{"id":"https://openalex.org/W7154211287","doi":"https://doi.org/10.48550/arxiv.2604.11390","title":"Beyond Reconstruction: Reconstruction-to-Vector Diffusion for Hyperspectral Anomaly Detection","display_name":"Beyond Reconstruction: Reconstruction-to-Vector Diffusion for Hyperspectral Anomaly Detection","publication_year":2026,"publication_date":"2026-04-13","ids":{"openalex":"https://openalex.org/W7154211287","doi":"https://doi.org/10.48550/arxiv.2604.11390"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.11390","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.11390","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":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.11390","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5133562919","display_name":"Jijun Xiang","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Xiang, Jijun","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Wang, Tao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Tao","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Wang, Jiayi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Jiayi","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Wang, Pengxiang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Pengxiang","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Chen, Cheng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Cheng","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Wang, Nian","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Nian","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5133562919"],"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":0.9437999725341797,"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":0.9437999725341797,"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":0.00839999970048666,"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/T12157","display_name":"Geochemistry and Geologic Mapping","score":0.0034000000450760126,"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/anomaly-detection","display_name":"Anomaly detection","score":0.6510999798774719},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.617900013923645},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6162999868392944},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.6044999957084656},{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.5896000266075134},{"id":"https://openalex.org/keywords/scalar","display_name":"Scalar (mathematics)","score":0.5859000086784363},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.4163999855518341},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.376800000667572}],"concepts":[{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.6510999798774719},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.617900013923645},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6162999868392944},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6137999892234802},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.6044999957084656},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.5896000266075134},{"id":"https://openalex.org/C57691317","wikidata":"https://www.wikidata.org/wiki/Q1289248","display_name":"Scalar (mathematics)","level":2,"score":0.5859000086784363},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5297999978065491},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.4163999855518341},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.38429999351501465},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.376800000667572},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.3504999876022339},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.33980000019073486},{"id":"https://openalex.org/C529865628","wikidata":"https://www.wikidata.org/wiki/Q1790740","display_name":"Manifold (fluid mechanics)","level":2,"score":0.32670000195503235},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3003000020980835},{"id":"https://openalex.org/C55128770","wikidata":"https://www.wikidata.org/wiki/Q5275440","display_name":"Diffusion map","level":4,"score":0.2980000078678131},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.2921000123023987},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.2913999855518341},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.28619998693466187},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.28619998693466187},{"id":"https://openalex.org/C146357865","wikidata":"https://www.wikidata.org/wiki/Q1123245","display_name":"Stage (stratigraphy)","level":2,"score":0.26829999685287476},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.2671000063419342},{"id":"https://openalex.org/C150921843","wikidata":"https://www.wikidata.org/wiki/Q1170431","display_name":"Resampling","level":2,"score":0.2581000030040741},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.2556999921798706}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.11390","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.11390","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":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.11390","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.11390","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":"article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","score":0.6858518123626709,"display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"While":[0],"Hyperspectral":[1],"Anomaly":[2],"Detection":[3],"(HAD)":[4],"excels":[5],"at":[6,186],"identifying":[7],"sparse":[8],"targets":[9,149],"in":[10,17],"complex":[11],"scenes,":[12],"existing":[13],"models":[14],"remain":[15],"trapped":[16],"a":[18,59,65,74,78,94,116,123,129,140,172],"scalar":[19,26,161],"\"reconstruction-as-endpoint\"":[20],"paradigm.":[21,70],"This":[22],"reliance":[23],"on":[24,165],"ambiguous":[25],"residuals":[27],"consistently":[28],"triggers":[29],"sub-pixel":[30,113],"anomaly":[31],"vanishing":[32],"during":[33],"spatial":[34],"downsampling,":[35],"alongside":[36],"severe":[37],"confirmation":[38,87],"bias":[39,88],"when":[40],"unpurified":[41],"anomalies":[42],"corrupt":[43],"training":[44],"weights.":[45],"In":[46],"this":[47],"paper,":[48],"we":[49],"propose":[50],"Reconstruction-to-Vector":[51],"Diffusion":[52,124],"(R2VD),":[53],"which":[54],"fundamentally":[55],"redefines":[56],"reconstruction":[57],"as":[58],"manifold":[60],"purification":[61],"origin":[62],"to":[63,105],"establish":[64],"novel":[66],"residual-guided":[67],"generative":[68],"dynamics":[69],"Our":[71],"framework":[72],"introduces":[73],"four-stage":[75],"pipeline:":[76],"(1)":[77],"Physical":[79,130],"Prior":[80],"Extraction":[81],"(PPE)":[82],"stage":[83,99,121,145],"that":[84,146,169],"mitigates":[85],"early":[86],"via":[89],"dual-stream":[90],"statistical":[91],"guidance;":[92],"(2)":[93],"Guided":[95],"Manifold":[96],"Purification":[97],"(GMP)":[98],"utilizing":[100],"an":[101],"OmniContext":[102],"Autoencoder":[103],"(OCA)":[104],"extract":[106],"purified":[107],"residual":[108],"maps":[109],"while":[110],"preserving":[111],"fragile":[112],"topologies;":[114],"(3)":[115],"Residual":[117],"Score":[118],"Modeling":[119],"(RSM)":[120],"where":[122],"Transformer":[125],"(DiT),":[126],"guarded":[127],"by":[128,152],"Spectral":[131],"Firewall":[132],"(PSF),":[133],"effectively":[134],"isolates":[135],"cross-spectral":[136],"leakage;":[137],"and":[138,179],"(4)":[139],"Vector":[141],"Dynamics":[142],"Inference":[143],"(VDI)":[144],"robustly":[147],"decouples":[148],"from":[150],"backgrounds":[151],"evaluating":[153],"high-dimensional":[154],"vector":[155],"interference":[156],"patterns":[157],"instead":[158],"of":[159],"conventional":[160],"errors.":[162],"Comprehensive":[163],"evaluations":[164],"eight":[166],"datasets":[167],"confirm":[168],"R2VD":[170],"establishes":[171],"new":[173],"state-of-the-art,":[174],"delivering":[175],"exceptional":[176],"target":[177],"detectability":[178],"background":[180],"suppression.":[181],"The":[182],"code":[183],"is":[184],"available":[185],"https://github.com/Bondojijun/R2VD.":[187]},"counts_by_year":[],"updated_date":"2026-04-21T08:09:41.155169","created_date":"2026-04-15T00:00:00"}