{"id":"https://openalex.org/W7105702778","doi":"https://doi.org/10.1109/ijcnn64981.2025.11228275","title":"Fine-Grained Meta-Learning with Semantic Augmentation for SAR Change Detection","display_name":"Fine-Grained Meta-Learning with Semantic Augmentation for SAR Change Detection","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W7105702778","doi":"https://doi.org/10.1109/ijcnn64981.2025.11228275"},"language":null,"primary_location":{"id":"doi:10.1109/ijcnn64981.2025.11228275","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11228275","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","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":null,"display_name":"Rongfang Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Rongfang Wang","raw_affiliation_strings":["Xidian University,School of Artificial Intelligence,Xi&#x2019;an,China,710071"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xidian University,School of Artificial Intelligence,Xi&#x2019;an,China,710071","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Chenyu Liu","orcid":null},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chenyu Liu","raw_affiliation_strings":["Xidian University,School of Artificial Intelligence,Xi&#x2019;an,China,710071"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xidian University,School of Artificial Intelligence,Xi&#x2019;an,China,710071","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Libin Sun","orcid":null},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Libin Sun","raw_affiliation_strings":["Xidian University,School of Computer Science and Technology,Xi&#x2019;an,China,710071"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xidian University,School of Computer Science and Technology,Xi&#x2019;an,China,710071","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Liang Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liang Wang","raw_affiliation_strings":["Xidian University,School of Artificial Intelligence,Xi&#x2019;an,China,710071"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xidian University,School of Artificial Intelligence,Xi&#x2019;an,China,710071","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Vireak Dara Ly","orcid":null},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Vireak Dara Ly","raw_affiliation_strings":["Xidian University,School of Artificial Intelligence,Xi&#x2019;an,China,710071"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xidian University,School of Artificial Intelligence,Xi&#x2019;an,China,710071","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"last","author":{"id":null,"display_name":"Changzhe Jiao","orcid":null},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Changzhe Jiao","raw_affiliation_strings":["Xidian University,School of Artificial Intelligence,Xi&#x2019;an,China,710071"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xidian University,School of Artificial Intelligence,Xi&#x2019;an,China,710071","institution_ids":["https://openalex.org/I149594827"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I149594827"],"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":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.85589998960495,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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/T10689","display_name":"Remote-Sensing Image Classification","score":0.85589998960495,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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":0.08720000088214874,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.011900000274181366,"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/synthetic-aperture-radar","display_name":"Synthetic aperture radar","score":0.6287999749183655},{"id":"https://openalex.org/keywords/change-detection","display_name":"Change detection","score":0.5828999876976013},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5069000124931335},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.5041000247001648},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4767000079154968},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4375},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.429500013589859},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4032000005245209},{"id":"https://openalex.org/keywords/covariance","display_name":"Covariance","score":0.3919000029563904}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7598999738693237},{"id":"https://openalex.org/C87360688","wikidata":"https://www.wikidata.org/wiki/Q740686","display_name":"Synthetic aperture radar","level":2,"score":0.6287999749183655},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5914000272750854},{"id":"https://openalex.org/C203595873","wikidata":"https://www.wikidata.org/wiki/Q25389927","display_name":"Change detection","level":2,"score":0.5828999876976013},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5069000124931335},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.5041000247001648},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4767000079154968},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4375},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.429500013589859},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4032000005245209},{"id":"https://openalex.org/C178650346","wikidata":"https://www.wikidata.org/wiki/Q201984","display_name":"Covariance","level":2,"score":0.3919000029563904},{"id":"https://openalex.org/C21200559","wikidata":"https://www.wikidata.org/wiki/Q7451068","display_name":"Sensitivity (control systems)","level":2,"score":0.38909998536109924},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.38760000467300415},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3725999891757965},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3382999897003174},{"id":"https://openalex.org/C2781122975","wikidata":"https://www.wikidata.org/wiki/Q16928266","display_name":"Semantic feature","level":2,"score":0.33320000767707825},{"id":"https://openalex.org/C10929652","wikidata":"https://www.wikidata.org/wiki/Q7279985","display_name":"Radar imaging","level":3,"score":0.3239000141620636},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3075999915599823},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.290800005197525},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.2757999897003174},{"id":"https://openalex.org/C2778915421","wikidata":"https://www.wikidata.org/wiki/Q3643177","display_name":"Performance improvement","level":2,"score":0.27129998803138733},{"id":"https://openalex.org/C2780440489","wikidata":"https://www.wikidata.org/wiki/Q5227278","display_name":"Data-driven","level":2,"score":0.2711000144481659},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.26669999957084656},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.26019999384880066},{"id":"https://openalex.org/C193536780","wikidata":"https://www.wikidata.org/wiki/Q1513153","display_name":"Edge detection","level":4,"score":0.25459998846054077},{"id":"https://openalex.org/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.25380000472068787},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.2529999911785126}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn64981.2025.11228275","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11228275","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W2117539524","https://openalex.org/W2152185773","https://openalex.org/W2160544350","https://openalex.org/W2195857622","https://openalex.org/W2221448138","https://openalex.org/W2919115771","https://openalex.org/W2931790542","https://openalex.org/W2962793481","https://openalex.org/W2963351448","https://openalex.org/W2963626105","https://openalex.org/W2963691377","https://openalex.org/W2963767194","https://openalex.org/W3027059738","https://openalex.org/W3177230409","https://openalex.org/W4206218592","https://openalex.org/W4312443924"],"related_works":[],"abstract_inverted_index":{"Synthetic":[0],"Aperture":[1],"Radar":[2],"(SAR)":[3],"images":[4],"have":[5],"become":[6],"a":[7,56,75,90,100,109,140],"primary":[8],"data":[9,49,135,150],"source":[10],"for":[11,82,104,114],"change":[12,67,85],"detection":[13,97,171],"due":[14],"to":[15,26,98],"their":[16],"all-weather,":[17],"all-day":[18],"imaging":[19],"capability,":[20],"high":[21],"resolution,":[22],"and":[23,41,108,129,147,196],"strong":[24],"sensitivity":[25],"ground":[27],"surface":[28],"variations.":[29],"However,":[30],"the":[31,47,60,64,106,116,122,131,170,174,204,208,219],"complex":[32],"scattering":[33],"characteristics":[34],"of":[35,59,66,126,134,173,210],"SAR":[36,83,183],"make":[37],"distinguishing":[38],"between":[39],"changed":[40,52],"unchanged":[42],"areas":[43,53],"particularly":[44],"challenging.":[45],"Additionally,":[46],"long-tail":[48],"distribution,":[50],"where":[51],"constitute":[54],"only":[55],"small":[57],"portion":[58],"dataset,":[61],"further":[62,200],"exacerbates":[63],"difficulty":[65],"detection.":[68,86],"To":[69],"address":[70],"these":[71],"challenges,":[72],"we":[73,88,138],"propose":[74,89],"Fine-Grained":[76],"Meta-Learning":[77],"with":[78,111],"Semantic":[79],"Augmentation":[80],"method":[81,180,217],"image":[84],"First,":[87],"fine-grained":[91],"classification":[92],"strategy":[93,120],"based":[94],"on":[95,181],"edge":[96],"construct":[99],"small,":[101],"balanced":[102],"dataset":[103,110],"training":[105,115],"meta-learner":[107,146,153],"reduced":[112],"imbalance":[113,205],"backbone":[117,175],"classifier.":[118,176],"This":[119],"enhances":[121],"feature":[123],"learning":[124,163],"capability":[125,226],"hard-to-classify":[127],"samples":[128],"reduces":[130],"adverse":[132],"effects":[133],"imbalance.":[136],"Next,":[137],"design":[139],"lightweight":[141],"yet":[142],"effective":[143],"Multi-Layer":[144],"Perceptron-based":[145],"incorporate":[148],"semantic":[149,160],"augmentation.":[151],"The":[152],"automatically":[154],"augments":[155],"minority":[156],"classes":[157],"along":[158],"meaningful":[159],"directions":[161],"by":[162],"appropriate":[164],"class-wise":[165],"covariance":[166],"matrices,":[167],"thereby":[168],"improving":[169],"performance":[172,209,221],"We":[177],"evaluate":[178],"our":[179,216],"four":[182,192],"datasets":[184],"through":[185],"cross-dataset":[186],"experiments,":[187],"demonstrating":[188],"its":[189,224],"superiority":[190],"over":[191],"methods":[193,212],"in":[194,227],"effectiveness":[195],"robustness.":[197],"Imbalance":[198],"analysis":[199],"reveals":[201],"that":[202],"as":[203],"ratio":[206],"increases,":[207],"comparison":[211],"degrades":[213],"significantly,":[214],"whereas":[215],"exhibits":[218],"least":[220],"deterioration,":[222],"confirming":[223],"competent":[225],"handling":[228],"imbalanced":[229],"data.":[230]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-11-14T00:00:00"}
