{"id":"https://openalex.org/W4406457874","doi":"https://doi.org/10.1109/bigdata62323.2024.10825513","title":"Scalable SubXPCA with Extended Morphological Profiles Applied to Hyperspectral Image Classification on a Distributed Platform","display_name":"Scalable SubXPCA with Extended Morphological Profiles Applied to Hyperspectral Image Classification on a Distributed Platform","publication_year":2024,"publication_date":"2024-12-15","ids":{"openalex":"https://openalex.org/W4406457874","doi":"https://doi.org/10.1109/bigdata62323.2024.10825513"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata62323.2024.10825513","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata62323.2024.10825513","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Big Data (BigData)","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/A5039309624","display_name":"B. Rupa","orcid":null},"institutions":[{"id":"https://openalex.org/I36893310","display_name":"University of Hyderabad","ror":"https://ror.org/04a7rxb17","country_code":"IN","type":"education","lineage":["https://openalex.org/I36893310"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Bogolu Rupa","raw_affiliation_strings":["University of Hyderabad,School of Computer and Information Sciences,Hyderabad,Telangana,India"],"affiliations":[{"raw_affiliation_string":"University of Hyderabad,School of Computer and Information Sciences,Hyderabad,Telangana,India","institution_ids":["https://openalex.org/I36893310"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5070889481","display_name":"Atul Negi","orcid":"https://orcid.org/0000-0001-5707-130X"},"institutions":[{"id":"https://openalex.org/I36893310","display_name":"University of Hyderabad","ror":"https://ror.org/04a7rxb17","country_code":"IN","type":"education","lineage":["https://openalex.org/I36893310"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Atul Negi","raw_affiliation_strings":["University of Hyderabad,School of Computer and Information Sciences,Hyderabad,Telangana,India"],"affiliations":[{"raw_affiliation_string":"University of Hyderabad,School of Computer and Information Sciences,Hyderabad,Telangana,India","institution_ids":["https://openalex.org/I36893310"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5039309624"],"corresponding_institution_ids":["https://openalex.org/I36893310"],"apc_list":null,"apc_paid":null,"fwci":0.3374,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.67886079,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"3532","last_page":"3537"},"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/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.9934999942779541,"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"}},{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.9804999828338623,"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/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.8578243255615234},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7269542217254639},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.6719111204147339},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.5814247131347656},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5365128517150879},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.47323545813560486},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.43783125281333923},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4302331507205963},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.08056285977363586}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.8578243255615234},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7269542217254639},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.6719111204147339},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.5814247131347656},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5365128517150879},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.47323545813560486},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.43783125281333923},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4302331507205963},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.08056285977363586}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata62323.2024.10825513","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata62323.2024.10825513","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Big Data (BigData)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W1963784540","https://openalex.org/W1992961908","https://openalex.org/W2043694524","https://openalex.org/W2066700623","https://openalex.org/W2074058676","https://openalex.org/W2087263574","https://openalex.org/W2092781721","https://openalex.org/W2114819256","https://openalex.org/W2121456247","https://openalex.org/W2145962650","https://openalex.org/W2295124130","https://openalex.org/W2338459354","https://openalex.org/W2505399031","https://openalex.org/W2530314904","https://openalex.org/W2755992512","https://openalex.org/W2767805377","https://openalex.org/W2793272303","https://openalex.org/W2911494248","https://openalex.org/W3002674187","https://openalex.org/W4230982142","https://openalex.org/W4380202760","https://openalex.org/W4387709423","https://openalex.org/W4389160548","https://openalex.org/W4391924597"],"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/W2404757046","https://openalex.org/W2044184146","https://openalex.org/W2070598848","https://openalex.org/W2019190440","https://openalex.org/W2546503577"],"abstract_inverted_index":{"The":[0,163,180],"resolution":[1],"of":[2,5,23,45,83,175,187,210],"large":[3],"volumes":[4,44],"high-dimensional":[6],"hyperspectral":[7,49,84,141],"images":[8,65],"have":[9],"increased":[10],"significantly":[11],"due":[12],"to":[13,40,60],"the":[14,24,95,118,146,156,173,178,208],"ongoing":[15],"advancements":[16],"in":[17,110,172,177,192,207],"satellite":[18],"remote":[19],"sensing":[20],"technology.":[21],"One":[22],"most":[25],"well-known,":[26],"widely":[27],"used,":[28],"and":[29,66,79,102,124,139,160,190,197],"open-source":[30],"distributed":[31,75,114],"processing":[32],"framework":[33],"is":[34,38,53,153],"Apache":[35],"Spark,":[36],"which":[37],"proven":[39],"quickly":[41],"handle":[42],"enormous":[43],"these":[46,62],"remotely":[47],"sensed":[48],"images.":[50,85],"Therefore,":[51],"there":[52],"a":[54,74,111,184,193,198],"need":[55],"for":[56,77,98,106],"dimensionality":[57,81,100,108,127],"reduction":[58,82,101,109,128,149,158],"algorithms":[59],"process":[61],"high":[63],"dimensional":[64],"achieve":[67],"accurate":[68],"application":[69],"performance.":[70],"This":[71],"paper":[72],"suggests":[73],"approach":[76],"spectral":[78,99,157],"spatial":[80,107],"We":[86,116],"implemented":[87],"feature":[88,123],"partitioning":[89,126],"principal":[90],"component":[91],"analysis":[92],"based":[93],"on":[94,136],"SubXPCA":[96],"method":[97,120,150],"Extended":[103,131],"Morphological":[104,132],"Profiles":[105],"Spark":[112,195],"cluster":[113,196,202],"environment.":[115],"compared":[117],"proposed":[119,147,164,181],"against":[121],"other":[122],"non-feature":[125],"methods":[129,159],"with":[130,169,204],"Profiles.":[133],"Our":[134],"experiments":[135],"different":[137],"real":[138],"synthetic":[140],"image":[142],"datasets":[143],"confirm":[144],"that":[145],"spectral-spatial":[148],"classification":[151],"performance":[152],"better":[154],"than":[155],"their":[161],"competitors.":[162],"method\u2019s":[165],"running":[166],"time":[167],"decreases":[168],"an":[170,205],"increase":[171,206],"number":[174,209],"cores":[176],"cluster.":[179],"methodology":[182],"showed":[183],"speedup":[185],"factor":[186],"7.12\u00d7,":[188],"13.17\u00d7":[189],"more":[191],"standalone":[194],"Google":[199],"cloud":[200],"Dataproc":[201],"respectively,":[203],"cores.":[211]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}