{"id":"https://openalex.org/W2944125875","doi":"https://doi.org/10.1109/ipas.2018.8708899","title":"Deep Learning for Hyperspectral Image Classification on Embedded Platforms","display_name":"Deep Learning for Hyperspectral Image Classification on Embedded Platforms","publication_year":2018,"publication_date":"2018-12-01","ids":{"openalex":"https://openalex.org/W2944125875","doi":"https://doi.org/10.1109/ipas.2018.8708899","mag":"2944125875"},"language":"en","primary_location":{"id":"doi:10.1109/ipas.2018.8708899","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ipas.2018.8708899","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE International Conference on Image Processing, Applications and Systems (IPAS)","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/A5003023603","display_name":"Siddharth Balakrishnan","orcid":null},"institutions":[{"id":"https://openalex.org/I170201317","display_name":"University of Pittsburgh","ror":"https://ror.org/01an3r305","country_code":"US","type":"education","lineage":["https://openalex.org/I170201317"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Siddharth Balakrishnan","raw_affiliation_strings":["Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, USA","institution_ids":["https://openalex.org/I170201317"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5042589258","display_name":"David Langerman","orcid":"https://orcid.org/0000-0001-8777-4655"},"institutions":[{"id":"https://openalex.org/I170201317","display_name":"University of Pittsburgh","ror":"https://ror.org/01an3r305","country_code":"US","type":"education","lineage":["https://openalex.org/I170201317"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"David Langerman","raw_affiliation_strings":["Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, USA","institution_ids":["https://openalex.org/I170201317"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012346902","display_name":"Evan W. Gretok","orcid":null},"institutions":[{"id":"https://openalex.org/I170201317","display_name":"University of Pittsburgh","ror":"https://ror.org/01an3r305","country_code":"US","type":"education","lineage":["https://openalex.org/I170201317"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Evan Gretok","raw_affiliation_strings":["Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, USA","institution_ids":["https://openalex.org/I170201317"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5082898376","display_name":"Alan D. George","orcid":"https://orcid.org/0000-0001-9665-2879"},"institutions":[{"id":"https://openalex.org/I170201317","display_name":"University of Pittsburgh","ror":"https://ror.org/01an3r305","country_code":"US","type":"education","lineage":["https://openalex.org/I170201317"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Alan D. George","raw_affiliation_strings":["Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, USA","institution_ids":["https://openalex.org/I170201317"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I170201317"],"apc_list":null,"apc_paid":null,"fwci":0.2092,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.63529345,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"12","issue":null,"first_page":"187","last_page":"191"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9994999766349792,"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":0.9994999766349792,"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":0.9739000201225281,"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/T12389","display_name":"Infrared Target Detection Methodologies","score":0.9732000231742859,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.8727343678474426},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7005714178085327},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6012108325958252},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.5243363976478577},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.47735002636909485},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.44317182898521423},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.42803192138671875},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.40956923365592957},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.3759371042251587},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.24950441718101501}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.8727343678474426},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7005714178085327},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6012108325958252},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.5243363976478577},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.47735002636909485},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.44317182898521423},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.42803192138671875},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.40956923365592957},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.3759371042251587},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.24950441718101501}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ipas.2018.8708899","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ipas.2018.8708899","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE International Conference on Image Processing, Applications and Systems (IPAS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5299999713897705,"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W1521436688","https://openalex.org/W1642806187","https://openalex.org/W1966580635","https://openalex.org/W1977177161","https://openalex.org/W2013482901","https://openalex.org/W2101234009","https://openalex.org/W2127332912","https://openalex.org/W2164669345","https://openalex.org/W2402144811","https://openalex.org/W2523246573","https://openalex.org/W2558391528","https://openalex.org/W2573524522","https://openalex.org/W2611452721","https://openalex.org/W2617007462","https://openalex.org/W2791339614","https://openalex.org/W2953384591","https://openalex.org/W3104795559","https://openalex.org/W6675354045","https://openalex.org/W6713134421","https://openalex.org/W6727249380","https://openalex.org/W6730044376"],"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/W2385371209","https://openalex.org/W4250051149","https://openalex.org/W2083270190","https://openalex.org/W1991437568","https://openalex.org/W2795259429"],"abstract_inverted_index":{"Hyperspectral":[0],"image":[1,19],"(HSI)":[2],"analysis":[3,56],"refers":[4],"to":[5,9,75,89,173],"the":[6,26,80,119,138,141,151,169,184,192],"processes":[7],"used":[8,122],"identify":[10],"and":[11,50,91,113,134,140,147,195],"classify":[12],"objects":[13],"photographed":[14],"using":[15],"equipment":[16],"that":[17],"can":[18],"photons":[20],"from":[21,33],"a":[22,44,69,131],"broad":[23],"range":[24],"of":[25,47,71,73,85,177],"electromagnetic":[27],"spectrum.":[28],"Downlinking":[29],"such":[30,55],"large":[31,45],"images":[32,62,72],"space":[34,58],"on":[35,99,130,158],"radiation-resistant":[36],"platforms":[37,101,186],"with":[38,102],"limited":[39,103],"on-board":[40],"computing":[41,104],"power":[42],"takes":[43],"amount":[46],"time,":[48],"memory,":[49],"other":[51],"mission-critical":[52],"resources.":[53,105],"Performing":[54],"in":[57,123,161],"before":[59],"downlinking":[60],"all":[61],"will":[63],"save":[64],"these":[65],"resources":[66],"by":[67],"enabling":[68],"subset":[70],"interest":[74],"be":[76],"downloaded":[77],"rather":[78],"than":[79],"entire":[81],"set.":[82],"The":[83],"goal":[84],"this":[86,124,162],"study":[87],"is":[88,166,181],"benchmark":[90],"evaluate":[92],"HSI-classification":[93],"methods":[94,121],"which":[95],"incorporate":[96],"deep":[97],"learning":[98],"embedded":[100,136,185],"Support":[106],"Vector":[107],"Machine":[108],"(SVM),":[109],"Multi-Layer":[110],"Perceptron":[111],"(MLP),":[112],"Convolutional":[114],"Neural":[115],"Network":[116],"(CNN)":[117],"are":[118],"classification":[120,165,180],"study.":[125],"These":[126],"algorithms":[127],"were":[128],"executed":[129],"desktop":[132,170],"PC":[133,171],"two":[135],"platforms:":[137],"ODROID-C2":[139],"Raspberry":[142],"Pi":[143],"3B.":[144],"Accuracy,":[145],"run-time,":[146],"memory":[148],"benchmarks":[149],"determined":[150],"optimal":[152],"model":[153],"for":[154,168,183],"each":[155],"platform.":[156],"Based":[157],"results":[159],"gathered":[160],"research,":[163],"CNN":[164],"recommended":[167,182],"due":[172],"its":[174],"high":[175],"accuracy":[176],"97%.":[178],"MLP":[179],"under":[187],"study,":[188],"as":[189],"it":[190],"showcased":[191],"shortest":[193],"run-time":[194],"second-highest":[196],"accuracy.":[197]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}
