{"id":"https://openalex.org/W7165054390","doi":"https://doi.org/10.48550/arxiv.2606.17722","title":"GSPan: A Continuous Gaussian Primitive Representation for Arbitrary-Scale Pansharpening","display_name":"GSPan: A Continuous Gaussian Primitive Representation for Arbitrary-Scale Pansharpening","publication_year":2026,"publication_date":"2026-06-16","ids":{"openalex":"https://openalex.org/W7165054390","doi":"https://doi.org/10.48550/arxiv.2606.17722"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.17722","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.17722","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":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.2606.17722","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5138790132","display_name":"Fangyi Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Fangyi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030018760","display_name":"Xiaomin Yang","orcid":"https://orcid.org/0000-0002-5593-5108"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Xiaoyuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138779703","display_name":"Yixiao Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Yixiao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138782342","display_name":"Zongyang Sui","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sui, Zongyang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5048339079","display_name":"Kangqing Shen","orcid":"https://orcid.org/0000-0002-6136-5594"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shen, Kangqing","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5138797874","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":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/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9944999814033508,"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/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9944999814033508,"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/T11105","display_name":"Advanced Image Processing Techniques","score":0.0013000000035390258,"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"}},{"id":"https://openalex.org/T10111","display_name":"Remote Sensing in Agriculture","score":0.0012000000569969416,"subfield":{"id":"https://openalex.org/subfields/2303","display_name":"Ecology"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/multispectral-image","display_name":"Multispectral image","score":0.7361000180244446},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.6287999749183655},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.6274999976158142},{"id":"https://openalex.org/keywords/panchromatic-film","display_name":"Panchromatic film","score":0.5509999990463257},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5471000075340271},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5092999935150146},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.48579999804496765},{"id":"https://openalex.org/keywords/image-fusion","display_name":"Image fusion","score":0.4302000105381012},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.41620001196861267},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.3912000060081482}],"concepts":[{"id":"https://openalex.org/C173163844","wikidata":"https://www.wikidata.org/wiki/Q1761440","display_name":"Multispectral image","level":2,"score":0.7361000180244446},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7197999954223633},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6699000000953674},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.6287999749183655},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.6274999976158142},{"id":"https://openalex.org/C107445234","wikidata":"https://www.wikidata.org/wiki/Q280995","display_name":"Panchromatic film","level":3,"score":0.5509999990463257},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5471000075340271},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5092999935150146},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4966999888420105},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.48579999804496765},{"id":"https://openalex.org/C69744172","wikidata":"https://www.wikidata.org/wiki/Q860822","display_name":"Image fusion","level":3,"score":0.4302000105381012},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.41620001196861267},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4083999991416931},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3912000060081482},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.38339999318122864},{"id":"https://openalex.org/C205372480","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"Image resolution","level":2,"score":0.36890000104904175},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.3659000098705292},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.35359999537467957},{"id":"https://openalex.org/C121927907","wikidata":"https://www.wikidata.org/wiki/Q1952516","display_name":"Multiresolution analysis","level":5,"score":0.3499999940395355},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.3447999954223633},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.32690000534057617},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.320499986410141},{"id":"https://openalex.org/C65892221","wikidata":"https://www.wikidata.org/wiki/Q1113935","display_name":"Gaussian filter","level":3,"score":0.2922999858856201},{"id":"https://openalex.org/C166550679","wikidata":"https://www.wikidata.org/wiki/Q263400","display_name":"Gaussian network model","level":3,"score":0.28929999470710754},{"id":"https://openalex.org/C138268822","wikidata":"https://www.wikidata.org/wiki/Q1051925","display_name":"Resolution (logic)","level":2,"score":0.2766000032424927},{"id":"https://openalex.org/C151201525","wikidata":"https://www.wikidata.org/wiki/Q177239","display_name":"Limit (mathematics)","level":2,"score":0.27480000257492065},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.27219998836517334},{"id":"https://openalex.org/C2778971668","wikidata":"https://www.wikidata.org/wiki/Q5510284","display_name":"Fusion rules","level":4,"score":0.2718999981880188},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2662999927997589},{"id":"https://openalex.org/C2779219049","wikidata":"https://www.wikidata.org/wiki/Q852117","display_name":"Quadric","level":2,"score":0.2615000009536743},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.2551000118255615}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.17722","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.17722","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":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.2606.17722","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.17722","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":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":{"Pansharpening":[0],"aims":[1],"to":[2,79,111,198],"generate":[3],"high-resolution":[4],"multispectral":[5,11],"(HRMS)":[6],"images":[7,114],"by":[8],"fusing":[9],"low-resolution":[10],"(LRMS)":[12],"and":[13,59,86,99,139,157,179,195],"panchromatic":[14],"(PAN)":[15],"observations.":[16,88],"Most":[17],"existing":[18],"deep":[19],"learning":[20],"methods":[21],"treat":[22],"pansharpening":[23],"as":[24,57,94,192],"fixed-grid":[25,199],"prediction,":[26],"which":[27,132],"limits":[28],"scale":[29],"adaptation.":[30],"To":[31],"address":[32],"this,":[33],"we":[34],"propose":[35],"GSPan,":[36],"a":[37,66,73,95,126,136,173,193],"framework":[38],"that":[39,161],"introduces":[40],"2D":[41,61],"Gaussian":[42,62,189],"Splatting":[43],"(GS)":[44],"into":[45,101],"pansharpening.":[46,151],"Instead":[47],"of":[48,187],"directly":[49],"predicting":[50],"pixels,":[51],"GSPan":[52,110,162],"represents":[53],"band-wise":[54],"residual":[55,96,190],"details":[56],"continuous":[58,107,188],"learnable":[60],"primitives.":[63],"We":[64],"design":[65],"Dual-Stream":[67],"Hierarchical":[68],"Interaction":[69],"(DSHI)":[70],"architecture":[71],"with":[72],"Spatial-Spectral":[74],"Interactive":[75],"Attention":[76],"(SSIA)":[77],"module":[78],"estimate":[80],"these":[81],"primitives":[82,91,134],"from":[83],"complementary":[84],"PAN":[85],"MS":[87,104],"The":[89],"predicted":[90],"are":[92],"rendered":[93],"detail":[97],"field":[98],"injected":[100],"the":[102,141,145,185],"upsampled":[103],"image.":[105],"This":[106],"representation":[108],"allows":[109],"render":[112],"fused":[113,142],"on":[115,153],"arbitrary":[116],"target":[117,146],"sampling":[118],"grids":[119],"without":[120],"scale-specific":[121],"retraining.":[122],"It":[123],"further":[124],"enables":[125],"Scale-Decoupled":[127],"Asymmetric":[128],"Inference":[129],"(SDAI)":[130],"strategy,":[131],"estimates":[133],"at":[135,144],"reduced":[137],"resolution":[138,147],"renders":[140],"image":[143],"for":[148],"efficient":[149],"large-scene":[150],"Experiments":[152],"QuickBird,":[154],"GaoFen-2,":[155],"WorldView-3,":[156],"WorldView-3-4K":[158],"datasets":[159],"show":[160],"delivers":[163],"state-of-the-art":[164],"fusion":[165,180],"performance.":[166],"Moreover,":[167],"SDAI":[168],"markedly":[169],"accelerates":[170],"inference,":[171],"achieving":[172],"favorable":[174],"trade-off":[175],"between":[176],"computational":[177],"efficiency":[178],"quality.":[181],"Our":[182],"results":[183],"demonstrate":[184],"potential":[186],"representations":[191],"flexible":[194],"scale-decoupled":[196],"alternative":[197],"prediction.":[200]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-18T00:00:00"}