{"id":"https://openalex.org/W2344955579","doi":"https://doi.org/10.1109/lgrs.2016.2552403","title":"Tensor Decomposition and PCA Jointed Algorithm for Hyperspectral Image Denoising","display_name":"Tensor Decomposition and PCA Jointed Algorithm for Hyperspectral Image Denoising","publication_year":2016,"publication_date":"2016-04-28","ids":{"openalex":"https://openalex.org/W2344955579","doi":"https://doi.org/10.1109/lgrs.2016.2552403","mag":"2344955579"},"language":"en","primary_location":{"id":"doi:10.1109/lgrs.2016.2552403","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2016.2552403","pdf_url":null,"source":{"id":"https://openalex.org/S126920919","display_name":"IEEE Geoscience and Remote Sensing Letters","issn_l":"1545-598X","issn":["1545-598X","1558-0571"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Geoscience and Remote Sensing Letters","raw_type":"journal-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/A5001908942","display_name":"Shushu Meng","orcid":null},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shushu Meng","raw_affiliation_strings":["School of Communication and Information Engineering, University of Electronic Science and Technology of China, Chengdu, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Communication and Information Engineering, University of Electronic Science and Technology of China, Chengdu, China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057667197","display_name":"Long-Ting Huang","orcid":"https://orcid.org/0000-0003-2236-0501"},"institutions":[{"id":"https://openalex.org/I168719708","display_name":"City University of Hong Kong","ror":"https://ror.org/03q8dnn23","country_code":"HK","type":"education","lineage":["https://openalex.org/I168719708"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Long-Ting Huang","raw_affiliation_strings":["Department of Electronic Engineering, City University of Hong Kong, Kowloon, Hong Kong"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electronic Engineering, City University of Hong Kong, Kowloon, Hong Kong","institution_ids":["https://openalex.org/I168719708"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5049463786","display_name":"Wen-Qin Wang","orcid":"https://orcid.org/0000-0003-4466-9585"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wen-Qin Wang","raw_affiliation_strings":["School of Communication and Information Engineering, University of Electronic Science and Technology of China, Chengdu, China"],"raw_orcid":"https://orcid.org/0000-0003-4466-9585","affiliations":[{"raw_affiliation_string":"School of Communication and Information Engineering, University of Electronic Science and Technology of China, Chengdu, China","institution_ids":["https://openalex.org/I150229711"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.4748,"has_fulltext":false,"cited_by_count":46,"citation_normalized_percentile":{"value":0.90330458,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":93,"max":99},"biblio":{"volume":"13","issue":"7","first_page":"897","last_page":"901"},"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/T12303","display_name":"Tensor decomposition and applications","score":0.9955000281333923,"subfield":{"id":"https://openalex.org/subfields/2605","display_name":"Computational Mathematics"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9926999807357788,"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":0.8481059074401855},{"id":"https://openalex.org/keywords/principal-component-analysis","display_name":"Principal component analysis","score":0.8085945844650269},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.7460172176361084},{"id":"https://openalex.org/keywords/preprocessor","display_name":"Preprocessor","score":0.7322512865066528},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6423941254615784},{"id":"https://openalex.org/keywords/tucker-decomposition","display_name":"Tucker decomposition","score":0.5600141286849976},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5437610745429993},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5345155000686646},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.5161829590797424},{"id":"https://openalex.org/keywords/decomposition","display_name":"Decomposition","score":0.5090117454528809},{"id":"https://openalex.org/keywords/data-pre-processing","display_name":"Data pre-processing","score":0.42155855894088745},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.41772791743278503},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.35160017013549805},{"id":"https://openalex.org/keywords/tensor","display_name":"Tensor (intrinsic definition)","score":0.34784579277038574},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3474733233451843},{"id":"https://openalex.org/keywords/tensor-decomposition","display_name":"Tensor decomposition","score":0.31081777811050415}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.8481059074401855},{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.8085945844650269},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.7460172176361084},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.7322512865066528},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6423941254615784},{"id":"https://openalex.org/C42704193","wikidata":"https://www.wikidata.org/wiki/Q7851097","display_name":"Tucker decomposition","level":4,"score":0.5600141286849976},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5437610745429993},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5345155000686646},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5161829590797424},{"id":"https://openalex.org/C124681953","wikidata":"https://www.wikidata.org/wiki/Q339062","display_name":"Decomposition","level":2,"score":0.5090117454528809},{"id":"https://openalex.org/C10551718","wikidata":"https://www.wikidata.org/wiki/Q5227332","display_name":"Data pre-processing","level":2,"score":0.42155855894088745},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.41772791743278503},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.35160017013549805},{"id":"https://openalex.org/C155281189","wikidata":"https://www.wikidata.org/wiki/Q3518150","display_name":"Tensor (intrinsic definition)","level":2,"score":0.34784579277038574},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3474733233451843},{"id":"https://openalex.org/C2986737658","wikidata":"https://www.wikidata.org/wiki/Q30103009","display_name":"Tensor decomposition","level":3,"score":0.31081777811050415},{"id":"https://openalex.org/C18903297","wikidata":"https://www.wikidata.org/wiki/Q7150","display_name":"Ecology","level":1,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lgrs.2016.2552403","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2016.2552403","pdf_url":null,"source":{"id":"https://openalex.org/S126920919","display_name":"IEEE Geoscience and Remote Sensing Letters","issn_l":"1545-598X","issn":["1545-598X","1558-0571"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Geoscience and Remote Sensing Letters","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G4308700709","display_name":"\u57fa\u4e8e\u9891\u63a7\u9635\u7684\u96f7\u8fbe\u5c04\u9891\u9690\u8eab\u7406\u8bba\u673a\u7406\u4e0e\u6ce2\u675f\u5f62\u6210\u7b97\u6cd5\u7814\u7a76","funder_award_id":"61571081","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"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":17,"referenced_works":["https://openalex.org/W1974438823","https://openalex.org/W1985242206","https://openalex.org/W1997718749","https://openalex.org/W2005106632","https://openalex.org/W2024165284","https://openalex.org/W2028436154","https://openalex.org/W2036226226","https://openalex.org/W2039596145","https://openalex.org/W2063220925","https://openalex.org/W2070424424","https://openalex.org/W2097075663","https://openalex.org/W2102219056","https://openalex.org/W2113827757","https://openalex.org/W2142009246","https://openalex.org/W2161073299","https://openalex.org/W2165755981","https://openalex.org/W2170885533"],"related_works":["https://openalex.org/W2989490741","https://openalex.org/W3092506759","https://openalex.org/W2367545121","https://openalex.org/W4248881655","https://openalex.org/W3171001481","https://openalex.org/W2482165163","https://openalex.org/W3010890513","https://openalex.org/W120741642","https://openalex.org/W138569904","https://openalex.org/W3130016156"],"abstract_inverted_index":{"Denoising":[0],"is":[1,76],"a":[2],"critical":[3],"preprocessing":[4],"step":[5],"for":[6],"hyperspectral":[7],"image":[8],"(HSI)":[9],"classification":[10],"and":[11,23,55,72],"detection.":[12],"Traditional":[13],"methods":[14,94],"usually":[15],"convert":[16],"high-dimensional":[17,31],"HSI":[18,48],"data":[19,22],"to":[20],"2-D":[21],"process":[24],"them":[25],"separately.":[26],"Consequently,":[27],"the":[28,34,89,96],"inherent":[29],"structured":[30],"information":[32],"in":[33,95],"original":[35],"observations":[36],"may":[37],"be":[38],"discarded.":[39],"To":[40],"overcome":[41],"this":[42,44,80],"disadvantage,":[43],"letter":[45],"tackles":[46],"an":[47],"denoising":[49],"by":[50],"jointly":[51],"exploiting":[52],"Tucker":[53,62],"decomposition":[54,63],"principal":[56],"component":[57],"analysis":[58,71],"(PCA).":[59],"A":[60],"truncated":[61],"method":[64,82,91],"based":[65],"on":[66],"noise":[67],"power":[68],"ratio":[69,101],"(NPR)":[70],"jointed":[73,81],"with":[74],"PCA":[75],"presented.":[77],"We":[78],"call":[79],"as":[83],"NPR-Tucker+PCA.":[84],"Experimental":[85],"results":[86],"show":[87],"that":[88],"proposed":[90],"outperforms":[92],"existing":[93],"sense":[97],"of":[98],"peak":[99],"signal-to-noise":[100],"performance.":[102]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":8},{"year":2023,"cited_by_count":9},{"year":2022,"cited_by_count":7},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":4},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":7},{"year":2017,"cited_by_count":3}],"updated_date":"2026-07-29T09:40:50.615796","created_date":"2025-10-10T00:00:00"}
