{"id":"https://openalex.org/W3045387165","doi":"https://doi.org/10.1109/tgrs.2020.3009290","title":"Transformation of Multispectral Data to Quasi-Hyperspectral Data Using Convolutional Neural Network Regression","display_name":"Transformation of Multispectral Data to Quasi-Hyperspectral Data Using Convolutional Neural Network Regression","publication_year":2020,"publication_date":"2020-07-23","ids":{"openalex":"https://openalex.org/W3045387165","doi":"https://doi.org/10.1109/tgrs.2020.3009290","mag":"3045387165"},"language":"en","primary_location":{"id":"doi:10.1109/tgrs.2020.3009290","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2020.3009290","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/A5009598295","display_name":"Subir Paul","orcid":"https://orcid.org/0000-0001-5651-2775"},"institutions":[{"id":"https://openalex.org/I59270414","display_name":"Indian Institute of Science Bangalore","ror":"https://ror.org/04dese585","country_code":"IN","type":"education","lineage":["https://openalex.org/I59270414"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Subir Paul","raw_affiliation_strings":["Indian Institute of Science, Bengaluru, India"],"affiliations":[{"raw_affiliation_string":"Indian Institute of Science, Bengaluru, India","institution_ids":["https://openalex.org/I59270414"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5083573588","display_name":"D. Nagesh Kumar","orcid":"https://orcid.org/0000-0002-5294-8501"},"institutions":[{"id":"https://openalex.org/I59270414","display_name":"Indian Institute of Science Bangalore","ror":"https://ror.org/04dese585","country_code":"IN","type":"education","lineage":["https://openalex.org/I59270414"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"D. Nagesh Kumar","raw_affiliation_strings":["Indian Institute of Science, Bengaluru, India"],"affiliations":[{"raw_affiliation_string":"Indian Institute of Science, Bengaluru, India","institution_ids":["https://openalex.org/I59270414"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5009598295"],"corresponding_institution_ids":["https://openalex.org/I59270414"],"apc_list":null,"apc_paid":null,"fwci":0.9545,"has_fulltext":false,"cited_by_count":14,"citation_normalized_percentile":{"value":0.81767638,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":"59","issue":"4","first_page":"3352","last_page":"3368"},"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/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9970999956130981,"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":0.9923999905586243,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/multispectral-image","display_name":"Multispectral image","score":0.801727294921875},{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.7738748788833618},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6447719931602478},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.6151801943778992},{"id":"https://openalex.org/keywords/transformation","display_name":"Transformation (genetics)","score":0.5885436534881592},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5600003004074097},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.504485011100769},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.497121125459671},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.49399834871292114},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4590074419975281},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.4158458113670349},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.368444561958313},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.1264697015285492},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.0858255922794342}],"concepts":[{"id":"https://openalex.org/C173163844","wikidata":"https://www.wikidata.org/wiki/Q1761440","display_name":"Multispectral image","level":2,"score":0.801727294921875},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.7738748788833618},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6447719931602478},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.6151801943778992},{"id":"https://openalex.org/C204241405","wikidata":"https://www.wikidata.org/wiki/Q461499","display_name":"Transformation (genetics)","level":3,"score":0.5885436534881592},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5600003004074097},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.504485011100769},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.497121125459671},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.49399834871292114},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4590074419975281},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.4158458113670349},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.368444561958313},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.1264697015285492},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0858255922794342},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tgrs.2020.3009290","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2020.3009290","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"},{"id":"pmh:oai:eprints.iisc.ac.in:68712","is_oa":false,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4306401429","display_name":"ePrints@IISc (Indian Institute of Science)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I59270414","host_organization_name":"Indian Institute of Science Bangalore","host_organization_lineage":["https://openalex.org/I59270414"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":"","raw_type":"Journal Article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G86736951","display_name":null,"funder_award_id":"MoES/PAMC/H&C/41/2013-PC-II","funder_id":"https://openalex.org/F4320321026","funder_display_name":"Ministry of Earth Sciences"}],"funders":[{"id":"https://openalex.org/F4320321026","display_name":"Ministry of Earth Sciences","ror":"https://ror.org/013cf5k59"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":33,"referenced_works":["https://openalex.org/W1885185971","https://openalex.org/W1928973393","https://openalex.org/W1993699518","https://openalex.org/W2001970031","https://openalex.org/W2029316659","https://openalex.org/W2108171831","https://openalex.org/W2133665775","https://openalex.org/W2193045528","https://openalex.org/W2345295604","https://openalex.org/W2345557152","https://openalex.org/W2415594836","https://openalex.org/W2469029062","https://openalex.org/W2500751094","https://openalex.org/W2519653196","https://openalex.org/W2520826941","https://openalex.org/W2533102868","https://openalex.org/W2547307714","https://openalex.org/W2557283755","https://openalex.org/W2572303978","https://openalex.org/W2765280539","https://openalex.org/W2781674358","https://openalex.org/W2790128178","https://openalex.org/W2792249172","https://openalex.org/W2803946774","https://openalex.org/W2941387379","https://openalex.org/W2965729626","https://openalex.org/W2967229946","https://openalex.org/W2980085412","https://openalex.org/W2981468638","https://openalex.org/W3015135619","https://openalex.org/W6640169470","https://openalex.org/W6704703165","https://openalex.org/W6720339662"],"related_works":["https://openalex.org/W2072166414","https://openalex.org/W3209970181","https://openalex.org/W2060875994","https://openalex.org/W4318664220","https://openalex.org/W3034375524","https://openalex.org/W4230131218","https://openalex.org/W2070598848","https://openalex.org/W2022304901","https://openalex.org/W2018850895","https://openalex.org/W1987483041"],"abstract_inverted_index":{"Hyperspectral":[0],"(HS)":[1],"data":[2,13,35,60,63,98,166,195,209,244,248,265],"are":[3,175,210,249],"proven":[4,250],"to":[5,10,94,142,164,177,215,235,251,263],"be":[6,65,252,269],"more":[7,232],"resourceful":[8,253],"compared":[9,105,234],"multispectral":[11],"(MS)":[12],"for":[14,69,89,162,254,275],"object":[15],"detection,":[16],"classification,":[17],"and":[18,39,42,123,145,206,238,245],"several":[19],"other":[20],"applications.":[21],"However,":[22],"absence":[23,280],"of":[24,45,52,56,79,137,156,189,192,198,221,281],"any":[25],"space-borne":[26],"HS":[27,53,283],"sensor":[28],"since":[29],"2017,":[30],"which":[31],"can":[32,64,268],"provide":[33],"open-source":[34],"with":[36,106,196,213],"global":[37],"coverage,":[38],"high":[40],"cost":[41],"limited":[43],"obtainability":[44],"airborne":[46],"sensors-based":[47],"images":[48],"limit":[49],"the":[50,77,107,135,138,148,153,193,199,219,223,242,279],"use":[51,78],"data.":[54,140,202],"Transformation":[55],"readily":[57],"available":[58],"MS":[59,90,163,262],"into":[61],"quasi-HS":[62,95,165,264],"a":[66,85,114,124,272],"feasible":[67],"solution":[68],"this":[70,73,246],"issue.":[71],"In":[72],"article,":[74],"we":[75],"propose":[76],"convolutional":[80],"neural":[81],"network":[82],"regression":[83,121,131],"(CNNR),":[84],"deep":[86,158],"learning-based":[87,159],"algorithm,":[88],"(i.e.,":[91,96],"Landsat":[92],"7/8)":[93],"quasi-Hyperion)":[97],"transformation.":[99],"The":[100,203,258],"proposed":[101,149,259],"CNNR":[102,150,229,260],"model":[103,117,151,230],"is":[104,226,231],"existing":[108,144],"pseudo-HS":[109],"image":[110],"transformation":[111,167,266],"algorithm":[112],"(PHITA),":[113],"simple":[115,146],"linear":[116,120],"[i.e.,":[118,128],"stepwise":[119],"(SLR)],":[122],"nonlinear":[125,170],"modeling":[126],"approach":[127,267],"support":[129],"vector":[130],"(SVR)]":[132],"by":[133],"evaluating":[134],"quality":[136],"quasi-Hyperion":[139,194,208,243],"Contrary":[141],"these":[143],"models,":[147],"has":[152],"added":[154],"advantage":[155],"utilizing":[157],"spectral-spatial":[160],"features":[161],"through":[168],"regression-based":[169],"modeling.":[171],"Different":[172],"statistical":[173],"metrics":[174],"calculated":[176],"compare":[178],"each":[179,190],"band's":[180],"reflectance":[181,187],"values":[182],"as":[183,185,271],"well":[184],"spectral":[186],"curve":[188],"pixel":[191],"that":[197,228],"original":[200,282],"Hyperion":[201],"developed":[204],"models":[205],"generated":[207],"also":[211],"evaluated":[212],"application":[214],"crop":[216,255],"classification.":[217],"Analyzing":[218],"results":[220],"all":[222],"experiments,":[224],"it":[225],"evident":[227],"efficient":[233],"PHITA,":[236],"SLR,":[237],"SVR":[239],"in":[240,278],"creating":[241],"transformed":[247],"classification":[256],"application.":[257],"model-based":[261],"used":[270],"viable":[273],"alternative":[274],"different":[276],"applications":[277],"images.":[284]},"counts_by_year":[{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":2}],"updated_date":"2026-03-08T08:50:53.379069","created_date":"2025-10-10T00:00:00"}