{"id":"https://openalex.org/W4387803215","doi":"https://doi.org/10.1109/igarss52108.2023.10281623","title":"Probability-Based Binary Attribute Weighted Prediction Network for SAR Image Classification","display_name":"Probability-Based Binary Attribute Weighted Prediction Network for SAR Image Classification","publication_year":2023,"publication_date":"2023-07-16","ids":{"openalex":"https://openalex.org/W4387803215","doi":"https://doi.org/10.1109/igarss52108.2023.10281623"},"language":"en","primary_location":{"id":"doi:10.1109/igarss52108.2023.10281623","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss52108.2023.10281623","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-article"},"type":"conference-paper","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/A5101453999","display_name":"Xiayang Xiao","orcid":"https://orcid.org/0000-0002-2797-7124"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiayang Xiao","raw_affiliation_strings":["Fudan University,Key Laboratory for Information Science of Electromagnetic Waves (MoE),Shanghai,China,200433"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fudan University,Key Laboratory for Information Science of Electromagnetic Waves (MoE),Shanghai,China,200433","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101921643","display_name":"Ziqi Ye","orcid":"https://orcid.org/0000-0001-5037-595X"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ziqi Ye","raw_affiliation_strings":["Fudan University,Key Laboratory for Information Science of Electromagnetic Waves (MoE),Shanghai,China,200433"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fudan University,Key Laboratory for Information Science of Electromagnetic Waves (MoE),Shanghai,China,200433","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5104250455","display_name":"Qiaoyu Liu","orcid":null},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qiaoyu Liu","raw_affiliation_strings":["Fudan University,Key Laboratory for Information Science of Electromagnetic Waves (MoE),Shanghai,China,200433"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fudan University,Key Laboratory for Information Science of Electromagnetic Waves (MoE),Shanghai,China,200433","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100405762","display_name":"Haipeng Wang","orcid":"https://orcid.org/0000-0003-1912-7143"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haipeng Wang","raw_affiliation_strings":["Fudan University,Key Laboratory for Information Science of Electromagnetic Waves (MoE),Shanghai,China,200433"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fudan University,Key Laboratory for Information Science of Electromagnetic Waves (MoE),Shanghai,China,200433","institution_ids":["https://openalex.org/I24943067"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I24943067"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"7038","last_page":"7041"},"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/T11038","display_name":"Advanced SAR Imaging Techniques","score":0.9980000257492065,"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/T11609","display_name":"Geophysical Methods and Applications","score":0.9825999736785889,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean 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/computer-science","display_name":"Computer science","score":0.7449778914451599},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7014133930206299},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6910685300827026},{"id":"https://openalex.org/keywords/synthetic-aperture-radar","display_name":"Synthetic aperture radar","score":0.6685177087783813},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6442320942878723},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.6032134890556335},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.4907442331314087},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4832766354084015},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.4783360958099365},{"id":"https://openalex.org/keywords/binary-number","display_name":"Binary number","score":0.44746431708335876},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3913050889968872},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.3086998164653778},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1336877942085266}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7449778914451599},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7014133930206299},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6910685300827026},{"id":"https://openalex.org/C87360688","wikidata":"https://www.wikidata.org/wiki/Q740686","display_name":"Synthetic aperture radar","level":2,"score":0.6685177087783813},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6442320942878723},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.6032134890556335},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.4907442331314087},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4832766354084015},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.4783360958099365},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.44746431708335876},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3913050889968872},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3086998164653778},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1336877942085266},{"id":"https://openalex.org/C94375191","wikidata":"https://www.wikidata.org/wiki/Q11205","display_name":"Arithmetic","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss52108.2023.10281623","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss52108.2023.10281623","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W93016980","https://openalex.org/W2038501427","https://openalex.org/W2098411764","https://openalex.org/W2601450892","https://openalex.org/W2618530766","https://openalex.org/W2753160622","https://openalex.org/W3184853743","https://openalex.org/W3213042253","https://openalex.org/W6735236233","https://openalex.org/W6743661861"],"related_works":["https://openalex.org/W2081900870","https://openalex.org/W2345479200","https://openalex.org/W2183306018","https://openalex.org/W2849310602","https://openalex.org/W4210966920","https://openalex.org/W2156233651","https://openalex.org/W2005234362","https://openalex.org/W2550009779","https://openalex.org/W1997235926","https://openalex.org/W4390143830"],"abstract_inverted_index":{"The":[0],"problem":[1],"of":[2,10,63,105],"insufficient":[3],"samples":[4],"has":[5],"been":[6],"limiting":[7],"the":[8,21,88,99,103,116,135],"performance":[9,104],"intelligent":[11],"interpretation":[12],"in":[13,39,108,118],"Synthetic":[14],"Aperture":[15],"Radar":[16],"(SAR)":[17],"images.":[18],"Humans":[19],"have":[20],"ability":[22],"to":[23,81],"recognize":[24],"new":[25],"instances":[26],"with":[27],"only":[28],"a":[29,36,49,64,93,139],"few":[30],"samples,":[31],"indicating":[32],"that":[33],"attributes":[34,77],"play":[35],"crucial":[37],"role":[38],"recognition.":[40],"Attributes":[41],"can":[42],"be":[43],"shared":[44],"across":[45],"categories":[46],"and":[47,68,122,138],"provide":[48],"distinctive":[50],"representation.":[51],"Motivated":[52],"by":[53,97,113],"this":[54,56],"fact,":[55],"paper":[57],"proposes":[58],"an":[59,69],"attribute-guided":[60],"network":[61],"consisting":[62],"base":[65],"classifier":[66,71],"(BC)":[67],"attribute":[70,100,120],"(AC).":[72],"Firstly,":[73],"we":[74],"design":[75],"binary":[76],"for":[78],"SAR":[79,109,141],"objects":[80],"enable":[82],"more":[83],"distinct":[84],"feature":[85,124],"representations.":[86],"Secondly,":[87],"images":[89,110],"are":[90],"mapped":[91],"into":[92],"semantic":[94],"embedding":[95,98],"space":[96,121],"vectors.":[101],"Finally,":[102],"few-shot":[106],"classification":[107],"is":[111,129],"improved":[112],"jointly":[114],"optimizing":[115],"loss":[117],"both":[119],"deep":[123],"space.":[125],"Our":[126],"attribute-based":[127],"framework":[128],"validated":[130],"through":[131],"ablation":[132],"experiments":[133],"on":[134],"MSTAR":[136],"dataset":[137],"self-built":[140],"aircraft":[142],"dataset.":[143]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-07-15T18:14:33.161393","created_date":"2025-10-10T00:00:00"}
