{"id":"https://openalex.org/W7147738797","doi":"https://doi.org/10.1109/icvisp68610.2025.11451748","title":"Sparse Unmixing with Dictionary-Based Spatial-Spectral Low-Rank Decomposition for Hyperspectral Anomaly Detection","display_name":"Sparse Unmixing with Dictionary-Based Spatial-Spectral Low-Rank Decomposition for Hyperspectral Anomaly Detection","publication_year":2025,"publication_date":"2025-11-28","ids":{"openalex":"https://openalex.org/W7147738797","doi":"https://doi.org/10.1109/icvisp68610.2025.11451748"},"language":null,"primary_location":{"id":"doi:10.1109/icvisp68610.2025.11451748","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icvisp68610.2025.11451748","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 9th International Conference on Vision, Image and Signal Processing (ICVISP)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5021113910","display_name":"Zhigang Yang","orcid":"https://orcid.org/0000-0002-7268-5390"},"institutions":[{"id":"https://openalex.org/I198645480","display_name":"North China University of Water Resources and Electric Power","ror":"https://ror.org/03acrzv41","country_code":"CN","type":"education","lineage":["https://openalex.org/I198645480"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zhe Yang","raw_affiliation_strings":["Jiangxi University of Water Resources and Electric Power,Nanchang,China,330099"],"affiliations":[{"raw_affiliation_string":"Jiangxi University of Water Resources and Electric Power,Nanchang,China,330099","institution_ids":["https://openalex.org/I198645480"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100739632","display_name":"Fan Li","orcid":"https://orcid.org/0000-0002-7763-2968"},"institutions":[{"id":"https://openalex.org/I198645480","display_name":"North China University of Water Resources and Electric Power","ror":"https://ror.org/03acrzv41","country_code":"CN","type":"education","lineage":["https://openalex.org/I198645480"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fan Li","raw_affiliation_strings":["Jiangxi University of Water Resources and Electric Power,Nanchang,China,330099"],"affiliations":[{"raw_affiliation_string":"Jiangxi University of Water Resources and Electric Power,Nanchang,China,330099","institution_ids":["https://openalex.org/I198645480"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126834620","display_name":"Shaoquan Zhang","orcid":null},"institutions":[{"id":"https://openalex.org/I198645480","display_name":"North China University of Water Resources and Electric Power","ror":"https://ror.org/03acrzv41","country_code":"CN","type":"education","lineage":["https://openalex.org/I198645480"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shaoquan Zhang","raw_affiliation_strings":["Jiangxi University of Water Resources and Electric Power,Nanchang,China,330099"],"affiliations":[{"raw_affiliation_string":"Jiangxi University of Water Resources and Electric Power,Nanchang,China,330099","institution_ids":["https://openalex.org/I198645480"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132599363","display_name":"Yuyang Liu","orcid":null},"institutions":[{"id":"https://openalex.org/I198645480","display_name":"North China University of Water Resources and Electric Power","ror":"https://ror.org/03acrzv41","country_code":"CN","type":"education","lineage":["https://openalex.org/I198645480"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuyang Liu","raw_affiliation_strings":["Jiangxi University of Water Resources and Electric Power,Nanchang,China,330099"],"affiliations":[{"raw_affiliation_string":"Jiangxi University of Water Resources and Electric Power,Nanchang,China,330099","institution_ids":["https://openalex.org/I198645480"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Xiyu Chen","orcid":null},"institutions":[{"id":"https://openalex.org/I198645480","display_name":"North China University of Water Resources and Electric Power","ror":"https://ror.org/03acrzv41","country_code":"CN","type":"education","lineage":["https://openalex.org/I198645480"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiyu Chen","raw_affiliation_strings":["Jiangxi University of Water Resources and Electric Power,Nanchang,China,330099"],"affiliations":[{"raw_affiliation_string":"Jiangxi University of Water Resources and Electric Power,Nanchang,China,330099","institution_ids":["https://openalex.org/I198645480"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130776181","display_name":"Kexing Li","orcid":null},"institutions":[{"id":"https://openalex.org/I198645480","display_name":"North China University of Water Resources and Electric Power","ror":"https://ror.org/03acrzv41","country_code":"CN","type":"education","lineage":["https://openalex.org/I198645480"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kexing Li","raw_affiliation_strings":["Jiangxi University of Water Resources and Electric Power,Nanchang,China,330099"],"affiliations":[{"raw_affiliation_string":"Jiangxi University of Water Resources and Electric Power,Nanchang,China,330099","institution_ids":["https://openalex.org/I198645480"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5027239613","display_name":"Chengzhi Deng","orcid":"https://orcid.org/0000-0002-6762-5780"},"institutions":[{"id":"https://openalex.org/I198645480","display_name":"North China University of Water Resources and Electric Power","ror":"https://ror.org/03acrzv41","country_code":"CN","type":"education","lineage":["https://openalex.org/I198645480"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chengzhi Deng","raw_affiliation_strings":["Jiangxi University of Water Resources and Electric Power,Nanchang,China,330099"],"affiliations":[{"raw_affiliation_string":"Jiangxi University of Water Resources and Electric Power,Nanchang,China,330099","institution_ids":["https://openalex.org/I198645480"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5021113910"],"corresponding_institution_ids":["https://openalex.org/I198645480"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.74148151,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.994700014591217,"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":0.994700014591217,"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/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.0010999999940395355,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"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":0.00039999998989515007,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.8822000026702881},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.7387999892234802},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6942999958992004},{"id":"https://openalex.org/keywords/weighting","display_name":"Weighting","score":0.6863999962806702},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.5493000149726868},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.5163000226020813},{"id":"https://openalex.org/keywords/matrix","display_name":"Matrix (chemical analysis)","score":0.40389999747276306},{"id":"https://openalex.org/keywords/sparse-matrix","display_name":"Sparse matrix","score":0.3617999851703644},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.3594000041484833}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.8822000026702881},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.7387999892234802},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6942999958992004},{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.6863999962806702},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.5493000149726868},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5489000082015991},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.5163000226020813},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5020999908447266},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.40389999747276306},{"id":"https://openalex.org/C56372850","wikidata":"https://www.wikidata.org/wiki/Q1050404","display_name":"Sparse matrix","level":3,"score":0.3617999851703644},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.3594000041484833},{"id":"https://openalex.org/C124681953","wikidata":"https://www.wikidata.org/wiki/Q339062","display_name":"Decomposition","level":2,"score":0.35920000076293945},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3474000096321106},{"id":"https://openalex.org/C58237817","wikidata":"https://www.wikidata.org/wiki/Q5376204","display_name":"Endmember","level":3,"score":0.3465000092983246},{"id":"https://openalex.org/C70136482","wikidata":"https://www.wikidata.org/wiki/Q13583781","display_name":"A-weighting","level":3,"score":0.34040001034736633},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3368000090122223},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3269999921321869},{"id":"https://openalex.org/C42355184","wikidata":"https://www.wikidata.org/wiki/Q1361088","display_name":"Matrix decomposition","level":3,"score":0.3197999894618988},{"id":"https://openalex.org/C22789450","wikidata":"https://www.wikidata.org/wiki/Q420904","display_name":"Singular value decomposition","level":2,"score":0.31310001015663147},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.31130000948905945},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2962999939918518},{"id":"https://openalex.org/C185142706","wikidata":"https://www.wikidata.org/wiki/Q1134404","display_name":"Covariance matrix","level":2,"score":0.28619998693466187},{"id":"https://openalex.org/C2780551164","wikidata":"https://www.wikidata.org/wiki/Q2306599","display_name":"Column (typography)","level":3,"score":0.2856999933719635},{"id":"https://openalex.org/C105611402","wikidata":"https://www.wikidata.org/wiki/Q2976589","display_name":"Spectral clustering","level":3,"score":0.27959999442100525},{"id":"https://openalex.org/C2778514742","wikidata":"https://www.wikidata.org/wiki/Q16245026","display_name":"Abundance estimation","level":3,"score":0.2784000039100647},{"id":"https://openalex.org/C124066611","wikidata":"https://www.wikidata.org/wiki/Q28684319","display_name":"Sparse approximation","level":2,"score":0.2669999897480011},{"id":"https://openalex.org/C176641082","wikidata":"https://www.wikidata.org/wiki/Q2446767","display_name":"Spectral signature","level":2,"score":0.26010000705718994}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icvisp68610.2025.11451748","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icvisp68610.2025.11451748","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 9th International Conference on Vision, Image and Signal Processing (ICVISP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W1963659868","https://openalex.org/W1970099214","https://openalex.org/W2001298023","https://openalex.org/W2004491663","https://openalex.org/W2040078680","https://openalex.org/W2047870694","https://openalex.org/W2295576075","https://openalex.org/W2497075055","https://openalex.org/W2743180144","https://openalex.org/W2792167075","https://openalex.org/W2796629918","https://openalex.org/W2900199428","https://openalex.org/W2972614519","https://openalex.org/W3008839601","https://openalex.org/W3087883793","https://openalex.org/W4233367343","https://openalex.org/W4366678167","https://openalex.org/W4386432198","https://openalex.org/W4401797351","https://openalex.org/W4402259050"],"related_works":[],"abstract_inverted_index":{"For":[0],"the":[1,16,20,36,62,73,85,92,115,140,158],"processing":[2],"of":[3,38,41,91,117,139,153],"hyperspectral":[4,27,56,145],"image":[5],"(HSI)":[6],"information,":[7],"anomaly":[8,32,57,163],"detection":[9,33,58,164],"represents":[10],"a":[11,54,77,105],"well-recognized":[12],"research":[13],"hotspot.":[14],"Given":[15],"inherent":[17],"characteristic":[18],"that":[19,157],"background":[21],"and":[22,45,97,146,171],"anomalous":[23,131],"spectral":[24,44,118],"distributions":[25],"in":[26,166],"images":[28],"are":[29,133],"interleaved,":[30],"existing":[31],"algorithms":[34],"have":[35],"problem":[37],"insufficient":[39],"exploitation":[40],"HSI\u2019s":[42],"integrated":[43],"spatial":[46,123],"information.":[47,129],"Against":[48],"this":[49,51],"backdrop,":[50],"study":[52],"advances":[53],"novel":[55,106],"algorithm":[59,75,160],"underpinned":[60],"by":[61,127,135],"sparse":[63,80],"unmixing":[64,81],"model":[65,82,111],"coupled":[66],"with":[67,102,122],"dictionary-based":[68,107],"spatial-spectral":[69,108],"low-rank":[70,103,109],"decomposition.":[71],"Specifically,":[72],"proposed":[74,159],"constructs":[76],"spatially":[78],"constrained":[79],"to":[83,168],"obtain":[84],"abundance":[86,93],"matrix.":[87],"Then,":[88],"using":[89],"columns":[90],"matrix":[94,142],"as":[95],"samples":[96],"combining":[98],"mean":[99],"shift":[100],"clustering":[101],"representation,":[104],"decomposition":[110],"is":[112],"developed":[113],"through":[114],"incorporation":[116],"weighting":[119,124],"factors,":[120],"together":[121],"factors":[125],"informed":[126],"neighborhood":[128],"Finally,":[130],"targets":[132],"extracted":[134],"summing":[136],"each":[137],"column":[138],"error":[141],"derived":[143],"from":[144],"reconstructed":[147],"images.":[148],"Tests":[149],"on":[150],"real":[151],"datasets":[152],"different":[154],"scenarios":[155],"confirm":[156],"exhibits":[161],"optimal":[162],"performance":[165],"comparison":[167],"seven":[169],"classical":[170],"advanced":[172],"approaches.":[173]},"counts_by_year":[],"updated_date":"2026-04-17T18:11:37.981687","created_date":"2026-04-02T00:00:00"}