{"id":"https://openalex.org/W2557938988","doi":"https://doi.org/10.1109/tgrs.2016.2624505","title":"Modified Residual Method for the Estimation of Noise in Hyperspectral Images","display_name":"Modified Residual Method for the Estimation of Noise in Hyperspectral Images","publication_year":2016,"publication_date":"2016-11-29","ids":{"openalex":"https://openalex.org/W2557938988","doi":"https://doi.org/10.1109/tgrs.2016.2624505","mag":"2557938988"},"language":"en","primary_location":{"id":"doi:10.1109/tgrs.2016.2624505","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2016.2624505","pdf_url":null,"source":{"id":"https://openalex.org/S111326731","display_name":"IEEE Transactions on Geoscience and Remote Sensing","issn_l":"0196-2892","issn":["0196-2892","1558-0644"],"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 Transactions on Geoscience and Remote Sensing","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/A5101777931","display_name":"Asad Mahmood","orcid":"https://orcid.org/0000-0003-4936-6874"},"institutions":[{"id":"https://openalex.org/I192619145","display_name":"University of the Witwatersrand","ror":"https://ror.org/03rp50x72","country_code":"ZA","type":"education","lineage":["https://openalex.org/I192619145"]}],"countries":["ZA"],"is_corresponding":false,"raw_author_name":"Asad Mahmood","raw_affiliation_strings":["School of Electrical and Information Engineering, University of the Witwatersrand, Johannesburg, South Africa"],"raw_orcid":"https://orcid.org/0000-0003-4936-6874","affiliations":[{"raw_affiliation_string":"School of Electrical and Information Engineering, University of the Witwatersrand, Johannesburg, South Africa","institution_ids":["https://openalex.org/I192619145"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033464542","display_name":"Amandine Robin","orcid":null},"institutions":[{"id":"https://openalex.org/I192619145","display_name":"University of the Witwatersrand","ror":"https://ror.org/03rp50x72","country_code":"ZA","type":"education","lineage":["https://openalex.org/I192619145"]}],"countries":["ZA"],"is_corresponding":false,"raw_author_name":"Amandine Robin","raw_affiliation_strings":["School of Computer Science and Applied Mathematics, University of the Witwatersrand, Johannesburg, South Africa"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Applied Mathematics, University of the Witwatersrand, Johannesburg, South Africa","institution_ids":["https://openalex.org/I192619145"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5051832085","display_name":"Michael Sears","orcid":null},"institutions":[{"id":"https://openalex.org/I192619145","display_name":"University of the Witwatersrand","ror":"https://ror.org/03rp50x72","country_code":"ZA","type":"education","lineage":["https://openalex.org/I192619145"]}],"countries":["ZA"],"is_corresponding":false,"raw_author_name":"Michael Sears","raw_affiliation_strings":["School of Computer Science and Applied Mathematics, University of the Witwatersrand, Johannesburg, South Africa"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Applied Mathematics, University of the Witwatersrand, Johannesburg, South Africa","institution_ids":["https://openalex.org/I192619145"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I192619145"],"apc_list":null,"apc_paid":null,"fwci":2.8811,"has_fulltext":false,"cited_by_count":17,"citation_normalized_percentile":{"value":0.92483021,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":"55","issue":"3","first_page":"1451","last_page":"1460"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9998999834060669,"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":0.9998999834060669,"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/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.9868000149726868,"subfield":{"id":"https://openalex.org/subfields/1602","display_name":"Analytical Chemistry"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12157","display_name":"Geochemistry and Geologic Mapping","score":0.9842000007629395,"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/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.8133235573768616},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.7349019050598145},{"id":"https://openalex.org/keywords/value-noise","display_name":"Value noise","score":0.6396245956420898},{"id":"https://openalex.org/keywords/gradient-noise","display_name":"Gradient noise","score":0.634442925453186},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.6326436996459961},{"id":"https://openalex.org/keywords/covariance-matrix","display_name":"Covariance matrix","score":0.5012960433959961},{"id":"https://openalex.org/keywords/noise-measurement","display_name":"Noise measurement","score":0.49446964263916016},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.4901908040046692},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4801620841026306},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4764186143875122},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.4706862270832062},{"id":"https://openalex.org/keywords/colors-of-noise","display_name":"Colors of noise","score":0.45640671253204346},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.44339966773986816},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4406488537788391},{"id":"https://openalex.org/keywords/gaussian-noise","display_name":"Gaussian noise","score":0.4395511746406555},{"id":"https://openalex.org/keywords/covariance","display_name":"Covariance","score":0.41262906789779663},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.37846335768699646},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.26500949263572693},{"id":"https://openalex.org/keywords/noise-floor","display_name":"Noise floor","score":0.2328064739704132},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.14586228132247925}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.8133235573768616},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.7349019050598145},{"id":"https://openalex.org/C182163834","wikidata":"https://www.wikidata.org/wiki/Q2926529","display_name":"Value noise","level":5,"score":0.6396245956420898},{"id":"https://openalex.org/C200378446","wikidata":"https://www.wikidata.org/wiki/Q4147391","display_name":"Gradient noise","level":5,"score":0.634442925453186},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.6326436996459961},{"id":"https://openalex.org/C185142706","wikidata":"https://www.wikidata.org/wiki/Q1134404","display_name":"Covariance matrix","level":2,"score":0.5012960433959961},{"id":"https://openalex.org/C29265498","wikidata":"https://www.wikidata.org/wiki/Q7047719","display_name":"Noise measurement","level":3,"score":0.49446964263916016},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.4901908040046692},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4801620841026306},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4764186143875122},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.4706862270832062},{"id":"https://openalex.org/C114996537","wikidata":"https://www.wikidata.org/wiki/Q4854529","display_name":"Colors of noise","level":3,"score":0.45640671253204346},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.44339966773986816},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4406488537788391},{"id":"https://openalex.org/C4199805","wikidata":"https://www.wikidata.org/wiki/Q2725903","display_name":"Gaussian noise","level":2,"score":0.4395511746406555},{"id":"https://openalex.org/C178650346","wikidata":"https://www.wikidata.org/wiki/Q201984","display_name":"Covariance","level":2,"score":0.41262906789779663},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.37846335768699646},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.26500949263572693},{"id":"https://openalex.org/C187612029","wikidata":"https://www.wikidata.org/wiki/Q17083130","display_name":"Noise floor","level":4,"score":0.2328064739704132},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.14586228132247925}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tgrs.2016.2624505","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2016.2624505","pdf_url":null,"source":{"id":"https://openalex.org/S111326731","display_name":"IEEE Transactions on Geoscience and Remote Sensing","issn_l":"0196-2892","issn":["0196-2892","1558-0644"],"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 Transactions on Geoscience and Remote Sensing","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"No poverty","id":"https://metadata.un.org/sdg/1","score":0.5899999737739563}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W851423022","https://openalex.org/W994592074","https://openalex.org/W1526470323","https://openalex.org/W2014374676","https://openalex.org/W2070424424","https://openalex.org/W2074342013","https://openalex.org/W2090365581","https://openalex.org/W2114341361","https://openalex.org/W2114486983","https://openalex.org/W2117767750","https://openalex.org/W2142192675","https://openalex.org/W2160179017","https://openalex.org/W2165755981","https://openalex.org/W2765155772","https://openalex.org/W2765314056","https://openalex.org/W6623693198","https://openalex.org/W6626122873","https://openalex.org/W6745290338","https://openalex.org/W6745339100"],"related_works":["https://openalex.org/W2079755857","https://openalex.org/W2116036791","https://openalex.org/W4226451553","https://openalex.org/W2170781407","https://openalex.org/W2588855097","https://openalex.org/W2955414824","https://openalex.org/W1986743941","https://openalex.org/W2116566913","https://openalex.org/W2092661960","https://openalex.org/W2811019791"],"abstract_inverted_index":{"Many":[0],"hyperspectral":[1,96],"image":[2],"processing":[3],"algorithms":[4,60],"(e.g.,":[5],"detection,":[6],"classification,":[7],"endmember":[8],"extraction,":[9],"and":[10,51,112,187],"so":[11],"on)":[12],"are":[13,217],"generally":[14],"designed":[15],"with":[16],"the":[17,32,38,55,65,77,93,127,131,138,144,153,172,183,197,205,211,227],"assumption":[18],"of":[19,34,54,67,80,146,163,182,213,226],"no":[20],"spectral":[21,43,169],"or":[22],"spatial":[23],"correlation":[24,36,170],"in":[25,41,48,64,89,135,171],"noise.":[26,69,148,173],"However,":[27],"previous":[28],"studies":[29],"have":[30],"shown":[31],"presence":[33,66],"nonnegligible":[35],"between":[37,46],"noise":[39,83,85,97,111,124,132,177,184,198,207,215],"samples":[40],"different":[42],"bands,":[44,50],"especially":[45],"noises":[47],"adjacent":[49],"that":[52,92,118,152,204],"most":[53],"well-known":[56],"intrinsic":[57],"dimension":[58],"estimation":[59,98,154],"give":[61],"poor":[62],"estimates":[63,134,208],"correlated":[68,82,110],"Thus,":[70],"there":[71,166],"is":[72,156,167],"a":[73,115,161,193],"need":[74],"to":[75,120,123,137,195],"tackle":[76],"specific":[78],"case":[79,145],"spectrally":[81,109],"for":[84,143,188],"estimation.":[86],"We":[87],"show,":[88],"this":[90],"paper,":[91],"commonly":[94],"employed":[95],"algorithm":[99],"based":[100],"on":[101],"regression":[102],"residuals":[103],"can":[104],"be":[105,121],"significantly":[106],"affected":[107],"by":[108,160],"we":[113,190],"suggest":[114],"modified":[116],"approach":[117],"proves":[119],"robust":[122],"correlation.":[125],"Furthermore,":[126],"proposed":[128,175],"method":[129,141,194],"improves":[130],"variance":[133],"comparison":[136],"classic":[139],"residual":[140],"even":[142],"uncorrelated":[147],"Simulation":[149,201],"results":[150,202],"show":[151],"error":[155],"reduced":[157],"at":[158],"times":[159],"factor":[162],"5":[164],"when":[165],"high":[168],"Our":[174],"per-pixel":[176,206],"estimator":[178],"requires":[179],"an":[180],"estimate":[181,196],"covariance":[185,199],"matrix,":[186],"this,":[189],"also":[191],"propose":[192],"matrix.":[200],"demonstrate":[203],"obtained":[209,223],"via":[210,224],"use":[212,225],"estimated":[214],"statistics":[216],"almost":[218],"as":[219,221],"good":[220],"those":[222],"true":[228],"statistics.":[229]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":4},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":4},{"year":2017,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}