{"id":"https://openalex.org/W7160439493","doi":"https://doi.org/10.48550/arxiv.2605.03371","title":"SoDa2: Single-Stage Open-Set Domain Adaptation via Decoupled Alignment for Cross-Scene Hyperspectral Image Classification","display_name":"SoDa2: Single-Stage Open-Set Domain Adaptation via Decoupled Alignment for Cross-Scene Hyperspectral Image Classification","publication_year":2026,"publication_date":"2026-05-05","ids":{"openalex":"https://openalex.org/W7160439493","doi":"https://doi.org/10.48550/arxiv.2605.03371"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.03371","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.03371","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","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.2605.03371","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5135520974","display_name":"Yiwen Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Liu, Yiwen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100325515","display_name":"Minghua Wang","orcid":"https://orcid.org/0000-0001-5715-130X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Minghua","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135475777","display_name":"Jing Yao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yao, Jing","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135474353","display_name":"Xin Zhao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhao, Xin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5135490754","display_name":"Gemine Vivone","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Vivone, Gemine","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5135520974"],"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/T10689","display_name":"Remote-Sensing Image Classification","score":0.8738999962806702,"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":0.8738999962806702,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.11190000176429749,"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/T10057","display_name":"Face and Expression Recognition","score":0.0010999999940395355,"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/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7364000082015991},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.7093999981880188},{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.5810999870300293},{"id":"https://openalex.org/keywords/cosine-similarity","display_name":"Cosine similarity","score":0.5266000032424927},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5134000182151794},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4787999987602234},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.4672999978065491},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.459199994802475},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.4505999982357025}],"concepts":[{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7364000082015991},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.7093999981880188},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6883000135421753},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6018999814987183},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.5810999870300293},{"id":"https://openalex.org/C2780762811","wikidata":"https://www.wikidata.org/wiki/Q1784941","display_name":"Cosine similarity","level":3,"score":0.5266000032424927},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5134000182151794},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4787999987602234},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.4672999978065491},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.459199994802475},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.4505999982357025},{"id":"https://openalex.org/C2776434776","wikidata":"https://www.wikidata.org/wiki/Q19246213","display_name":"Domain adaptation","level":3,"score":0.4325000047683716},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.3644999861717224},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.36340001225471497},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3596999943256378},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.34369999170303345},{"id":"https://openalex.org/C2221639","wikidata":"https://www.wikidata.org/wiki/Q2877","display_name":"Discrete cosine transform","level":3,"score":0.32010000944137573},{"id":"https://openalex.org/C166550679","wikidata":"https://www.wikidata.org/wiki/Q263400","display_name":"Gaussian network model","level":3,"score":0.29269999265670776},{"id":"https://openalex.org/C31510193","wikidata":"https://www.wikidata.org/wiki/Q1192553","display_name":"Facial recognition system","level":3,"score":0.2897999882698059},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2872999906539917},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.2806999981403351},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.2667999863624573},{"id":"https://openalex.org/C2776036281","wikidata":"https://www.wikidata.org/wiki/Q48769818","display_name":"Constraint (computer-aided design)","level":2,"score":0.2662000060081482},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.2651999890804291},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.263700008392334},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.26330000162124634},{"id":"https://openalex.org/C178009071","wikidata":"https://www.wikidata.org/wiki/Q93344","display_name":"Trigonometric functions","level":2,"score":0.26260000467300415},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.2565999925136566}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.03371","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.03371","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.03371","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.03371","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[{"score":0.8072319030761719,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Cross-scene":[0],"hyperspectral":[1],"image":[2],"(HSI)":[3],"classification":[4,57,229],"stands":[5],"as":[6],"a":[7,93,188],"fundamental":[8],"research":[9],"topic":[10],"in":[11,27],"remote":[12],"sensing,":[13],"with":[14,99],"extensive":[15],"applications":[16],"spanning":[17],"various":[18],"fields.":[19],"Owing":[20],"to":[21,47,114,140,168,192],"the":[22,28,32,70,116,136,143,147,151,194,200],"inclusion":[23],"of":[24,34,73,210,218],"unknown":[25,183,211],"categories":[26],"target":[29,154],"domain":[30,35,41,65,96],"and":[31,122,126,146,153,173,182,231],"existence":[33],"shift":[36,66],"across":[37],"different":[38],"scenes,":[39],"open-set":[40,54,95,205,235],"adaptation":[42,97],"techniques":[43],"are":[44],"commonly":[45],"employed":[46],"address":[48,87],"cross-scene":[49,55,104,236],"HSI":[50,56,105,219],"classification.":[51,106],"However,":[52],"existing":[53],"methods":[58],"still":[59],"face":[60],"two":[61,201],"critical":[62],"challenges:":[63],"(1)":[64],"issues":[67],"arising":[68],"from":[69,118],"direct":[71],"alignment":[72,101,133],"mixed":[74],"spectral-spatial":[75],"features;":[76],"(2)":[77],"high":[78],"computational":[79],"costs":[80],"caused":[81],"by":[82],"two-stage":[83],"training":[84],"strategies.":[85],"To":[86],"these":[88],"issues,":[89],"this":[90],"paper":[91],"proposes":[92],"single-stage":[94,163],"method":[98],"decoupled":[100,132],"(SoDa$^2$)":[102],"for":[103,177,234],"A":[107,161],"contribution-aware":[108],"dual-modality":[109],"feature":[110,202],"extraction":[111],"is":[112,166],"customized":[113],"disentangle":[115],"characteristics":[117],"spectral":[119,144],"sequence":[120],"signals":[121],"spatial":[123,148],"details,":[124],"selectively":[125],"adaptively":[127],"enhancing":[128],"discriminative":[129],"features.":[130,160],"The":[131],"module":[134],"minimizes":[135],"Maximum":[137],"Mean":[138],"Discrepancy":[139],"independently":[141],"reduce":[142],"discrepancy":[145,149],"between":[150,180,199],"source":[152],"domains,":[155],"extracting":[156],"more":[157],"fine-grained":[158],"domain-invariant":[159],"cost-effective":[162],"dual-branch":[164],"framework":[165,186],"designed":[167],"learn":[169],"MMD-constrainted":[170],"aligned":[171],"features":[172,176],"constraint-free":[174],"intrinsic":[175],"adaptive":[178],"distinction":[179],"known":[181],"classes.":[184,212],"This":[185],"employs":[187],"Gaussian":[189],"Mixture":[190],"Model":[191],"model":[193,232],"squared":[195],"cosine":[196],"similarity":[197],"distribution":[198],"types,":[203],"enabling":[204],"recognition":[206],"without":[207],"prior":[208],"knowledge":[209],"Extensive":[213],"experiments":[214],"on":[215],"three":[216],"groups":[217],"datasets":[220],"demonstrate":[221],"that":[222],"SoDa$^2$":[223],"outperforms":[224],"state-of-the-art":[225],"methods,":[226],"achieving":[227],"superior":[228],"accuracy":[230],"transferability":[233],"tasks.":[237]},"counts_by_year":[],"updated_date":"2026-05-07T06:12:12.454206","created_date":"2026-05-07T00:00:00"}
