{"id":"https://openalex.org/W7166836778","doi":"https://doi.org/10.48550/arxiv.2606.31668","title":"SAMBA: A Scatter-Guided Masked Bidirectional Mamba Foundation Model for SAR Target Recognition","display_name":"SAMBA: A Scatter-Guided Masked Bidirectional Mamba Foundation Model for SAR Target Recognition","publication_year":2026,"publication_date":"2026-06-30","ids":{"openalex":"https://openalex.org/W7166836778","doi":"https://doi.org/10.48550/arxiv.2606.31668"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.31668","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.31668","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2606.31668","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5139724645","display_name":"Ke Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Ke","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139742780","display_name":"Xiaoyi Pan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pan, Xiaoyi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019193732","display_name":"Zhaoyu Gu","orcid":"https://orcid.org/0000-0003-2702-3539"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gu, Zhaoyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139846180","display_name":"Xiaofeng Ai","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ai, Xiaofeng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073136550","display_name":"Zhewei Xu","orcid":"https://orcid.org/0000-0003-4718-6175"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu, Zhiming","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139809614","display_name":"Feng Zhao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhao, Feng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5139758036","display_name":"Shunping Xiao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xiao, Shunping","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"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":0.9713000059127808,"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":0.9713000059127808,"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":0.014800000004470348,"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/T10891","display_name":"Radar Systems and Signal Processing","score":0.002099999925121665,"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":0.6608999967575073},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.4320000112056732},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.3732999861240387},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.35019999742507935},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.3488999903202057},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.33550000190734863},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.33500000834465027},{"id":"https://openalex.org/keywords/automatic-target-recognition","display_name":"Automatic target recognition","score":0.3330000042915344},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.3188000023365021}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7645999789237976},{"id":"https://openalex.org/C87360688","wikidata":"https://www.wikidata.org/wiki/Q740686","display_name":"Synthetic aperture radar","level":2,"score":0.6608999967575073},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5135999917984009},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.4320000112056732},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.3732999861240387},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.35019999742507935},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3488999903202057},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.33550000190734863},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.33500000834465027},{"id":"https://openalex.org/C117623542","wikidata":"https://www.wikidata.org/wiki/Q621974","display_name":"Automatic target recognition","level":3,"score":0.3330000042915344},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.3188000023365021},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.3172000050544739},{"id":"https://openalex.org/C27158222","wikidata":"https://www.wikidata.org/wiki/Q5532422","display_name":"Generalizability theory","level":2,"score":0.31439998745918274},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.30399999022483826},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3009999990463257},{"id":"https://openalex.org/C61270487","wikidata":"https://www.wikidata.org/wiki/Q476843","display_name":"Interception","level":2,"score":0.29420000314712524},{"id":"https://openalex.org/C2777402240","wikidata":"https://www.wikidata.org/wiki/Q6783436","display_name":"Masking (illustration)","level":2,"score":0.29409998655319214},{"id":"https://openalex.org/C109094680","wikidata":"https://www.wikidata.org/wiki/Q6060432","display_name":"Inverse synthetic aperture radar","level":4,"score":0.28619998693466187},{"id":"https://openalex.org/C48145219","wikidata":"https://www.wikidata.org/wiki/Q1335365","display_name":"Security token","level":2,"score":0.28380000591278076},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.27810001373291016},{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.2750999927520752},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2718000113964081},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.2711000144481659},{"id":"https://openalex.org/C132094186","wikidata":"https://www.wikidata.org/wiki/Q641585","display_name":"Clutter","level":3,"score":0.27090001106262207},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.27079999446868896},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.25369998812675476},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.25040000677108765}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.31668","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.31668","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.31668","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.31668","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Synthetic":[0],"aperture":[1],"radar":[2],"automatic":[3],"target":[4,73],"recognition":[5],"(SAR":[6],"ATR)":[7],"is":[8,19],"critical":[9],"for":[10,71],"Earth":[11],"observation":[12],"and":[13,41,165,195,219],"defense,":[14],"but":[15],"its":[16,209],"practical":[17],"deployment":[18],"constrained":[20],"by":[21,106],"scarce":[22],"annotated":[23],"training":[24],"data.":[25],"Self-supervised":[26],"pre-training":[27,68,137,143,156],"alleviates":[28],"this":[29],"label":[30],"bottleneck,":[31],"yet":[32],"prevailing":[33],"Transformer":[34,166],"architectures":[35],"incur":[36],"prohibitive":[37],"quadratic":[38],"computational":[39,93],"complexity,":[40],"conventional":[42],"universal":[43],"masking":[44,103,172],"neglects":[45],"the":[46,111,141,170,177,183],"unique":[47],"electromagnetic":[48],"scattering":[49],"properties":[50],"intrinsic":[51,116],"to":[52,91,126,139],"SAR":[53,72,107,214],"imagery.":[54],"To":[55],"address":[56],"these":[57],"limitations,":[58],"we":[59],"propose":[60],"SAMBA":[61,149,199],"(Scattering-Guided":[62],"Bidirectional":[63],"Mamba),":[64],"an":[65],"efficient":[66],"self-supervised":[67],"foundation":[69],"model":[70],"interpretation.":[74],"Our":[75],"framework":[76],"features":[77],"three":[78],"core":[79],"innovations:":[80],"(i)":[81],"a":[82,87,96,120,134],"linear-complexity":[83],"Mamba":[84],"encoder":[85],"with":[86,114,158,169],"mid-sequence":[88],"class":[89],"token":[90],"mitigate":[92],"bottlenecks;":[94],"(ii)":[95],"three-level":[97],"hierarchical":[98],"Scattering-Guided":[99],"Masked":[100],"Autoencoder":[101],"(SG-MAE)":[102],"strategy":[104,173,180],"guided":[105],"physical":[108],"priors,":[109],"aligning":[110],"pretext":[112],"task":[113],"SAR's":[115],"imaging":[117],"mechanism;":[118],"(iii)":[119],"lightweight":[121],"SpatialMix":[122],"feature":[123,129],"interaction":[124],"module":[125],"enhance":[127],"cross-region":[128],"fusion.":[130],"We":[131],"also":[132],"design":[133],"two-stage":[135],"cross-domain":[136],"pipeline":[138],"optimize":[140],"overall":[142],"process.":[144],"Extensive":[145],"evaluations":[146],"demonstrate":[147],"that":[148],"consistently":[150],"delivers":[151],"superior":[152],"performance":[153,203],"across":[154,212],"all":[155],"configurations,":[157],"substantially":[159],"fewer":[160],"parameters":[161],"than":[162],"both":[163],"CNN":[164],"baselines.":[167],"Compared":[168],"default":[171],"in":[174],"standard":[175],"MAE,":[176],"proposed":[178],"SG-MAE":[179],"further":[181],"boosts":[182],"model's":[184],"few-shot":[185],"transfer":[186],"capability.":[187],"Benchmarking":[188],"on":[189,204],"seven":[190],"downstream":[191],"datasets":[192],"covering":[193],"classification":[194],"detection":[196],"tasks":[197],"shows":[198],"achieves":[200],"state-of-the-art":[201],"(SOTA)":[202],"most":[205],"metrics,":[206],"fully":[207],"validating":[208],"robust":[210],"generalizability":[211],"diverse":[213],"interpretation":[215],"tasks.":[216],"Source":[217],"code":[218],"pre-trained":[220],"weights":[221],"are":[222],"publicly":[223],"available":[224],"at":[225],"https://github.com/mynswkk/SAMBA.":[226]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-07-02T00:00:00"}