{"id":"https://openalex.org/W4293057086","doi":"https://doi.org/10.1109/tgrs.2022.3201628","title":"A Feature Decomposition-Based Method for Automatic Ship Detection Crossing Different Satellite SAR Images","display_name":"A Feature Decomposition-Based Method for Automatic Ship Detection Crossing Different Satellite SAR Images","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W4293057086","doi":"https://doi.org/10.1109/tgrs.2022.3201628"},"language":"en","primary_location":{"id":"doi:10.1109/tgrs.2022.3201628","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2022.3201628","pdf_url":null,"source":{"id":"https://openalex.org/S111326731","display_name":"IEEE Transactions on Geoscience and Remote Sensing","issn_l":"0196-2892","issn":["0196-2892","1558-0644"],"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 Transactions on Geoscience and Remote Sensing","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/A5019779567","display_name":"Siyuan Zhao","orcid":"https://orcid.org/0000-0002-8923-7877"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]},{"id":"https://openalex.org/I4210104252","display_name":"Air Force Engineering University","ror":"https://ror.org/00seraz22","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210104252"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Siyuan Zhao","raw_affiliation_strings":["Shanghai Key Laboratory of Intelligent Sensing and Recognition, Shanghai Jiao Tong University, Shanghai, China","an, China","Institute of Information and Navigation, Air Force Engineering University, Xi&#x2019"],"raw_orcid":"https://orcid.org/0000-0002-8923-7877","affiliations":[{"raw_affiliation_string":"Shanghai Key Laboratory of Intelligent Sensing and Recognition, Shanghai Jiao Tong University, Shanghai, China","institution_ids":["https://openalex.org/I183067930"]},{"raw_affiliation_string":"an, China","institution_ids":[]},{"raw_affiliation_string":"Institute of Information and Navigation, Air Force Engineering University, Xi&#x2019","institution_ids":["https://openalex.org/I4210104252"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100762950","display_name":"Ying Luo","orcid":"https://orcid.org/0000-0003-1460-4289"},"institutions":[{"id":"https://openalex.org/I4210104252","display_name":"Air Force Engineering University","ror":"https://ror.org/00seraz22","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210104252"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ying Luo","raw_affiliation_strings":["Institute of Information and Navigation, Air Force Engineering University, Xi’an, China","an, China","Collaborative Innovation Center of Information Sensing and Understanding, Xi&#x2019"],"raw_orcid":"https://orcid.org/0000-0003-1460-4289","affiliations":[{"raw_affiliation_string":"Institute of Information and Navigation, Air Force Engineering University, Xi’an, China","institution_ids":["https://openalex.org/I4210104252"]},{"raw_affiliation_string":"an, China","institution_ids":[]},{"raw_affiliation_string":"Collaborative Innovation Center of Information Sensing and Understanding, Xi&#x2019","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100375832","display_name":"Tao Zhang","orcid":"https://orcid.org/0000-0002-7192-5153"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tao Zhang","raw_affiliation_strings":["Shanghai Key Laboratory of Intelligent Sensing and Recognition, Shanghai Jiao Tong University, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0002-7192-5153","affiliations":[{"raw_affiliation_string":"Shanghai Key Laboratory of Intelligent Sensing and Recognition, Shanghai Jiao Tong University, Shanghai, China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027639981","display_name":"Weiwei Guo","orcid":"https://orcid.org/0000-0001-5037-0972"},"institutions":[{"id":"https://openalex.org/I116953780","display_name":"Tongji University","ror":"https://ror.org/03rc6as71","country_code":"CN","type":"education","lineage":["https://openalex.org/I116953780"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weiwei Guo","raw_affiliation_strings":["Tongji-MIT City Science Laboratory, Center of Digital Innovation, Tongji University, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0001-5037-0972","affiliations":[{"raw_affiliation_string":"Tongji-MIT City Science Laboratory, Center of Digital Innovation, Tongji University, Shanghai, China","institution_ids":["https://openalex.org/I116953780"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5059094652","display_name":"Zenghui Zhang","orcid":"https://orcid.org/0000-0002-1238-8538"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zenghui Zhang","raw_affiliation_strings":["Shanghai Key Laboratory of Intelligent Sensing and Recognition, Shanghai Jiao Tong University, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0002-1238-8538","affiliations":[{"raw_affiliation_string":"Shanghai Key Laboratory of Intelligent Sensing and Recognition, Shanghai Jiao Tong University, Shanghai, China","institution_ids":["https://openalex.org/I183067930"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":3.7128,"has_fulltext":false,"cited_by_count":33,"citation_normalized_percentile":{"value":0.93872725,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":"60","issue":null,"first_page":"1","last_page":"15"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9988999962806702,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9988999962806702,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9966999888420105,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9854999780654907,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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":0.7566074132919312},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7485136985778809},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.7007478475570679},{"id":"https://openalex.org/keywords/synthetic-aperture-radar","display_name":"Synthetic aperture radar","score":0.6637585759162903},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6479387283325195},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.6472228765487671},{"id":"https://openalex.org/keywords/minimum-bounding-box","display_name":"Minimum bounding box","score":0.5916333794593811},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5005033016204834},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4356759488582611},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.4146902561187744},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4143970310688019},{"id":"https://openalex.org/keywords/backbone-network","display_name":"Backbone network","score":0.41367584466934204},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.27741432189941406}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7566074132919312},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7485136985778809},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.7007478475570679},{"id":"https://openalex.org/C87360688","wikidata":"https://www.wikidata.org/wiki/Q740686","display_name":"Synthetic aperture radar","level":2,"score":0.6637585759162903},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6479387283325195},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.6472228765487671},{"id":"https://openalex.org/C147037132","wikidata":"https://www.wikidata.org/wiki/Q6865426","display_name":"Minimum bounding box","level":3,"score":0.5916333794593811},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5005033016204834},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4356759488582611},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.4146902561187744},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4143970310688019},{"id":"https://openalex.org/C88796919","wikidata":"https://www.wikidata.org/wiki/Q1142907","display_name":"Backbone network","level":2,"score":0.41367584466934204},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.27741432189941406},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"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/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tgrs.2022.3201628","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2022.3201628","pdf_url":null,"source":{"id":"https://openalex.org/S111326731","display_name":"IEEE Transactions on Geoscience and Remote Sensing","issn_l":"0196-2892","issn":["0196-2892","1558-0644"],"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 Transactions on Geoscience and Remote Sensing","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G3937783750","display_name":null,"funder_award_id":"62131020","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8681329955","display_name":"\u9762\u5411SAR\u56fe\u50cf\u76ee\u6807\u8bc6\u522b\u7684\u6df1\u5ea6\u5b66\u4e60\u53ef\u89e3\u91ca\u6027\u7814\u7a76","funder_award_id":"62071333","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":51,"referenced_works":["https://openalex.org/W7489460","https://openalex.org/W639708223","https://openalex.org/W1950727688","https://openalex.org/W2011247596","https://openalex.org/W2031489346","https://openalex.org/W2044531722","https://openalex.org/W2067927985","https://openalex.org/W2069829982","https://openalex.org/W2070858509","https://openalex.org/W2131953535","https://openalex.org/W2144506857","https://openalex.org/W2187089797","https://openalex.org/W2341861041","https://openalex.org/W2545583697","https://openalex.org/W2618530766","https://openalex.org/W2762294195","https://openalex.org/W2774244034","https://openalex.org/W2774618766","https://openalex.org/W2793141496","https://openalex.org/W2799646862","https://openalex.org/W2884367402","https://openalex.org/W2886335102","https://openalex.org/W2944029760","https://openalex.org/W2963927307","https://openalex.org/W2964115968","https://openalex.org/W2968634921","https://openalex.org/W2971512460","https://openalex.org/W2990641522","https://openalex.org/W2998115938","https://openalex.org/W3109679703","https://openalex.org/W3110011650","https://openalex.org/W3118957360","https://openalex.org/W3135497965","https://openalex.org/W3168553612","https://openalex.org/W3174367271","https://openalex.org/W3177345794","https://openalex.org/W3198862447","https://openalex.org/W3200733355","https://openalex.org/W3202277637","https://openalex.org/W3210839290","https://openalex.org/W4205294740","https://openalex.org/W4205629240","https://openalex.org/W4206750462","https://openalex.org/W4226443111","https://openalex.org/W4226456028","https://openalex.org/W4290043226","https://openalex.org/W6747837047","https://openalex.org/W6756824971","https://openalex.org/W6761139768","https://openalex.org/W6770444568","https://openalex.org/W6795725608"],"related_works":["https://openalex.org/W4327500857","https://openalex.org/W2965994363","https://openalex.org/W4311223090","https://openalex.org/W1689909837","https://openalex.org/W4205729548","https://openalex.org/W4301521271","https://openalex.org/W1895541646","https://openalex.org/W2953362004","https://openalex.org/W4298525700","https://openalex.org/W2952251705"],"abstract_inverted_index":{"In":[0,70],"the":[1,37,55,67,76,117,122,126,131,138,170,197,203,224,239],"face":[2],"of":[3,14,99,231,247],"Synthetic":[4],"Aperture":[5],"Radar":[6],"(SAR)":[7],"image":[8,82,233],"object":[9,46,63,164,229],"detection":[10,26,47,56,68,84,248],"with":[11],"different":[12,91],"distributions":[13],"training":[15],"and":[16,107,125,140,147,157,173,178,217,228],"test":[17],"data,":[18],"traditional":[19],"supervised":[20],"learning":[21],"methods":[22,244],"cannot":[23],"achieve":[24],"good":[25],"performance.":[27,249],"Domain":[28],"adaptation":[29],"(DA)":[30],"method":[31,85,241],"has":[32],"been":[33],"shown":[34],"to":[35,39,73,163],"have":[36],"ability":[38],"solve":[40,75],"this":[41,71],"problem,":[42,78],"but":[43],"existing":[44],"DA":[45,52,156,190],"algorithms":[48],"all":[49],"use":[50],"adversarial":[51,155,189,218],"theory":[53],"for":[54,116,154,192,206],"task,":[57],"which":[58],"is":[59,93,102],"ineffective":[60],"in":[61,66,245],"solving":[62],"regression":[64,165],"localization":[65],"task.":[69],"article,":[72],"better":[74],"above":[77],"an":[79],"automatic":[80],"SAR":[81,232],"ship":[83],"based":[86],"on":[87],"feature":[88,96,112,193,209],"decomposition":[89,182],"crossing":[90],"satellites":[92],"proposed.":[94],"The":[95],"extraction":[97],"layer":[98],"backbone":[100,204],"network":[101,205,216],"divided":[103],"into":[104,176,202],"low":[105,123],"level":[106,124],"high":[108,132],"level,":[109,133],"where":[110],"domain-invariant":[111,158],"extractors":[113],"are":[114,200],"designed":[115],"local":[118,139,171,198],"features":[119,128,142,152,159,172,175,199],"extracted":[120,129,143],"from":[121,130,144,196],"global":[127,141,174,208],"respectively.":[134],"We":[135],"argue":[136],"that":[137,161,238],"source":[145],"domain":[146,149,219],"target":[148],"contain":[150],"domain-specific":[151],"(DSF)":[153],"(DIF)":[160],"contribute":[162],"localization.":[166],"Then,":[167],"we":[168,187,221],"decompose":[169],"DSF":[177,185],"DIF":[179,195],"via":[180],"vector":[181],"method.":[183],"For":[184],"counterpart,":[186],"introduce":[188],"attention":[191],"alignment.":[194],"fused":[201],"high-level":[207],"extraction.":[210],"Finally,":[211],"by":[212],"using":[213],"region":[214],"proposal":[215],"classifier,":[220],"can":[222],"get":[223],"accurate":[225],"bounding":[226],"box":[227],"class":[230],"objects.":[234],"Extensive":[235],"experiments":[236],"prove":[237],"proposed":[240],"outperforms":[242],"state-of-the-art":[243],"terms":[246]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":9},{"year":2024,"cited_by_count":12},{"year":2023,"cited_by_count":8},{"year":2022,"cited_by_count":3}],"updated_date":"2026-07-22T07:51:19.307946","created_date":"2025-10-10T00:00:00"}