{"id":"https://openalex.org/W4415221941","doi":"https://doi.org/10.1109/jstars.2025.3622030","title":"Cross-Scale Window Attention for Cross-Domain Hyperspectral Image Classification","display_name":"Cross-Scale Window Attention for Cross-Domain Hyperspectral Image Classification","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4415221941","doi":"https://doi.org/10.1109/jstars.2025.3622030"},"language":"en","primary_location":{"id":"doi:10.1109/jstars.2025.3622030","is_oa":true,"landing_page_url":"https://doi.org/10.1109/jstars.2025.3622030","pdf_url":null,"source":{"id":"https://openalex.org/S117727964","display_name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","issn_l":"1939-1404","issn":["1939-1404","2151-1535"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/jstars.2025.3622030","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5031368188","display_name":"Yishu Peng","orcid":"https://orcid.org/0000-0003-3942-978X"},"institutions":[{"id":"https://openalex.org/I100286613","display_name":"Hunan Institute of Science and Technology","ror":"https://ror.org/044ysd349","country_code":"CN","type":"education","lineage":["https://openalex.org/I100286613"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yishu Peng","raw_affiliation_strings":["School of Information Science and Engineering, Hunan Institute of Science and Technology, Yueyang, China"],"raw_orcid":"https://orcid.org/0000-0003-3942-978X","affiliations":[{"raw_affiliation_string":"School of Information Science and Engineering, Hunan Institute of Science and Technology, Yueyang, China","institution_ids":["https://openalex.org/I100286613"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Xianping Fan","orcid":"https://orcid.org/0009-0007-9461-3244"},"institutions":[{"id":"https://openalex.org/I100286613","display_name":"Hunan Institute of Science and Technology","ror":"https://ror.org/044ysd349","country_code":"CN","type":"education","lineage":["https://openalex.org/I100286613"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xianping Fan","raw_affiliation_strings":["School of Information Science and Engineering, Hunan Institute of Science and Technology, Yueyang, China"],"raw_orcid":"https://orcid.org/0009-0007-9461-3244","affiliations":[{"raw_affiliation_string":"School of Information Science and Engineering, Hunan Institute of Science and Technology, Yueyang, China","institution_ids":["https://openalex.org/I100286613"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Siyuan Chen","orcid":"https://orcid.org/0000-0001-9100-9809"},"institutions":[{"id":"https://openalex.org/I100286613","display_name":"Hunan Institute of Science and Technology","ror":"https://ror.org/044ysd349","country_code":"CN","type":"education","lineage":["https://openalex.org/I100286613"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Siyuan Chen","raw_affiliation_strings":["School of Information Science and Engineering, Hunan Institute of Science and Technology, Yueyang, China"],"raw_orcid":"https://orcid.org/0000-0001-9100-9809","affiliations":[{"raw_affiliation_string":"School of Information Science and Engineering, Hunan Institute of Science and Technology, Yueyang, China","institution_ids":["https://openalex.org/I100286613"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5037075182","display_name":"Bing Tu","orcid":"https://orcid.org/0000-0001-5802-9496"},"institutions":[{"id":"https://openalex.org/I200845125","display_name":"Nanjing University of Information Science and Technology","ror":"https://ror.org/02y0rxk19","country_code":"CN","type":"education","lineage":["https://openalex.org/I200845125"]},{"id":"https://openalex.org/I4210128284","display_name":"Institute of Optics and Electronics, Chinese Academy of Sciences","ror":"https://ror.org/02bn68w95","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210128284"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bing Tu","raw_affiliation_strings":["Institute of Optics and Electronics, the State Key Laboratory Cultivation Base of Atmospheric Optoelectronic Detection and Information Fusion, Jiangsu International Joint Laboratory on Meteorological Photonics and Optoelectronic Detection, and Jiangsu Engineering Research Center for Intelligent Optoelectronic Sensing Technology of Atmosphere, Nanjing University of Information Science and Technology, Nanjing, China","Institute of Optics and Electronics, the Jiangsu Key Laboratory for Optoelectronic Detection of Atmosphere and Ocean, and the Jiangsu International Joint Laboratory on Meteorological Photonics and Optoelectronic Detection, Nanjing University of Information Science and Technology, Nanjing, Jiangsu, China"],"raw_orcid":"https://orcid.org/0000-0001-5802-9496","affiliations":[{"raw_affiliation_string":"Institute of Optics and Electronics, the State Key Laboratory Cultivation Base of Atmospheric Optoelectronic Detection and Information Fusion, Jiangsu International Joint Laboratory on Meteorological Photonics and Optoelectronic Detection, and Jiangsu Engineering Research Center for Intelligent Optoelectronic Sensing Technology of Atmosphere, Nanjing University of Information Science and Technology, Nanjing, China","institution_ids":["https://openalex.org/I4210128284","https://openalex.org/I200845125"]},{"raw_affiliation_string":"Institute of Optics and Electronics, the Jiangsu Key Laboratory for Optoelectronic Detection of Atmosphere and Ocean, and the Jiangsu International Joint Laboratory on Meteorological Photonics and Optoelectronic Detection, Nanjing University of Information Science and Technology, Nanjing, Jiangsu, China","institution_ids":["https://openalex.org/I200845125","https://openalex.org/I4210128284"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5031368188"],"corresponding_institution_ids":["https://openalex.org/I100286613"],"apc_list":{"value":1250,"currency":"USD","value_usd":1250},"apc_paid":{"value":1250,"currency":"USD","value_usd":1250},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.39611189,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"18","issue":null,"first_page":"27565","last_page":"27581"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9894000291824341,"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":0.9894000291824341,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.7538999915122986},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7281000018119812},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5939000248908997},{"id":"https://openalex.org/keywords/upsampling","display_name":"Upsampling","score":0.5909000039100647},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5881999731063843},{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.5401999950408936},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.525600016117096},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5152000188827515},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.4523000121116638}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7649999856948853},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.7538999915122986},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7281000018119812},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7103000283241272},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5939000248908997},{"id":"https://openalex.org/C110384440","wikidata":"https://www.wikidata.org/wiki/Q1143270","display_name":"Upsampling","level":3,"score":0.5909000039100647},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5881999731063843},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.5401999950408936},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.525600016117096},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5152000188827515},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.4523000121116638},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.4163999855518341},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.37389999628067017},{"id":"https://openalex.org/C2778751112","wikidata":"https://www.wikidata.org/wiki/Q835016","display_name":"Window (computing)","level":2,"score":0.35580000281333923},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.3529999852180481},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.3434000015258789},{"id":"https://openalex.org/C102392041","wikidata":"https://www.wikidata.org/wiki/Q592860","display_name":"Sliding window protocol","level":3,"score":0.3375000059604645},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.3287000060081482},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.32330000400543213},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.3224000036716461},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3100000023841858},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.296099990606308},{"id":"https://openalex.org/C199579030","wikidata":"https://www.wikidata.org/wiki/Q2851778","display_name":"Automatic image annotation","level":4,"score":0.28279998898506165},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.26829999685287476},{"id":"https://openalex.org/C193415008","wikidata":"https://www.wikidata.org/wiki/Q639681","display_name":"Network architecture","level":2,"score":0.26489999890327454},{"id":"https://openalex.org/C1667742","wikidata":"https://www.wikidata.org/wiki/Q10927554","display_name":"Image retrieval","level":3,"score":0.26030001044273376},{"id":"https://openalex.org/C53533937","wikidata":"https://www.wikidata.org/wiki/Q185020","display_name":"Histogram","level":3,"score":0.25850000977516174},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.2547000050544739},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.2506999969482422}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/jstars.2025.3622030","is_oa":true,"landing_page_url":"https://doi.org/10.1109/jstars.2025.3622030","pdf_url":null,"source":{"id":"https://openalex.org/S117727964","display_name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","issn_l":"1939-1404","issn":["1939-1404","2151-1535"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:e5fece4f616f4b9496a2c7902c32c7ac","is_oa":true,"landing_page_url":"https://doaj.org/article/e5fece4f616f4b9496a2c7902c32c7ac","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 18, Pp 27565-27581 (2025)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/jstars.2025.3622030","is_oa":true,"landing_page_url":"https://doi.org/10.1109/jstars.2025.3622030","pdf_url":null,"source":{"id":"https://openalex.org/S117727964","display_name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","issn_l":"1939-1404","issn":["1939-1404","2151-1535"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2150242690","display_name":null,"funder_award_id":"62375083","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":55,"referenced_works":["https://openalex.org/W1521436688","https://openalex.org/W1522734439","https://openalex.org/W2001298023","https://openalex.org/W2042873848","https://openalex.org/W2069231830","https://openalex.org/W2101711129","https://openalex.org/W2114819256","https://openalex.org/W2114828048","https://openalex.org/W2131697388","https://openalex.org/W2132648706","https://openalex.org/W2162698522","https://openalex.org/W2346557146","https://openalex.org/W2500751094","https://openalex.org/W2544803425","https://openalex.org/W2549139847","https://openalex.org/W2736600340","https://openalex.org/W2752782242","https://openalex.org/W2754507318","https://openalex.org/W2764276316","https://openalex.org/W2767805377","https://openalex.org/W2792827505","https://openalex.org/W2822065499","https://openalex.org/W2898204262","https://openalex.org/W2899747753","https://openalex.org/W2901397428","https://openalex.org/W2914331134","https://openalex.org/W2964105864","https://openalex.org/W2991616716","https://openalex.org/W3012399044","https://openalex.org/W3026298314","https://openalex.org/W3086338207","https://openalex.org/W3109713579","https://openalex.org/W3130365209","https://openalex.org/W3132524115","https://openalex.org/W3132867842","https://openalex.org/W3162112557","https://openalex.org/W3167332372","https://openalex.org/W3204445362","https://openalex.org/W3214821343","https://openalex.org/W4225146933","https://openalex.org/W4283760989","https://openalex.org/W4288054357","https://openalex.org/W4288076010","https://openalex.org/W4327522253","https://openalex.org/W4387777353","https://openalex.org/W4391547538","https://openalex.org/W4400724905","https://openalex.org/W4402674148","https://openalex.org/W4402743905","https://openalex.org/W4406856751","https://openalex.org/W4407937947","https://openalex.org/W4407948958","https://openalex.org/W4408606362","https://openalex.org/W4410294806","https://openalex.org/W4412030510"],"related_works":[],"abstract_inverted_index":{"Hyperspectral":[0],"image":[1,161],"classification":[2,35,162],"(HSIC)":[3],"is":[4,60,107,121,142,171],"crucial":[5],"in":[6,62,96,159],"several":[7],"fields,":[8],"but":[9],"relies":[10],"on":[11],"a":[12,41,49,110,116,166,237],"large":[13],"number":[14,239],"of":[15,23,29,181,192,240],"labeled":[16,241],"samples.":[17,242],"Due":[18],"to":[19,32,76,90,138,173,177,195],"the":[20,27,92,102,131,150,175,189,197,207,213,233],"high":[21],"cost":[22],"manual":[24],"annotation":[25],"and":[26,73,83,109,127,155,184,202,211],"scarcity":[28],"samples,":[30],"how":[31],"achieve":[33],"high-precision":[34],"with":[36,46,115,236],"limited":[37],"samples":[38,70],"has":[39],"become":[40],"major":[42],"challenge.":[43],"To":[44],"cope":[45],"this":[47,63],"challenge,":[48],"novel":[50],"framework":[51],"named":[52],"Cross-Scale":[53],"Window":[54],"Attention":[55],"Cross-Domain":[56],"Few-Shot":[57],"Learning":[58],"(CSWA)":[59],"proposed":[61],"paper.":[64],"Overall,":[65],"CSWA":[66,230],"interactively":[67],"utilizes":[68],"training":[69],"from":[71,134,206,224],"source":[72],"target":[74],"domains":[75],"learn":[77,199],"their":[78],"global":[79,139,156],"class":[80],"representations":[81,205],"separately,":[82],"adopts":[84],"an":[85],"inter-domain":[86],"information":[87],"interaction":[88],"strategy":[89,114],"alleviate":[91],"domain":[93],"bias":[94],"problem":[95],"cross-domain":[97],"learning.":[98],"For":[99],"feature":[100,129,193,204,219],"extraction,":[101],"local":[103,111,135,152],"window":[104,112],"attention":[105],"mechanism":[106],"applied,":[108],"partitioning":[113],"pyramidal":[117],"hierarchical":[118,132],"downsampling":[119],"structure":[120],"introduced.":[122],"Through":[123],"intra-window":[124],"self-attention":[125],"computation":[126],"cross-window":[128],"restructuring,":[130],"characterization":[133],"spectral\u2013spatial":[136,179,217],"features":[137,180],"contextual":[140],"semantics":[141],"gradually":[143],"realized,":[144],"which":[145,186],"effectively":[146],"takes":[147],"into":[148],"account":[149],"fine-grained":[151,218],"discriminative":[153,203],"properties":[154],"scene":[157],"consistency":[158],"hyperspectral":[160,209],"tasks.":[163],"In":[164],"addition,":[165],"cross-scale":[167],"convolutional":[168],"network":[169,198],"(XPConvNet)":[170],"designed":[172],"enable":[174],"model":[176],"capture":[178],"different":[182],"dimensions":[183],"scales,":[185],"acts":[187],"at":[188],"initial":[190],"stage":[191],"extraction":[194],"help":[196],"more":[200],"robust":[201],"rich":[208],"information,":[210],"lays":[212],"foundation":[214],"for":[215],"subsequent":[216],"extraction.":[220],"Extensive":[221],"experimental":[222],"results":[223],"three":[225],"benchmark":[226],"datasets":[227],"show":[228],"that":[229],"can":[231],"outperform":[232],"state-of-the-art":[234],"methods":[235],"small":[238]},"counts_by_year":[],"updated_date":"2026-05-06T08:25:59.206177","created_date":"2025-10-16T00:00:00"}
