{"id":"https://openalex.org/W2292167745","doi":"https://doi.org/10.1109/icsipa.2015.7412241","title":"A novel approach to hyperspectral data feature extraction using rational function curve fitting","display_name":"A novel approach to hyperspectral data feature extraction using rational function curve fitting","publication_year":2015,"publication_date":"2015-10-01","ids":{"openalex":"https://openalex.org/W2292167745","doi":"https://doi.org/10.1109/icsipa.2015.7412241","mag":"2292167745"},"language":"en","primary_location":{"id":"doi:10.1109/icsipa.2015.7412241","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icsipa.2015.7412241","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE International Conference on Signal and Image Processing Applications (ICSIPA)","raw_type":"proceedings-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/A5027201368","display_name":"S. Abolfazl Hosseini","orcid":"https://orcid.org/0000-0002-9023-6222"},"institutions":[{"id":"https://openalex.org/I1516879","display_name":"Tarbiat Modares University","ror":"https://ror.org/03mwgfy56","country_code":"IR","type":"education","lineage":["https://openalex.org/I1516879"]}],"countries":["IR"],"is_corresponding":true,"raw_author_name":"S. Abolfazl Hosseini","raw_affiliation_strings":["Department of electrical and computer engineering, Tarbiat Modares University Tehran, Iran"],"affiliations":[{"raw_affiliation_string":"Department of electrical and computer engineering, Tarbiat Modares University Tehran, Iran","institution_ids":["https://openalex.org/I1516879"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5040092306","display_name":"Hassan Ghassemian","orcid":"https://orcid.org/0000-0002-2303-1753"},"institutions":[{"id":"https://openalex.org/I1516879","display_name":"Tarbiat Modares University","ror":"https://ror.org/03mwgfy56","country_code":"IR","type":"education","lineage":["https://openalex.org/I1516879"]}],"countries":["IR"],"is_corresponding":false,"raw_author_name":"Hassan Ghassemian","raw_affiliation_strings":["Department of electrical and computer engineering, Tarbiat Modares University Tehran, Iran"],"affiliations":[{"raw_affiliation_string":"Department of electrical and computer engineering, Tarbiat Modares University Tehran, Iran","institution_ids":["https://openalex.org/I1516879"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5027201368"],"corresponding_institution_ids":["https://openalex.org/I1516879"],"apc_list":null,"apc_paid":null,"fwci":1.2439,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.84697187,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":"1","issue":null,"first_page":"494","last_page":"499"},"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/T13890","display_name":"Remote Sensing and Land Use","score":0.9968000054359436,"subfield":{"id":"https://openalex.org/subfields/1902","display_name":"Atmospheric Science"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.940500020980835,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.8768603801727295},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.7255403399467468},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7089002728462219},{"id":"https://openalex.org/keywords/rational-function","display_name":"Rational function","score":0.6358073949813843},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.5733537077903748},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5572070479393005},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5535752773284912},{"id":"https://openalex.org/keywords/principal-component-analysis","display_name":"Principal component analysis","score":0.5358126759529114},{"id":"https://openalex.org/keywords/linear-discriminant-analysis","display_name":"Linear discriminant analysis","score":0.512304425239563},{"id":"https://openalex.org/keywords/curve-fitting","display_name":"Curve fitting","score":0.4639987349510193},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.45401325821876526},{"id":"https://openalex.org/keywords/function","display_name":"Function (biology)","score":0.4499340355396271},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.43642571568489075},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.1536286175251007}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.8768603801727295},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.7255403399467468},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7089002728462219},{"id":"https://openalex.org/C75190567","wikidata":"https://www.wikidata.org/wiki/Q41237","display_name":"Rational function","level":2,"score":0.6358073949813843},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.5733537077903748},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5572070479393005},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5535752773284912},{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.5358126759529114},{"id":"https://openalex.org/C69738355","wikidata":"https://www.wikidata.org/wiki/Q1228929","display_name":"Linear discriminant analysis","level":2,"score":0.512304425239563},{"id":"https://openalex.org/C184389593","wikidata":"https://www.wikidata.org/wiki/Q603159","display_name":"Curve fitting","level":2,"score":0.4639987349510193},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.45401325821876526},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.4499340355396271},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.43642571568489075},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.1536286175251007},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C78458016","wikidata":"https://www.wikidata.org/wiki/Q840400","display_name":"Evolutionary biology","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icsipa.2015.7412241","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icsipa.2015.7412241","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE International Conference on Signal and Image Processing Applications (ICSIPA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.6299999952316284}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W1976359033","https://openalex.org/W1979153668","https://openalex.org/W1997413030","https://openalex.org/W2039609561","https://openalex.org/W2069231830","https://openalex.org/W2082874195","https://openalex.org/W2086970160","https://openalex.org/W2093101244","https://openalex.org/W2097900616","https://openalex.org/W2108917905","https://openalex.org/W2129652905","https://openalex.org/W2136251662","https://openalex.org/W2146952738","https://openalex.org/W2149671299","https://openalex.org/W2150727275","https://openalex.org/W2155799428","https://openalex.org/W2157039825","https://openalex.org/W2162968378","https://openalex.org/W2168481151"],"related_works":["https://openalex.org/W2072166414","https://openalex.org/W2052589448","https://openalex.org/W2765337000","https://openalex.org/W3104072235","https://openalex.org/W2391447249","https://openalex.org/W2312955079","https://openalex.org/W2350996391","https://openalex.org/W2066119650","https://openalex.org/W2059615944","https://openalex.org/W4224057882"],"abstract_inverted_index":{"Regarding":[0],"to":[1,15,86,209,222,225,234,241,245],"large":[2],"volumes":[3],"of":[4,10,32,50,75,92,96,101,124,150,187,228],"hyperspectral":[5,51,77],"data":[6,37,52,244],"that":[7,93,118,144,171,194],"contain":[8],"hundreds":[9],"spectral":[11,54,89,128],"bands":[12],"and":[13,55,99,158,163,231,236],"due":[14],"their":[16,164],"very":[17],"high":[18],"between-band":[19],"correlation,":[20],"feature":[21,24,40,62,211,247],"reduction":[22,41,63],"(either":[23],"selection":[25],"or":[26],"extraction)":[27],"is":[28,70,84],"an":[29],"essential":[30],"part":[31],"classification":[33,191,206],"process":[34],"for":[35],"this":[36,59],"type.":[38],"Many":[39],"techniques":[42],"have":[43],"been":[44,138],"developed":[45,85],"by":[46,140,178],"considering":[47],"different":[48,173,185],"specifications":[49],"in":[53,127],"spatial":[56],"domains.":[57],"In":[58,167,214],"paper,":[60],"a":[61,76,79],"technique":[64],"based":[65,146],"on":[66,115,147],"extracting":[67],"new":[68,108,112,246],"features":[69],"proposed.":[71],"For":[72],"each":[73],"pixel":[74],"image,":[78],"specific":[80],"rational":[81,103,179],"function":[82,104],"approximation":[83],"fit":[87],"the":[88,97,102,116,119,195,216,220],"response":[90,129],"curve":[91,130],"pixel.":[94],"Coefficients":[95],"numerator":[98],"denominator":[100],"are":[105,145],"considered":[106],"as":[107,153],"extracted":[109],"features.":[110],"This":[111],"method":[113,203],"relies":[114],"fact":[117],"sequence":[120],"discipline":[121],"-":[122,131],"ordinance":[123],"reflectance":[125],"coefficients":[126],"contains":[132],"some":[133],"information":[134],"which":[135,239],"has":[136,219],"not":[137],"addressed":[139],"other":[141,237],"competing":[142,210],"methods":[143,238],"statistical":[148],"analysis":[149],"data,":[151],"such":[152],"Principal":[154],"Component":[155],"Analysis":[156,161],"(PCA)":[157],"Linear":[159],"Discriminant":[160],"(LDA)":[162],"nonlinear":[165],"versions.":[166],"addition,":[168,215],"we":[169],"show":[170],"naturally":[172],"curves":[174],"can":[175],"be":[176,223],"approximated":[177],"functions":[180],"with":[181],"equal":[182],"form,":[183],"but":[184],"amounts":[186],"coefficients.":[188],"Maximum":[189],"likelihood":[190],"results":[192],"demonstrate":[193],"Rational":[196],"Function":[197],"Curve":[198],"Fitting":[199],"Feature":[200],"Extraction":[201],"(RFCF-FE)":[202],"provides":[204],"better":[205],"accuracies":[207],"compared":[208],"extraction":[212],"algorithms.":[213],"proposed":[217],"algorithm":[218],"possibility":[221],"applied":[224],"all":[226],"pixels":[227],"image":[229],"individually":[230],"simultaneously,":[232],"unlike":[233],"PCA":[235],"need":[240],"transform":[242],"whole":[243],"space.":[248]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2017,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}