{"id":"https://openalex.org/W2888751777","doi":"https://doi.org/10.3390/rs10091330","title":"Noise Removal Based on Tensor Modelling for Hyperspectral Image Classification","display_name":"Noise Removal Based on Tensor Modelling for Hyperspectral Image Classification","publication_year":2018,"publication_date":"2018-08-21","ids":{"openalex":"https://openalex.org/W2888751777","doi":"https://doi.org/10.3390/rs10091330","mag":"2888751777"},"language":"en","primary_location":{"id":"doi:10.3390/rs10091330","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs10091330","pdf_url":"https://www.mdpi.com/2072-4292/10/9/1330/pdf?version=1534856037","source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Remote Sensing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2072-4292/10/9/1330/pdf?version=1534856037","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5052863335","display_name":"Salah Bourennane","orcid":"https://orcid.org/0000-0002-4683-6685"},"institutions":[{"id":"https://openalex.org/I4210142724","display_name":"Centrale Marseille","ror":"https://ror.org/040baw385","country_code":"FR","type":"education","lineage":["https://openalex.org/I4210142724"]},{"id":"https://openalex.org/I4210125985","display_name":"Institut Fresnel","ror":"https://ror.org/03br1wy20","country_code":"FR","type":"facility","lineage":["https://openalex.org/I1294671590","https://openalex.org/I1294671590","https://openalex.org/I21491767","https://openalex.org/I4210095849","https://openalex.org/I4210125985","https://openalex.org/I4210142724"]},{"id":"https://openalex.org/I1294671590","display_name":"Centre National de la Recherche Scientifique","ror":"https://ror.org/02feahw73","country_code":"FR","type":"funder","lineage":["https://openalex.org/I1294671590"]},{"id":"https://openalex.org/I21491767","display_name":"Aix-Marseille Universit\u00e9","ror":"https://ror.org/035xkbk20","country_code":"FR","type":"education","lineage":["https://openalex.org/I21491767"]}],"countries":["FR"],"is_corresponding":true,"raw_author_name":"Salah Bourennane","raw_affiliation_strings":["Centrale Marseille, Institut Fresnel, Aix Marseille Universit\u00e9, CNRS, 13013 Marseille, France"],"affiliations":[{"raw_affiliation_string":"Centrale Marseille, Institut Fresnel, Aix Marseille Universit\u00e9, CNRS, 13013 Marseille, France","institution_ids":["https://openalex.org/I4210142724","https://openalex.org/I4210125985","https://openalex.org/I21491767","https://openalex.org/I1294671590"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066425527","display_name":"Caroline Fossati","orcid":null},"institutions":[{"id":"https://openalex.org/I4210142724","display_name":"Centrale Marseille","ror":"https://ror.org/040baw385","country_code":"FR","type":"education","lineage":["https://openalex.org/I4210142724"]},{"id":"https://openalex.org/I4210125985","display_name":"Institut Fresnel","ror":"https://ror.org/03br1wy20","country_code":"FR","type":"facility","lineage":["https://openalex.org/I1294671590","https://openalex.org/I1294671590","https://openalex.org/I21491767","https://openalex.org/I4210095849","https://openalex.org/I4210125985","https://openalex.org/I4210142724"]},{"id":"https://openalex.org/I21491767","display_name":"Aix-Marseille Universit\u00e9","ror":"https://ror.org/035xkbk20","country_code":"FR","type":"education","lineage":["https://openalex.org/I21491767"]},{"id":"https://openalex.org/I1294671590","display_name":"Centre National de la Recherche Scientifique","ror":"https://ror.org/02feahw73","country_code":"FR","type":"funder","lineage":["https://openalex.org/I1294671590"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Caroline Fossati","raw_affiliation_strings":["Centrale Marseille, Institut Fresnel, Aix Marseille Universit\u00e9, CNRS, 13013 Marseille, France"],"affiliations":[{"raw_affiliation_string":"Centrale Marseille, Institut Fresnel, Aix Marseille Universit\u00e9, CNRS, 13013 Marseille, France","institution_ids":["https://openalex.org/I4210142724","https://openalex.org/I4210125985","https://openalex.org/I21491767","https://openalex.org/I1294671590"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5013522069","display_name":"Tao Lin","orcid":"https://orcid.org/0000-0003-0230-7877"},"institutions":[{"id":"https://openalex.org/I21491767","display_name":"Aix-Marseille Universit\u00e9","ror":"https://ror.org/035xkbk20","country_code":"FR","type":"education","lineage":["https://openalex.org/I21491767"]},{"id":"https://openalex.org/I4210142724","display_name":"Centrale Marseille","ror":"https://ror.org/040baw385","country_code":"FR","type":"education","lineage":["https://openalex.org/I4210142724"]},{"id":"https://openalex.org/I1294671590","display_name":"Centre National de la Recherche Scientifique","ror":"https://ror.org/02feahw73","country_code":"FR","type":"funder","lineage":["https://openalex.org/I1294671590"]},{"id":"https://openalex.org/I4210125985","display_name":"Institut Fresnel","ror":"https://ror.org/03br1wy20","country_code":"FR","type":"facility","lineage":["https://openalex.org/I1294671590","https://openalex.org/I1294671590","https://openalex.org/I21491767","https://openalex.org/I4210095849","https://openalex.org/I4210125985","https://openalex.org/I4210142724"]},{"id":"https://openalex.org/I4210126997","display_name":"Software and Engineering Associates (United States)","ror":"https://ror.org/03qt0ma29","country_code":"US","type":"company","lineage":["https://openalex.org/I4210126997"]}],"countries":["FR","US"],"is_corresponding":false,"raw_author_name":"Tao Lin","raw_affiliation_strings":["Centrale Marseille, Institut Fresnel, Aix Marseille Universit\u00e9, CNRS, 13013 Marseille, France","Magma Design Automation (Santa Clara - United States)"],"affiliations":[{"raw_affiliation_string":"Centrale Marseille, Institut Fresnel, Aix Marseille Universit\u00e9, CNRS, 13013 Marseille, France","institution_ids":["https://openalex.org/I4210142724","https://openalex.org/I4210125985","https://openalex.org/I21491767","https://openalex.org/I1294671590"]},{"raw_affiliation_string":"Magma Design Automation (Santa Clara - United States)","institution_ids":["https://openalex.org/I4210126997"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5052863335"],"corresponding_institution_ids":["https://openalex.org/I1294671590","https://openalex.org/I21491767","https://openalex.org/I4210125985","https://openalex.org/I4210142724"],"apc_list":{"value":2500,"currency":"CHF","value_usd":2707},"apc_paid":{"value":2500,"currency":"CHF","value_usd":2707},"fwci":0.5318,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.71370506,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"10","issue":"9","first_page":"1330","last_page":"1330"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.9997000098228455,"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"}},"topics":[{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.9997000098228455,"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/T10689","display_name":"Remote-Sensing Image Classification","score":0.9980999827384949,"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":0.9940999746322632,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.6834865212440491},{"id":"https://openalex.org/keywords/gaussian-noise","display_name":"Gaussian noise","score":0.6545156836509705},{"id":"https://openalex.org/keywords/value-noise","display_name":"Value noise","score":0.6408924460411072},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.6303312182426453},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.6217246055603027},{"id":"https://openalex.org/keywords/salt-and-pepper-noise","display_name":"Salt-and-pepper noise","score":0.612356960773468},{"id":"https://openalex.org/keywords/gradient-noise","display_name":"Gradient noise","score":0.5839359164237976},{"id":"https://openalex.org/keywords/wiener-filter","display_name":"Wiener filter","score":0.5798372626304626},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5333340764045715},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4955712556838989},{"id":"https://openalex.org/keywords/additive-white-gaussian-noise","display_name":"Additive white Gaussian noise","score":0.49070680141448975},{"id":"https://openalex.org/keywords/noise-measurement","display_name":"Noise measurement","score":0.49008381366729736},{"id":"https://openalex.org/keywords/white-noise","display_name":"White noise","score":0.4507253170013428},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.43153226375579834},{"id":"https://openalex.org/keywords/median-filter","display_name":"Median filter","score":0.4107726812362671},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.37652865052223206},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.34524106979370117},{"id":"https://openalex.org/keywords/noise-floor","display_name":"Noise floor","score":0.3057975471019745},{"id":"https://openalex.org/keywords/image-processing","display_name":"Image processing","score":0.21163058280944824},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.15446189045906067},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.09507641196250916}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.6834865212440491},{"id":"https://openalex.org/C4199805","wikidata":"https://www.wikidata.org/wiki/Q2725903","display_name":"Gaussian noise","level":2,"score":0.6545156836509705},{"id":"https://openalex.org/C182163834","wikidata":"https://www.wikidata.org/wiki/Q2926529","display_name":"Value noise","level":5,"score":0.6408924460411072},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.6303312182426453},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.6217246055603027},{"id":"https://openalex.org/C113660513","wikidata":"https://www.wikidata.org/wiki/Q849379","display_name":"Salt-and-pepper noise","level":5,"score":0.612356960773468},{"id":"https://openalex.org/C200378446","wikidata":"https://www.wikidata.org/wiki/Q4147391","display_name":"Gradient noise","level":5,"score":0.5839359164237976},{"id":"https://openalex.org/C18537770","wikidata":"https://www.wikidata.org/wiki/Q25523","display_name":"Wiener filter","level":2,"score":0.5798372626304626},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5333340764045715},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4955712556838989},{"id":"https://openalex.org/C169334058","wikidata":"https://www.wikidata.org/wiki/Q353292","display_name":"Additive white Gaussian noise","level":3,"score":0.49070680141448975},{"id":"https://openalex.org/C29265498","wikidata":"https://www.wikidata.org/wiki/Q7047719","display_name":"Noise measurement","level":3,"score":0.49008381366729736},{"id":"https://openalex.org/C112633086","wikidata":"https://www.wikidata.org/wiki/Q381287","display_name":"White noise","level":2,"score":0.4507253170013428},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.43153226375579834},{"id":"https://openalex.org/C55352655","wikidata":"https://www.wikidata.org/wiki/Q304247","display_name":"Median filter","level":4,"score":0.4107726812362671},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.37652865052223206},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.34524106979370117},{"id":"https://openalex.org/C187612029","wikidata":"https://www.wikidata.org/wiki/Q17083130","display_name":"Noise floor","level":4,"score":0.3057975471019745},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.21163058280944824},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.15446189045906067},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.09507641196250916}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.3390/rs10091330","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs10091330","pdf_url":"https://www.mdpi.com/2072-4292/10/9/1330/pdf?version=1534856037","source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Remote Sensing","raw_type":"journal-article"},{"id":"pmh:oai:HAL:hal-02475482v1","is_oa":true,"landing_page_url":"https://hal.science/hal-02475482","pdf_url":null,"source":{"id":"https://openalex.org/S4306402512","display_name":"HAL (Le Centre pour la Communication Scientifique Directe)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1294671590","host_organization_name":"Centre National de la Recherche Scientifique","host_organization_lineage":["https://openalex.org/I1294671590"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Remote Sensing, 2018, 10 (9), pp.1330. ⟨10.3390/rs10091330⟩","raw_type":"Journal articles"},{"id":"pmh:oai:doaj.org/article:7de1bb5bbf0c4900a31f5ddb5a3ee622","is_oa":true,"landing_page_url":"https://doaj.org/article/7de1bb5bbf0c4900a31f5ddb5a3ee622","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Remote Sensing, Vol 10, Iss 9, p 1330 (2018)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/2072-4292/10/9/1330/","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/rs10091330","pdf_url":null,"source":{"id":"https://openalex.org/S4306400947","display_name":"MDPI (MDPI AG)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210097602","host_organization_name":"Multidisciplinary Digital Publishing Institute (Switzerland)","host_organization_lineage":["https://openalex.org/I4210097602"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Remote Sensing; Volume 10; Issue 9; Pages: 1330","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/rs10091330","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs10091330","pdf_url":"https://www.mdpi.com/2072-4292/10/9/1330/pdf?version=1534856037","source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Remote Sensing","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","score":0.41999998688697815,"display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2888751777.pdf","grobid_xml":"https://content.openalex.org/works/W2888751777.grobid-xml"},"referenced_works_count":37,"referenced_works":["https://openalex.org/W16095134","https://openalex.org/W994592074","https://openalex.org/W1599593286","https://openalex.org/W1600959198","https://openalex.org/W1940223307","https://openalex.org/W1985242206","https://openalex.org/W1985832050","https://openalex.org/W2013912476","https://openalex.org/W2018010187","https://openalex.org/W2018282388","https://openalex.org/W2024165284","https://openalex.org/W2051877122","https://openalex.org/W2074342013","https://openalex.org/W2086022711","https://openalex.org/W2100846049","https://openalex.org/W2109747527","https://openalex.org/W2114341361","https://openalex.org/W2129891925","https://openalex.org/W2132984323","https://openalex.org/W2136035751","https://openalex.org/W2142192675","https://openalex.org/W2144451417","https://openalex.org/W2147353113","https://openalex.org/W2154255569","https://openalex.org/W2156069890","https://openalex.org/W2156366154","https://openalex.org/W2159469858","https://openalex.org/W2160484748","https://openalex.org/W2161037015","https://openalex.org/W2161073299","https://openalex.org/W2404400936","https://openalex.org/W2767887540","https://openalex.org/W2788572501","https://openalex.org/W2809635958","https://openalex.org/W6600634069","https://openalex.org/W6626122873","https://openalex.org/W6753019925"],"related_works":["https://openalex.org/W2027616686","https://openalex.org/W2120405331","https://openalex.org/W2116036791","https://openalex.org/W2306622391","https://openalex.org/W4379184580","https://openalex.org/W2020266320","https://openalex.org/W2980586888","https://openalex.org/W2144975018","https://openalex.org/W2262019715","https://openalex.org/W2810312662"],"abstract_inverted_index":{"With":[0],"the":[1,8,29,33,36,56,61,69,77,85,99,194,205,208,224,230,237,255,271,274,282,286,291,297],"current":[2],"state-of-the-art":[3],"computer":[4],"aided":[5],"manufacturing":[6],"tools,":[7],"spatial":[9,37],"resolution":[10,38],"of":[11,28,35,53,63],"hyperspectral":[12,101,195,300],"sensors":[13],"is":[14,66,201,211,227,268,318],"becoming":[15],"increasingly":[16],"higher":[17],"thus":[18,104],"making":[19,105],"it":[20,106,317],"easy":[21],"to":[22,44,84,109,121,132,166,172,181,203,213],"obtain":[23],"much":[24],"more":[25,107],"detailed":[26],"information":[27],"scene":[30],"captured.":[31],"However,":[32],"improvement":[34],"also":[39,252,309],"brings":[40],"new":[41,118,184],"challenging":[42],"problems":[43],"address":[45],"with":[46,123],"signal":[47,57,124],"dependent":[48,67,125],"photon":[49,64,86,93,135,189],"noise":[50,65,79,96,127,138,160,196,206,210,218,283,323],"being":[51],"one":[52],"them.":[54],"Unlike":[55],"independent":[58],"thermal":[59,95,137,191],"noise,":[60],"variance":[62],"on":[68,147,296],"signal,":[70],"therefore":[71],"many":[72],"denoising":[73,119,243,256,275],"methods":[74],"developed":[75],"for":[76,187,326],"stationary":[78],"cannot":[80],"be":[81],"applied":[82],"directly":[83],"noise.":[87,111,174,192],"To":[88],"make":[89],"things":[90],"worse,":[91],"both":[92,134,158],"and":[94,136,152,161,190,222,245,273,290,311,329],"coexist":[97],"in":[98,139,254,270],"captured":[100,260],"image":[102,301],"(HSI),":[103],"difficult":[108],"whiten":[110],"In":[112],"this":[113,179],"paper,":[114],"we":[115,142],"propose":[116],"a":[117,144,183],"framework":[120],"cope":[122],"nonwhite":[126],"(SDNW),":[128],"Pre-estimate\u2014Whitening\u2014Post-estimate":[129],"(PWP)":[130],"loop,":[131],"reduce":[133],"HSI.":[140],"Previously,":[141],"proposed":[143,231],"method":[145,186,232,244],"based":[146,242],"multidimensional":[148],"wavelet":[149],"packet":[150],"transform":[151],"multi-way":[153],"Wiener":[154,248],"filter":[155],"which":[156,169],"performs":[157],"white":[159,173,216],"spectral":[162],"dimensionality":[163],"reduction,":[164],"referred":[165],"as":[167],"MWPT-MWF,":[168],"was":[170],"restricted":[171],"We":[175],"get":[176],"inspired":[177],"from":[178,279],"MWPT-MWF":[180],"develop":[182],"iterative":[185],"reducing":[188],"Firstly,":[193],"parameters":[197],"estimation":[198],"(HYNPE)":[199],"algorithm":[200],"used":[202,253,269],"estimate":[204],"parameters,":[207],"SD":[209],"converted":[212],"an":[214],"additive":[215],"Gaussian":[217],"by":[219,229,261],"pre-whitening":[220],"procedure":[221],"then":[223],"whitened":[225],"HSI":[226,259],"denoised":[228],"SDNW-MWPT-MWF.":[233],"As":[234],"comparative":[235],"experiments,":[236],"Multiple":[238],"Linear":[239],"Regression":[240],"(MLR)":[241],"tensor-based":[246],"Multiway":[247],"Filter":[249],"(MWF)":[250],"are":[251,277,308],"framework.":[257],"An":[258],"Reflective":[262],"Optics":[263],"System":[264],"Imaging":[265],"Spectrometer":[266],"(ROSIS)":[267],"experiments":[272,314],"performances":[276],"assessed":[278],"various":[280],"aspects:":[281],"whitening":[284],"performance,":[285],"Signal-to-Noise":[287],"Ratio":[288],"(SNR),":[289],"classification":[292],"performance.":[293],"The":[294],"results":[295],"real-world":[298],"airborne":[299],"HYDICE":[302,328],"(Hyperspectral":[303],"Digital":[304],"Imagery":[305],"Collection":[306],"Experiment)":[307],"presented":[310],"analyzed.":[312],"These":[313],"show":[315],"that":[316],"worth":[319],"taking":[320],"into":[321],"account":[322],"signal-dependency":[324],"hypothesis":[325],"processing":[327],"ROSIS":[330],"HSIs.":[331]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":2}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2018-08-31T00:00:00"}