{"id":"https://openalex.org/W2293136015","doi":"https://doi.org/10.1109/icip.2015.7351171","title":"Hyperspectral classification using a composite kernel driven by nearest-neighbor spatial features","display_name":"Hyperspectral classification using a composite kernel driven by nearest-neighbor spatial features","publication_year":2015,"publication_date":"2015-09-01","ids":{"openalex":"https://openalex.org/W2293136015","doi":"https://doi.org/10.1109/icip.2015.7351171","mag":"2293136015"},"language":"en","primary_location":{"id":"doi:10.1109/icip.2015.7351171","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2015.7351171","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 Image Processing (ICIP)","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/A5087934737","display_name":"Vineetha Menon","orcid":"https://orcid.org/0000-0001-6916-5346"},"institutions":[{"id":"https://openalex.org/I99041443","display_name":"Mississippi State University","ror":"https://ror.org/0432jq872","country_code":"US","type":"education","lineage":["https://openalex.org/I4210141039","https://openalex.org/I99041443"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Vineetha Menon","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Mississippi State University, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Mississippi State University, USA","institution_ids":["https://openalex.org/I99041443"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037823063","display_name":"Saurabh Prasad","orcid":"https://orcid.org/0000-0003-3729-9360"},"institutions":[{"id":"https://openalex.org/I44461941","display_name":"University of Houston","ror":"https://ror.org/048sx0r50","country_code":"US","type":"education","lineage":["https://openalex.org/I44461941"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Saurabh Prasad","raw_affiliation_strings":["Department of Electrical and Computer Engineering, University of Houston, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of Houston, USA","institution_ids":["https://openalex.org/I44461941"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5078795882","display_name":"James E. Fowler","orcid":"https://orcid.org/0000-0003-2005-405X"},"institutions":[{"id":"https://openalex.org/I99041443","display_name":"Mississippi State University","ror":"https://ror.org/0432jq872","country_code":"US","type":"education","lineage":["https://openalex.org/I4210141039","https://openalex.org/I99041443"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"James E. Fowler","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Mississippi State University, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Mississippi State University, USA","institution_ids":["https://openalex.org/I99041443"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":3.3671,"has_fulltext":false,"cited_by_count":14,"citation_normalized_percentile":{"value":0.93597115,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":"52","issue":null,"first_page":"2100","last_page":"2104"},"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":0.9969000220298767,"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/T10111","display_name":"Remote Sensing in Agriculture","score":0.9781000018119812,"subfield":{"id":"https://openalex.org/subfields/2303","display_name":"Ecology"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental 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":0.7576644420623779},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7055720686912537},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.6814984679222107},{"id":"https://openalex.org/keywords/k-nearest-neighbors-algorithm","display_name":"k-nearest neighbors algorithm","score":0.6783648133277893},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6174319982528687},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5660485029220581},{"id":"https://openalex.org/keywords/composite-number","display_name":"Composite number","score":0.468948096036911},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.32268476486206055},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.19292348623275757},{"id":"https://openalex.org/keywords/combinatorics","display_name":"Combinatorics","score":0.07270842790603638}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.7576644420623779},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7055720686912537},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.6814984679222107},{"id":"https://openalex.org/C113238511","wikidata":"https://www.wikidata.org/wiki/Q1071612","display_name":"k-nearest neighbors algorithm","level":2,"score":0.6783648133277893},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6174319982528687},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5660485029220581},{"id":"https://openalex.org/C104779481","wikidata":"https://www.wikidata.org/wiki/Q50707","display_name":"Composite number","level":2,"score":0.468948096036911},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.32268476486206055},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.19292348623275757},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.07270842790603638}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip.2015.7351171","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2015.7351171","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 Image Processing (ICIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.550000011920929,"id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W2002849025","https://openalex.org/W2090826137","https://openalex.org/W2104269704","https://openalex.org/W2112129724","https://openalex.org/W2120184245","https://openalex.org/W2129652905","https://openalex.org/W2131450755","https://openalex.org/W2131697388","https://openalex.org/W2132648706","https://openalex.org/W2145649962","https://openalex.org/W2154240401","https://openalex.org/W2164330327","https://openalex.org/W2171171329","https://openalex.org/W6684897833"],"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/W2062957446","https://openalex.org/W2147397890","https://openalex.org/W2166213322","https://openalex.org/W2155880659","https://openalex.org/W894329006"],"abstract_inverted_index":{"There":[0],"is":[1,35,60,73,95,103],"increasing":[2],"interest":[3],"in":[4,75,80,142],"driving":[5],"supervised":[6,139],"classification":[7,141],"of":[8,30,133],"hyperspectral":[9,140],"imagery":[10],"by":[11],"a":[12,17,44,54,64,69],"support":[13,153],"vector":[14,154],"machine":[15],"using":[16],"composite":[18],"kernel":[19],"employing":[20],"both":[21],"spectral":[22,28,42],"and":[23],"spatial":[24,55,65,71,92,101],"features.":[25],"While":[26],"the":[27,31,41,49,58,77,84,91,98,130,134],"signature":[29],"current":[32,85],"hyper-spectral":[33],"pixel":[34,59,86],"often":[36],"used":[37,88],"directly":[38],"to":[39,83,89,106,125,144],"supply":[40],"feature,":[43],"statistic":[45],"-":[46,51],"such":[47],"as":[48,63],"mean":[50],"calculated":[52],"across":[53],"window":[56],"surrounding":[57],"typically":[61],"employed":[62],"feature.":[66,93],"In":[67],"contrast,":[68],"nearest-neighbor":[70,100,136],"feature":[72,102],"proposed":[74,99,135],"which":[76,116],"nearest":[78],"neighbors":[79],"Euclidean":[81],"distance":[82],"are":[87],"calculate":[90],"It":[94],"argued":[96],"that":[97,149],"more":[104],"likely":[105],"incorporate":[107],"relevant,":[108],"same-class":[109],"neighbor":[110],"pixels":[111],"than":[112],"window-based":[113],"features":[114],"for":[115],"borders":[117],"between":[118],"coherent":[119],"single-class":[120],"regions":[121],"may":[122],"give":[123],"rise":[124],"misclassification.":[126],"Experimental":[127],"results":[128],"illustrate":[129],"performance":[131],"advantage":[132],"framework":[137],"at":[138],"comparison":[143],"several":[145],"competing":[146],"benchmark":[147],"algorithms":[148],"also":[150],"employ":[151],"kernel-based":[152],"machines.":[155]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":3},{"year":2017,"cited_by_count":3},{"year":2016,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}