{"id":"https://openalex.org/W7140191465","doi":"https://doi.org/10.48550/arxiv.2603.21565","title":"Rethinking SAR ATR: A Target-Aware Frequency-Spatial Enhancement Framework with Noise-Resilient Knowledge Guidance","display_name":"Rethinking SAR ATR: A Target-Aware Frequency-Spatial Enhancement Framework with Noise-Resilient Knowledge Guidance","publication_year":2026,"publication_date":"2026-03-23","ids":{"openalex":"https://openalex.org/W7140191465","doi":"https://doi.org/10.48550/arxiv.2603.21565"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.21565","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.21565","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.21565","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Lin, Yansong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lin, Yansong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Cheng, Zihan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cheng, Zihan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Wang, Jielei","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Jielei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Lua, Guoming","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lua, Guoming","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Cui, Zongyong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cui, Zongyong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11038","display_name":"Advanced SAR Imaging Techniques","score":0.9656000137329102,"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"}},"topics":[{"id":"https://openalex.org/T11038","display_name":"Advanced SAR Imaging Techniques","score":0.9656000137329102,"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":0.00930000003427267,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.0071000000461936,"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/synthetic-aperture-radar","display_name":"Synthetic aperture radar","score":0.6384000182151794},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.588100016117096},{"id":"https://openalex.org/keywords/salient","display_name":"Salient","score":0.48339998722076416},{"id":"https://openalex.org/keywords/speckle-noise","display_name":"Speckle noise","score":0.4350000023841858},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.4101000130176544},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.37929999828338623},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.36390000581741333},{"id":"https://openalex.org/keywords/automatic-target-recognition","display_name":"Automatic target recognition","score":0.3630000054836273},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.35019999742507935},{"id":"https://openalex.org/keywords/radar-imaging","display_name":"Radar imaging","score":0.3465000092983246}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.72079998254776},{"id":"https://openalex.org/C87360688","wikidata":"https://www.wikidata.org/wiki/Q740686","display_name":"Synthetic aperture radar","level":2,"score":0.6384000182151794},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6025999784469604},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.588100016117096},{"id":"https://openalex.org/C2780719617","wikidata":"https://www.wikidata.org/wiki/Q1030752","display_name":"Salient","level":2,"score":0.48339998722076416},{"id":"https://openalex.org/C180940675","wikidata":"https://www.wikidata.org/wiki/Q7575045","display_name":"Speckle noise","level":3,"score":0.4350000023841858},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.4101000130176544},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.38760000467300415},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.37929999828338623},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.36390000581741333},{"id":"https://openalex.org/C117623542","wikidata":"https://www.wikidata.org/wiki/Q621974","display_name":"Automatic target recognition","level":3,"score":0.3630000054836273},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.35019999742507935},{"id":"https://openalex.org/C10929652","wikidata":"https://www.wikidata.org/wiki/Q7279985","display_name":"Radar imaging","level":3,"score":0.3465000092983246},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3416000008583069},{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.33799999952316284},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.33320000767707825},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.33169999718666077},{"id":"https://openalex.org/C142575187","wikidata":"https://www.wikidata.org/wiki/Q3358290","display_name":"Pyramid (geometry)","level":2,"score":0.33059999346733093},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.32690000534057617},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.3249000012874603},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3140000104904175},{"id":"https://openalex.org/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.3109999895095825},{"id":"https://openalex.org/C102290492","wikidata":"https://www.wikidata.org/wiki/Q7575045","display_name":"Speckle pattern","level":2,"score":0.30979999899864197},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.30230000615119934},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.29910001158714294},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.28610000014305115},{"id":"https://openalex.org/C196216189","wikidata":"https://www.wikidata.org/wiki/Q2867","display_name":"Wavelet transform","level":3,"score":0.2806999981403351},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.2799000144004822},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.26989999413490295},{"id":"https://openalex.org/C2776960227","wikidata":"https://www.wikidata.org/wiki/Q2586354","display_name":"Knowledge transfer","level":2,"score":0.2685000002384186},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2639999985694885},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.2597000002861023},{"id":"https://openalex.org/C72279823","wikidata":"https://www.wikidata.org/wiki/Q1139726","display_name":"Impulse response","level":2,"score":0.2538999915122986}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.21565","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.21565","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.21565","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.21565","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[{"score":0.41186243295669556,"display_name":"Life below water","id":"https://metadata.un.org/sdg/14"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Synthetic":[0],"aperture":[1],"radar":[2],"automatic":[3],"target":[4,30,60,112,140],"recognition":[5,35,141],"(SAR":[6],"ATR)":[7],"is":[8,100],"of":[9,125,139],"considerable":[10],"importance":[11],"in":[12,24],"marine":[13],"navigation":[14],"and":[15,37,82,128,161,172],"disaster":[16],"monitoring.":[17],"However,":[18],"the":[19,104,122,132,137,147,169,195,206],"coherent":[20],"speckle":[21],"noise":[22],"inherent":[23],"SAR":[25,59],"imagery":[26],"often":[27],"obscures":[28],"salient":[29],"features,":[31],"leading":[32],"to":[33,102,107,118],"degraded":[34],"accuracy":[36],"limited":[38],"model":[39,196],"generalization.":[40,213],"To":[41],"address":[42],"this":[43,45],"issue,":[44],"paper":[46],"proposes":[47],"a":[48,66,88],"target-aware":[49],"frequency-spatial":[50,67],"enhancement":[51,71],"framework":[52,64,209],"with":[53,93,153,186],"noise-resilient":[54,129],"knowledge":[55,96],"guidance":[56],"(FSCE)":[57],"for":[58],"recognition.":[61],"The":[62,175],"proposed":[63,133,207],"incorporates":[65],"shallow":[68,76],"feature":[69],"adaptive":[70],"(DSAF)":[72],"module,":[73],"which":[74],"processes":[75],"features":[77],"through":[78],"spatial":[79],"multi-scale":[80],"convolution":[81],"frequency-domain":[83],"wavelet":[84],"convolution.":[85],"In":[86],"addition,":[87],"teacher-student":[89],"learning":[90],"paradigm":[91],"combined":[92],"an":[94],"online":[95],"distillation":[97],"method":[98],"(KD)":[99],"employed":[101],"guide":[103],"student":[105],"network":[106,151],"focus":[108],"more":[109],"effectively":[110],"on":[111,146,168,189],"regions,":[113],"thereby":[114],"enhancing":[115],"its":[116],"robustness":[117],"high-noise":[119],"backgrounds.":[120],"Through":[121],"collaborative":[123],"optimization":[124],"attention":[126],"transfer":[127],"representation":[130],"learning,":[131],"approach":[134],"significantly":[135,193],"improves":[136],"stability":[138],"under":[142],"noisy":[143],"conditions.":[144],"Based":[145],"FSCE":[148,208],"framework,":[149],"two":[150],"architectures":[152],"different":[154],"performance":[155,184],"emphases":[156],"are":[157,166],"developed:":[158],"lightweight":[159],"DSAFNet-M":[160,192],"high-precision":[162],"DSAFNet-L.":[163],"Extensive":[164],"experiments":[165],"conducted":[167],"MSTAR,":[170],"FUSARShip":[171],"OpenSARShip":[173],"datasets.":[174],"results":[176,203],"show":[177],"that":[178,205],"DSAFNet-L":[179],"achieves":[180],"competitive":[181],"or":[182],"superior":[183],"compared":[185],"various":[187],"methods":[188],"three":[190],"datasets;":[191],"reduces":[194],"complexity":[197],"while":[198],"maintaining":[199],"comparable":[200],"accuracy.":[201],"These":[202],"indicate":[204],"exhibits":[210],"strong":[211],"cross-model":[212]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-03-25T00:00:00"}
