separable":[60],"dilated":[61],"convolutions.":[62],"Second,":[63],"to":[64,107,135,141,190],"overcome":[65],"limitations":[67],"of":[68,157,174,183,194],"traditional":[69],"methods":[72],"SAR":[74,113,216],"channel":[81],"pyramid":[84],"network,":[85],"MSCF-FPN,":[86],"which":[87],"effectively":[88],"sup-presses":[89],"background":[90],"noise":[91],"interference":[92],"highlighting":[94],"foreground":[95],"features":[97],"multi-modal":[99],"pooling":[100],"dynamic":[102],"calibration":[104],"mechanisms.":[105],"Finally,":[106],"further":[108],"improve":[109,136],"detection":[110,129,138,155,202],"accuracy":[111],"on":[112,163,214],"images":[114],"characterized":[115],"by":[116],"low":[117],"target-background":[118],"discriminability":[119],"orient-ations,":[123],"multi-branch":[127],"decoupled":[128],"head":[130],"integrated":[131],"with":[132,169],"receptive-field":[133],"attention":[134],"head's":[139],"capacity":[140],"perceive":[142],"spatial":[143],"as":[144,146],"well":[145],"orientation":[147],"information.":[148],"Experimental":[149],"results":[150],"demonstrate":[151],"MSCF-Net":[153,209],"achieves":[154],"accuracies":[156],"79.1%":[158],"(+4.9%)":[159],"92.1%":[161],"(+2%)":[162],"SRSDD-v1.0":[165],"HRSID,":[167],"respectively,":[168],"mean":[170],"average":[171],"precisions":[172],"(mAP)":[173],"71.7%":[175],"(+5.8%)":[176],"93.3%":[178],"(+0.2%).":[179],"Further-more,":[180],"number":[182],"model":[184,205],"parameters":[185],"is":[186],"decreased":[187],"from":[188],"10.89M":[189],"4.66M,":[191],"reduction":[193],"approximately":[195],"57.2%,":[196],"striking":[197],"an":[198],"effective":[199],"balance":[200],"between":[201],"performance":[203],"efficiency.":[206],"In":[207],"addition,":[208],"exhibits":[210],"robust":[211],"generalization":[212],"capabilities":[213],"large-scene":[215],"images,":[217],"rendering":[218],"it":[219],"well-suited":[220],"application":[222],"complicated":[224],"real-world":[225],"scenarios.":[226]},"counts_by_year":[],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2025-11-10T00:00:00"}