{"id":"https://openalex.org/W4415971158","doi":"https://doi.org/10.1109/lgrs.2025.3629709","title":"Point-Supervised Oriented Ship Detection via Segment Anything Model for SAR Images","display_name":"Point-Supervised Oriented Ship Detection via Segment Anything Model for SAR Images","publication_year":2025,"publication_date":"2025-11-06","ids":{"openalex":"https://openalex.org/W4415971158","doi":"https://doi.org/10.1109/lgrs.2025.3629709"},"language":null,"primary_location":{"id":"doi:10.1109/lgrs.2025.3629709","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2025.3629709","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/A5022274090","display_name":"Qiwei Lin","orcid":"https://orcid.org/0009-0005-7637-9154"},"institutions":[{"id":"https://openalex.org/I4387153688","display_name":"Beijing Institute of Radio Metrology and Measurement","ror":"https://ror.org/02c8ctc21","country_code":null,"type":"company","lineage":["https://openalex.org/I15089104","https://openalex.org/I4387153688"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Qiwei Lin","raw_affiliation_strings":["Beijing Institute of Radio Measurement, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing Institute of Radio Measurement, Beijing, China","institution_ids":["https://openalex.org/I4387153688"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072832353","display_name":"Nanqing Liu","orcid":"https://orcid.org/0000-0001-7564-4896"},"institutions":[{"id":"https://openalex.org/I4800084","display_name":"Southwest Jiaotong University","ror":"https://ror.org/00hn7w693","country_code":"CN","type":"education","lineage":["https://openalex.org/I4800084"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Nanqing Liu","raw_affiliation_strings":["School of Information Science and Technology, Southwest Jiaotong University, Chengdu, China"],"affiliations":[{"raw_affiliation_string":"School of Information Science and Technology, Southwest Jiaotong University, Chengdu, China","institution_ids":["https://openalex.org/I4800084"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044811454","display_name":"Zhiyuan Tan","orcid":"https://orcid.org/0000-0001-5420-2554"},"institutions":[{"id":"https://openalex.org/I4387153688","display_name":"Beijing Institute of Radio Metrology and Measurement","ror":"https://ror.org/02c8ctc21","country_code":null,"type":"company","lineage":["https://openalex.org/I15089104","https://openalex.org/I4387153688"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhiyuan Tan","raw_affiliation_strings":["Beijing Institute of Radio Measurement, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing Institute of Radio Measurement, Beijing, China","institution_ids":["https://openalex.org/I4387153688"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100355854","display_name":"Yang Liu","orcid":"https://orcid.org/0000-0002-3295-2917"},"institutions":[{"id":"https://openalex.org/I4387153688","display_name":"Beijing Institute of Radio Metrology and Measurement","ror":"https://ror.org/02c8ctc21","country_code":null,"type":"company","lineage":["https://openalex.org/I15089104","https://openalex.org/I4387153688"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yang Liu","raw_affiliation_strings":["Beijing Institute of Radio Measurement, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing Institute of Radio Measurement, Beijing, China","institution_ids":["https://openalex.org/I4387153688"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5019928183","display_name":"Qinghua Long","orcid":null},"institutions":[{"id":"https://openalex.org/I4387153688","display_name":"Beijing Institute of Radio Metrology and Measurement","ror":"https://ror.org/02c8ctc21","country_code":null,"type":"company","lineage":["https://openalex.org/I15089104","https://openalex.org/I4387153688"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qinghua Long","raw_affiliation_strings":["Beijing Institute of Radio Measurement, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing Institute of Radio Measurement, Beijing, China","institution_ids":["https://openalex.org/I4387153688"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5022274090"],"corresponding_institution_ids":["https://openalex.org/I4387153688"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.35425141,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"23","issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.6819999814033508,"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":0.6819999814033508,"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/T10801","display_name":"Synthetic Aperture Radar (SAR) Applications and Techniques","score":0.17980000376701355,"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/T11038","display_name":"Advanced SAR Imaging Techniques","score":0.07159999758005142,"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":0.616599977016449},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.6044999957084656},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.5866000056266785},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.5720000267028809},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.5443000197410583},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.510200023651123},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.4805000126361847},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.42320001125335693}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8163999915122986},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6437000036239624},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.621999979019165},{"id":"https://openalex.org/C87360688","wikidata":"https://www.wikidata.org/wiki/Q740686","display_name":"Synthetic aperture radar","level":2,"score":0.616599977016449},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.6044999957084656},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5866000056266785},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.5720000267028809},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.5443000197410583},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.510200023651123},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.4805000126361847},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.42320001125335693},{"id":"https://openalex.org/C190839683","wikidata":"https://www.wikidata.org/wiki/Q2448197","display_name":"Train","level":2,"score":0.39089998602867126},{"id":"https://openalex.org/C10929652","wikidata":"https://www.wikidata.org/wiki/Q7279985","display_name":"Radar imaging","level":3,"score":0.3878999948501587},{"id":"https://openalex.org/C50151734","wikidata":"https://www.wikidata.org/wiki/Q1759577","display_name":"Matched filter","level":3,"score":0.38199999928474426},{"id":"https://openalex.org/C2778999744","wikidata":"https://www.wikidata.org/wiki/Q7208292","display_name":"Point target","level":3,"score":0.37880000472068787},{"id":"https://openalex.org/C32022120","wikidata":"https://www.wikidata.org/wiki/Q797225","display_name":"Interference (communication)","level":3,"score":0.36730000376701355},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3249000012874603},{"id":"https://openalex.org/C32283439","wikidata":"https://www.wikidata.org/wiki/Q1407014","display_name":"Radar tracker","level":3,"score":0.3163999915122986},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.2946000099182129},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.28299999237060547},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.26330000162124634},{"id":"https://openalex.org/C29265498","wikidata":"https://www.wikidata.org/wiki/Q7047719","display_name":"Noise measurement","level":3,"score":0.2605000138282776},{"id":"https://openalex.org/C100675267","wikidata":"https://www.wikidata.org/wiki/Q1371624","display_name":"Background noise","level":2,"score":0.2556999921798706}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lgrs.2025.3629709","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2025.3629709","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":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W2088049833","https://openalex.org/W2964979676","https://openalex.org/W2982770724","https://openalex.org/W3038948729","https://openalex.org/W3174873843","https://openalex.org/W3200733355","https://openalex.org/W4214648418","https://openalex.org/W4390874575","https://openalex.org/W4392719505","https://openalex.org/W4402754222","https://openalex.org/W4402754284","https://openalex.org/W4406322219"],"related_works":[],"abstract_inverted_index":{"Conventional":[0],"ship":[1,23,76,88,144],"detection":[2,122,145],"methods":[3],"for":[4],"SAR":[5,45,59,67],"images":[6,68],"typically":[7],"require":[8],"complete":[9],"annotations,":[10],"which":[11],"are":[12],"time-consuming.":[13],"Hence,":[14],"we":[15,51,79],"propose":[16],"a":[17,97,117],"framework":[18],"named":[19],"PointOSD-SAR":[20],"that":[21,133],"trains":[22],"detectors":[24],"using":[25],"only":[26],"point":[27,91],"labels.":[28],"We":[29,95],"identify":[30],"two":[31,53],"main":[32],"challenges":[33],"in":[34,75],"point-supervised":[35],"detection:":[36],"the":[37,66,81,109,125,151,157],"lack":[38],"of":[39],"scale":[40],"information":[41,127],"and":[42,72,107,154],"interference":[43],"from":[44,90,128],"imaging":[46],"noise.":[47],"To":[48],"tackle":[49],"these,":[50],"design":[52],"collaborative":[54],"processes:":[55],"1)":[56],"Point-to-Mask:":[57],"A":[58],"Ship":[60],"Enhancement":[61],"(SSE)":[62],"module":[63],"first":[64],"preprocesses":[65],"to":[69,86,103],"reduce":[70],"noise":[71],"fill":[73],"gaps":[74],"structures.":[77],"Then,":[78],"fine-tune":[80],"Segment":[82],"Anything":[83],"Model":[84],"(SAM)":[85],"obtain":[87],"masks":[89,106,111],"prompts.":[92],"2)":[93],"Mask-to-HBox-to-RBox:":[94],"introduce":[96],"Noise":[98],"Mask":[99],"Filtering":[100],"(NMF)":[101],"process":[102],"filter":[104],"low-quality":[105],"convert":[108],"remaining":[110],"into":[112],"Horizontal":[113],"Boxes":[114],"(HBox).":[115],"Finally,":[116],"weakly":[118,141],"supervised":[119,142],"oriented":[120,143],"object":[121],"model":[123],"learns":[124],"angle":[126],"HBoxes.":[129],"Extensive":[130],"experiments":[131],"show":[132],"our":[134],"method":[135],"achieves":[136],"state-of-the-art":[137],"performance":[138],"among":[139],"recent":[140],"methods,":[146],"with":[147],"62.8%":[148],"accuracy":[149],"on":[150,156],"SSDD":[152],"dataset":[153],"55.3%":[155],"HRSID":[158],"dataset.":[159]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-11-06T00:00:00"}