{"id":"https://openalex.org/W2766644198","doi":"https://doi.org/10.1109/whispers.2013.8080610","title":"A data-noise tolerant method for multi-temporal hyperspectral images classification","display_name":"A data-noise tolerant method for multi-temporal hyperspectral images classification","publication_year":2013,"publication_date":"2013-06-01","ids":{"openalex":"https://openalex.org/W2766644198","doi":"https://doi.org/10.1109/whispers.2013.8080610","mag":"2766644198"},"language":"en","primary_location":{"id":"doi:10.1109/whispers.2013.8080610","is_oa":false,"landing_page_url":"https://doi.org/10.1109/whispers.2013.8080610","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2013 5th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5104858164","display_name":"Selim Hemissi","orcid":null},"institutions":[{"id":"https://openalex.org/I205703379","display_name":"Institut Mines-T\u00e9l\u00e9com","ror":"https://ror.org/025vp2923","country_code":"FR","type":"facility","lineage":["https://openalex.org/I205703379"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Selim Hemissi","raw_affiliation_strings":["ITI department, Telecom Bretagne, France"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"ITI department, Telecom Bretagne, France","institution_ids":["https://openalex.org/I205703379"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5053542801","display_name":"Imed Riadh Farah","orcid":"https://orcid.org/0000-0001-9114-5659"},"institutions":[{"id":"https://openalex.org/I205703379","display_name":"Institut Mines-T\u00e9l\u00e9com","ror":"https://ror.org/025vp2923","country_code":"FR","type":"facility","lineage":["https://openalex.org/I205703379"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Imed Riadh Farah","raw_affiliation_strings":["ITI department, Telecom Bretagne, France"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"ITI department, Telecom Bretagne, France","institution_ids":["https://openalex.org/I205703379"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I205703379"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.38610851,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"22","issue":null,"first_page":"1","last_page":"4"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9998000264167786,"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":0.9998000264167786,"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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9940000176429749,"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.9868999719619751,"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/computer-science","display_name":"Computer science","score":0.7790253162384033},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.7017126679420471},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.6678249835968018},{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.6247045993804932},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5877217650413513},{"id":"https://openalex.org/keywords/interpolation","display_name":"Interpolation (computer graphics)","score":0.5414400696754456},{"id":"https://openalex.org/keywords/noisy-data","display_name":"Noisy data","score":0.5369147658348083},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5106704831123352},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5049477219581604},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4348936676979065},{"id":"https://openalex.org/keywords/errors-in-variables-models","display_name":"Errors-in-variables models","score":0.4161718189716339},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3310352861881256},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.14637324213981628}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7790253162384033},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.7017126679420471},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.6678249835968018},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.6247045993804932},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5877217650413513},{"id":"https://openalex.org/C137800194","wikidata":"https://www.wikidata.org/wiki/Q11713455","display_name":"Interpolation (computer graphics)","level":3,"score":0.5414400696754456},{"id":"https://openalex.org/C2781170535","wikidata":"https://www.wikidata.org/wiki/Q30587856","display_name":"Noisy data","level":2,"score":0.5369147658348083},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5106704831123352},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5049477219581604},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4348936676979065},{"id":"https://openalex.org/C179024874","wikidata":"https://www.wikidata.org/wiki/Q5395728","display_name":"Errors-in-variables models","level":2,"score":0.4161718189716339},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3310352861881256},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.14637324213981628},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/whispers.2013.8080610","is_oa":false,"landing_page_url":"https://doi.org/10.1109/whispers.2013.8080610","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2013 5th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16","score":0.8199999928474426}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W4258259","https://openalex.org/W175761018","https://openalex.org/W1982572872","https://openalex.org/W2022573323","https://openalex.org/W2039813705","https://openalex.org/W2050913223","https://openalex.org/W2127625042","https://openalex.org/W2135770948","https://openalex.org/W2139212933","https://openalex.org/W2160317488","https://openalex.org/W2171468830","https://openalex.org/W2265461003","https://openalex.org/W3144493338"],"related_works":["https://openalex.org/W2072166414","https://openalex.org/W3209970181","https://openalex.org/W2060875994","https://openalex.org/W3034375524","https://openalex.org/W4230131218","https://openalex.org/W2070598848","https://openalex.org/W2044184146","https://openalex.org/W4313014865","https://openalex.org/W2605741893","https://openalex.org/W2980893298"],"abstract_inverted_index":{"Remote":[0],"sensing":[1],"data":[2,17,23],"is":[3,74],"not":[4],"perfect":[5],"and":[6,24,102,107],"can":[7,33,64],"be":[8],"affected":[9],"by":[10,79],"some":[11,43],"corruptions":[12],"that":[13],"may":[14],"alter":[15],"the":[16,22,59,96],"consistency,":[18],"models":[19],"created":[20],"from":[21,55],"decisions":[25],"made":[26],"based":[27],"on":[28],"these":[29],"models.":[30],"Estimation":[31],"error":[32,63,82,106,131],"diminish":[34],"system":[35],"efficiency":[36],"in":[37,76,115],"terms":[38],"of":[39,61,87,92],"classification":[40,119],"accuracy.":[41,120],"Accordingly,":[42],"learning":[44,53,84,113],"approaches":[45],"have":[46],"adopted":[47],"different":[48],"ways":[49],"to":[50,94,103,108,117,127],"enhance":[51,118],"their":[52],"abilities":[54],"erroneous":[56],"environments.":[57],"Nevertheless,":[58],"existence":[60],"interpolation":[62],"still":[65],"introduce":[66],"critical":[67],"conflicting":[68],"decisions.":[69],"A":[70],"more":[71],"reasonable":[72],"solution":[73],"introduced":[75],"this":[77,81,105],"paper":[78],"incorporating":[80],"into":[83,112],"process.":[85],"Instead":[86],"taking":[88],"any":[89],"unified":[90],"theory":[91],"noise":[93,97],"evaluate":[95],"impacts":[98],"separately,":[99],"we":[100,122],"try":[101],"estimate":[104],"incorporate":[109],"it,":[110],"cooperatively,":[111],"stage":[114],"order":[116],"Concretely,":[121],"present":[123],"a":[124],"new":[125],"way":[126],"deal":[128],"with":[129],"reconstruction":[130],"when":[132],"classifying":[133],"3D":[134],"spectral":[135],"signatures.":[136]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}