{"id":"https://openalex.org/W2765221238","doi":"https://doi.org/10.1109/whispers.2013.8080739","title":"Nonlinear classification of hyperspectral signatures in high noise","display_name":"Nonlinear classification of hyperspectral signatures in high noise","publication_year":2013,"publication_date":"2013-06-01","ids":{"openalex":"https://openalex.org/W2765221238","doi":"https://doi.org/10.1109/whispers.2013.8080739","mag":"2765221238"},"language":"en","primary_location":{"id":"doi:10.1109/whispers.2013.8080739","is_oa":false,"landing_page_url":"https://doi.org/10.1109/whispers.2013.8080739","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/A5068084125","display_name":"Mark S. Schmalz","orcid":null},"institutions":[{"id":"https://openalex.org/I33213144","display_name":"University of Florida","ror":"https://ror.org/02y3ad647","country_code":"US","type":"education","lineage":["https://openalex.org/I33213144"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mark S. Schmalz","raw_affiliation_strings":["University of Florida, Gainesville, FL, US"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Florida, Gainesville, FL, US","institution_ids":["https://openalex.org/I33213144"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086969690","display_name":"Eric Y. Hayden","orcid":"https://orcid.org/0000-0001-6977-5056"},"institutions":[{"id":"https://openalex.org/I33213144","display_name":"University of Florida","ror":"https://ror.org/02y3ad647","country_code":"US","type":"education","lineage":["https://openalex.org/I33213144"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Eric Hayden","raw_affiliation_strings":["University of Florida, Gainesville, FL, US"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Florida, Gainesville, FL, US","institution_ids":["https://openalex.org/I33213144"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5041788462","display_name":"Gerhard X. Ritter","orcid":null},"institutions":[{"id":"https://openalex.org/I33213144","display_name":"University of Florida","ror":"https://ror.org/02y3ad647","country_code":"US","type":"education","lineage":["https://openalex.org/I33213144"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Gerhard X. Ritter","raw_affiliation_strings":["University of Florida, Gainesville, FL, US"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Florida, Gainesville, FL, US","institution_ids":["https://openalex.org/I33213144"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I33213144"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.27678807,"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":"4"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9997000098228455,"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"}},"topics":[{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9997000098228455,"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"}},{"id":"https://openalex.org/T11447","display_name":"Blind Source Separation Techniques","score":0.996999979019165,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T12611","display_name":"Neural Networks and Reservoir Computing","score":0.9947999715805054,"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/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7306597232818604},{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.6889225244522095},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6687562465667725},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6206647157669067},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5803894400596619},{"id":"https://openalex.org/keywords/spectral-signature","display_name":"Spectral signature","score":0.4771244525909424},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.4572126865386963},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.4417266249656677},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.13381564617156982},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.0751151442527771}],"concepts":[{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7306597232818604},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.6889225244522095},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6687562465667725},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6206647157669067},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5803894400596619},{"id":"https://openalex.org/C176641082","wikidata":"https://www.wikidata.org/wiki/Q2446767","display_name":"Spectral signature","level":2,"score":0.4771244525909424},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.4572126865386963},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.4417266249656677},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.13381564617156982},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.0751151442527771},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/whispers.2013.8080739","is_oa":false,"landing_page_url":"https://doi.org/10.1109/whispers.2013.8080739","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":[{"score":0.5,"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":5,"referenced_works":["https://openalex.org/W2042474621","https://openalex.org/W2107120407","https://openalex.org/W2113632338","https://openalex.org/W2124925761","https://openalex.org/W2327233864"],"related_works":["https://openalex.org/W2738168532","https://openalex.org/W4367471608","https://openalex.org/W2037328426","https://openalex.org/W2889956472","https://openalex.org/W2054439167","https://openalex.org/W2044594927","https://openalex.org/W2012636591","https://openalex.org/W2539574252","https://openalex.org/W2791078257","https://openalex.org/W4287750516"],"abstract_inverted_index":{"Accurate":[0],"spectral":[1,19,74],"signature":[2,55,75,102,147,193],"classification":[3,14,194],"is":[4],"crucial":[5],"to":[6,54,124,189],"hyperspectral":[7,101,192],"signal":[8],"and":[9,31,39,71,108,181],"image":[10],"analysis,":[11],"especially":[12],"where":[13],"accuracy":[15,48],"depends":[16],"on":[17,112],"correct":[18],"endmember":[20],"determination.":[21],"In":[22,73],"practice,":[23],"signatures":[24],"can":[25,78],"be":[26],"corrupted":[27],"by":[28],"possibly-nonergodic":[29],"noise":[30],"clutter,":[32],"which":[33],"induces":[34],"suboptimal":[35],"object":[36],"class":[37],"separation":[38],"erroneous":[40],"classification.":[41,113],"We":[42,136],"have":[43,175],"elsewhere":[44],"shown":[45],"that":[46],"the":[47,125,132,141,151,196,208],"of":[49,66,82,127,143,198],"computed":[50],"pattern":[51,97,130,145,159,179],"recognition":[52],"applied":[53],"detection":[56],"can,":[57],"like":[58],"its":[59,109],"human":[60],"counterpart,":[61],"benefit":[62],"from":[63,161],"adaptive":[64,80],"processes":[65],"cognition":[67],"such":[68,98],"as":[69,99,140,157],"learning":[70],"forgetting.":[72],"classification,":[76],"this":[77,120],"support":[79],"tracking":[81],"input":[83],"nonergodicities.":[84],"This":[85],"paper":[86],"summarizes":[87],"our":[88],"recent":[89],"work":[90],"in":[91,116,150,195,207],"acquiring":[92],"or":[93,146],"inserting":[94],"a":[95,100,104,128,144,158,167],"new":[96,129],"into":[103],"classifier's":[105],"knowledge":[106,153],"base,":[107,154],"beneficial":[110],"effect":[111],"For":[114],"example,":[115,156,202],"neural":[117,172],"nets":[118],"(NNs),":[119],"insertion":[121,180],"process":[122],"corresponds":[123],"superposition":[126],"onto":[131],"NN":[133,163],"weight":[134,164],"matrix.":[135,165],"similarly":[137],"model":[138],"forgetting":[139],"deletion":[142,160,182],"currently":[148],"stored":[149],"classifier":[152],"for":[155,184,201],"an":[162],"Although":[166],"difficult":[168],"goal":[169],"with":[170],"classical":[171],"nets,":[173],"we":[174],"implemented":[176],"feedback":[177],"controlled":[178],"operations":[183],"lattice":[185],"associative":[186],"memories":[187],"(LAMs)":[188],"achieve":[190],"accurate":[191],"presence":[197],"nonergodic":[199],"noise,":[200],"at":[203],"peak-to-peak":[204],"signal-to-noise":[205],"ratios":[206],"range":[209],"SNR":[210],"\u2265":[211],"0.1:1.":[212]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}