{"id":"https://openalex.org/W7134943636","doi":"https://doi.org/10.48550/arxiv.2603.09037","title":"WS-Net: Weak-Signal Representation Learning and Gated Abundance Reconstruction for Hyperspectral Unmixing via State-Space and Weak Signal Attention Fusion","display_name":"WS-Net: Weak-Signal Representation Learning and Gated Abundance Reconstruction for Hyperspectral Unmixing via State-Space and Weak Signal Attention Fusion","publication_year":2026,"publication_date":"2026-03-10","ids":{"openalex":"https://openalex.org/W7134943636","doi":"https://doi.org/10.48550/arxiv.2603.09037"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.09037","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.09037","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.2603.09037","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5120324815","display_name":"Zekun Long","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Long, Zekun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5046987655","display_name":"Ali Zia","orcid":"https://orcid.org/0000-0003-4819-6666"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zia, Ali","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128740564","display_name":"Guanyiman Fu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fu, Guanyiman","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089640431","display_name":"Vivien Rolland","orcid":"https://orcid.org/0000-0002-9232-1287"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rolland, Vivien","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5128787168","display_name":"Jun Zhou","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhou, Jun","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/T10689","display_name":"Remote-Sensing Image Classification","score":0.864799976348877,"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":0.864799976348877,"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/T11659","display_name":"Advanced Image Fusion Techniques","score":0.08479999750852585,"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.014700000174343586,"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/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.8510000109672546},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6539000272750854},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.553600013256073},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5476999878883362},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.4927000105381012},{"id":"https://openalex.org/keywords/classification-of-discontinuities","display_name":"Classification of discontinuities","score":0.4860000014305115},{"id":"https://openalex.org/keywords/signal","display_name":"SIGNAL (programming language)","score":0.4611000120639801},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.44040000438690186}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.8510000109672546},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7062000036239624},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6539000272750854},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6171000003814697},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.553600013256073},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5476999878883362},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.4927000105381012},{"id":"https://openalex.org/C15627037","wikidata":"https://www.wikidata.org/wiki/Q541961","display_name":"Classification of discontinuities","level":2,"score":0.4860000014305115},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.4611000120639801},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.44040000438690186},{"id":"https://openalex.org/C19768560","wikidata":"https://www.wikidata.org/wiki/Q320727","display_name":"Dependency (UML)","level":2,"score":0.4142000079154968},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.3650999963283539},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.36419999599456787},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.325300008058548},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.310699999332428},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2937999963760376},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.2865999937057495},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2745000123977661},{"id":"https://openalex.org/C70958404","wikidata":"https://www.wikidata.org/wiki/Q7512728","display_name":"Signal reconstruction","level":4,"score":0.2632000148296356},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.25589999556541443},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2547999918460846},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.2506999969482422}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.09037","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.09037","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.2603.09037","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.09037","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":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Weak":[0,38,77],"spectral":[1,56,85],"responses":[2],"in":[3,16,136],"hyperspectral":[4,164],"images":[5],"are":[6],"often":[7],"obscured":[8],"by":[9],"dominant":[10,105],"endmembers":[11],"and":[12,37,54,106,113,120,133,138,158],"sensor":[13],"noise,":[14],"resulting":[15],"inaccurate":[17],"abundance":[18],"estimation.":[19],"This":[20],"paper":[21],"introduces":[22],"WS-Net,":[23],"a":[24,45,59,64,76,156],"deep":[25],"unmixing":[26],"framework":[27,142],"specifically":[28],"designed":[29],"to":[30,101,131],"address":[31],"weak-signal":[32,163],"collapse":[33],"through":[34],"state-space":[35,66],"modelling":[36],"Signal":[39,78],"Attention":[40,79],"fusion.":[41],"The":[42,141],"network":[43],"features":[44],"multi-resolution":[46],"wavelet-fused":[47],"encoder":[48],"that":[49,62,81],"captures":[50],"both":[51,93],"high-frequency":[52],"discontinuities":[53],"smooth":[55],"variations":[57],"with":[58],"hybrid":[60],"backbone":[61],"integrates":[63],"Mamba":[65],"branch":[67,80],"for":[68,150,162],"efficient":[69,160],"long-range":[70],"dependency":[71],"modelling.":[72],"It":[73],"also":[74],"incorporates":[75],"selectively":[82],"enhances":[83],"low-similarity":[84],"cues.":[86],"A":[87],"learnable":[88],"gating":[89],"mechanism":[90],"adaptively":[91],"fuses":[92],"representations,":[94],"while":[95],"the":[96],"decoder":[97],"leverages":[98],"KL-divergence-based":[99],"regularisation":[100],"enforce":[102],"separability":[103],"between":[104],"weak":[107,151],"endmembers.":[108],"Experiments":[109],"on":[110],"one":[111],"simulated":[112],"two":[114],"real":[115],"datasets":[116],"(synthetic":[117],"dataset,":[118],"Samson,":[119],"Apex)":[121],"demonstrate":[122],"consistent":[123],"improvements":[124],"over":[125],"six":[126],"state-of-the-art":[127],"baselines,":[128],"achieving":[129],"up":[130],"55%":[132],"63%":[134],"reductions":[135],"RMSE":[137],"SAD,":[139],"respectively.":[140],"maintains":[143],"stable":[144],"accuracy":[145],"under":[146],"low-SNR":[147],"conditions,":[148],"particularly":[149],"endmembers,":[152],"establishing":[153],"WS-Net":[154],"as":[155],"robust":[157],"computationally":[159],"benchmark":[161],"unmixing.":[165]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-03-12T00:00:00"}