{"id":"https://openalex.org/W2791254153","doi":"https://doi.org/10.3390/rs10030441","title":"Classification of Hyperspectral Images by SVM Using a Composite Kernel by Employing Spectral, Spatial and Hierarchical Structure Information","display_name":"Classification of Hyperspectral Images by SVM Using a Composite Kernel by Employing Spectral, Spatial and Hierarchical Structure Information","publication_year":2018,"publication_date":"2018-03-12","ids":{"openalex":"https://openalex.org/W2791254153","doi":"https://doi.org/10.3390/rs10030441","mag":"2791254153"},"language":"en","primary_location":{"id":"doi:10.3390/rs10030441","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs10030441","pdf_url":"https://www.mdpi.com/2072-4292/10/3/441/pdf?version=1520818571","source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Remote Sensing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2072-4292/10/3/441/pdf?version=1520818571","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100364886","display_name":"Yi Wang","orcid":"https://orcid.org/0000-0002-1347-7030"},"institutions":[{"id":"https://openalex.org/I3124059619","display_name":"China University of Geosciences","ror":"https://ror.org/04gcegc37","country_code":"CN","type":"education","lineage":["https://openalex.org/I3124059619"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yi Wang","raw_affiliation_strings":["Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China"],"affiliations":[{"raw_affiliation_string":"Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China","institution_ids":["https://openalex.org/I3124059619"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5060974978","display_name":"Hexiang Duan","orcid":null},"institutions":[{"id":"https://openalex.org/I3124059619","display_name":"China University of Geosciences","ror":"https://ror.org/04gcegc37","country_code":"CN","type":"education","lineage":["https://openalex.org/I3124059619"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hexiang Duan","raw_affiliation_strings":["Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China"],"affiliations":[{"raw_affiliation_string":"Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China","institution_ids":["https://openalex.org/I3124059619"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5100364886"],"corresponding_institution_ids":["https://openalex.org/I3124059619"],"apc_list":{"value":2500,"currency":"CHF","value_usd":2707},"apc_paid":{"value":2500,"currency":"CHF","value_usd":2707},"fwci":5.0186,"has_fulltext":true,"cited_by_count":52,"citation_normalized_percentile":{"value":0.9548534,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":"10","issue":"3","first_page":"441","last_page":"441"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":1.0,"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":1.0,"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":0.9954000115394592,"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/T11667","display_name":"Advanced Chemical Sensor Technologies","score":0.9412999749183655,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical 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/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7182233929634094},{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.707397997379303},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6237672567367554},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.5991562604904175},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5954357981681824},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.5099138021469116},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5092755556106567},{"id":"https://openalex.org/keywords/spatial-analysis","display_name":"Spatial analysis","score":0.45338213443756104},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.4339657723903656},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3067214787006378},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.24195212125778198},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.0908658504486084}],"concepts":[{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7182233929634094},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.707397997379303},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6237672567367554},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.5991562604904175},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5954357981681824},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.5099138021469116},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5092755556106567},{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.45338213443756104},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.4339657723903656},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3067214787006378},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.24195212125778198},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0908658504486084},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.3390/rs10030441","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs10030441","pdf_url":"https://www.mdpi.com/2072-4292/10/3/441/pdf?version=1520818571","source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Remote Sensing","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:2fd962fd054d48f0b5611c461da9e61f","is_oa":true,"landing_page_url":"https://doaj.org/article/2fd962fd054d48f0b5611c461da9e61f","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Remote Sensing, Vol 10, Iss 3, p 441 (2018)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/2072-4292/10/3/441/","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/rs10030441","pdf_url":null,"source":{"id":"https://openalex.org/S4306400947","display_name":"MDPI (MDPI AG)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210097602","host_organization_name":"Multidisciplinary Digital Publishing Institute (Switzerland)","host_organization_lineage":["https://openalex.org/I4210097602"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Remote Sensing; Volume 10; Issue 3; Pages: 441","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/rs10030441","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs10030441","pdf_url":"https://www.mdpi.com/2072-4292/10/3/441/pdf?version=1520818571","source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Remote Sensing","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2087396116","display_name":null,"funder_award_id":"China","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G2174087448","display_name":null,"funder_award_id":"61271408","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3317480652","display_name":null,"funder_award_id":"Science","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5994120800","display_name":null,"funder_award_id":"Natural","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320309036","display_name":"Purdue University","ror":"https://ror.org/02dqehb95"},{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320322874","display_name":"Universit\u00e0 degli Studi di Pavia","ror":"https://ror.org/00s6t1f81"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2791254153.pdf","grobid_xml":"https://content.openalex.org/works/W2791254153.grobid-xml"},"referenced_works_count":67,"referenced_works":["https://openalex.org/W59495185","https://openalex.org/W1522547150","https://openalex.org/W1939429412","https://openalex.org/W1961194538","https://openalex.org/W1988048998","https://openalex.org/W1992961908","https://openalex.org/W1997542937","https://openalex.org/W1998030734","https://openalex.org/W2001298023","https://openalex.org/W2005353255","https://openalex.org/W2008847349","https://openalex.org/W2011745746","https://openalex.org/W2043665634","https://openalex.org/W2044439250","https://openalex.org/W2057540576","https://openalex.org/W2059089906","https://openalex.org/W2062964394","https://openalex.org/W2064604707","https://openalex.org/W2067532478","https://openalex.org/W2082627840","https://openalex.org/W2085529604","https://openalex.org/W2092071303","https://openalex.org/W2095190520","https://openalex.org/W2098057602","https://openalex.org/W2101057528","https://openalex.org/W2101711129","https://openalex.org/W2105386417","https://openalex.org/W2107919956","https://openalex.org/W2113464037","https://openalex.org/W2113513024","https://openalex.org/W2114456237","https://openalex.org/W2114819256","https://openalex.org/W2127495569","https://openalex.org/W2129652905","https://openalex.org/W2130463078","https://openalex.org/W2131697388","https://openalex.org/W2131864940","https://openalex.org/W2136251662","https://openalex.org/W2138875721","https://openalex.org/W2146611644","https://openalex.org/W2149471024","https://openalex.org/W2149870286","https://openalex.org/W2150134853","https://openalex.org/W2154506590","https://openalex.org/W2154874087","https://openalex.org/W2156982777","https://openalex.org/W2158400785","https://openalex.org/W2160662337","https://openalex.org/W2164330327","https://openalex.org/W2164822588","https://openalex.org/W2165771856","https://openalex.org/W2166923144","https://openalex.org/W2179684938","https://openalex.org/W2242603519","https://openalex.org/W2325569213","https://openalex.org/W2487659399","https://openalex.org/W2518759513","https://openalex.org/W2547534670","https://openalex.org/W2551480915","https://openalex.org/W2598259734","https://openalex.org/W2613575128","https://openalex.org/W4239510810","https://openalex.org/W4320339642","https://openalex.org/W6675576870","https://openalex.org/W6677065643","https://openalex.org/W6684666805","https://openalex.org/W6685818685"],"related_works":["https://openalex.org/W2072166414","https://openalex.org/W3209970181","https://openalex.org/W2070598848","https://openalex.org/W3034375524","https://openalex.org/W2060875994","https://openalex.org/W2027399350","https://openalex.org/W2044184146","https://openalex.org/W2019190440","https://openalex.org/W2343470940","https://openalex.org/W3034864990"],"abstract_inverted_index":{"In":[0,22],"this":[1,23,121,162],"paper,":[2],"we":[3],"introduce":[4],"a":[5,37,137,141],"novel":[6],"classification":[7,173,200,206,219],"framework":[8,196,220],"for":[9,108,171],"hyperspectral":[10,187],"images":[11,190],"(HSIs)":[12],"by":[13,63,153,174],"jointly":[14],"employing":[15],"spectral,":[16],"spatial,":[17],"and":[18,57,74,92,213],"hierarchical":[19,79,89,148],"structure":[20,80,119,149,177],"information.":[21],"framework,":[24],"the":[25,33,43,54,58,65,82,99,105,117,126,130,147,194,217,228,237],"three":[26],"types":[27],"of":[28,39,84,120,140,143,161,210,239],"information":[29,150,178],"are":[30,96],"integrated":[31],"into":[32],"SVM":[34,230],"classifier":[35,231],"in":[36,53,179,208,224,236],"way":[38],"multiple":[40,180],"kernels.":[41],"Specifically,":[42,216],"spectral":[44,114],"kernel":[45,60,170],"is":[46,61,102,123,133,151,164],"constructed":[47,124],"through":[48],"each":[49],"pixel\u2019s":[50],"vector":[51],"value":[52],"original":[55,106],"HSI,":[56],"spatial":[59,176],"modeled":[62],"using":[64,125,154],"extended":[66],"morphological":[67],"profile":[68],"method":[69],"due":[70],"to":[71,111,135,165,191],"its":[72,113],"simplicity":[73],"effectiveness.":[75],"To":[76],"accurately":[77],"characterize":[78],"features,":[81],"techniques":[83],"Fish-Markov":[85],"selector":[86],"(FMS),":[87],"marker-based":[88],"segmentation":[90,144,156],"(MHSEG)":[91],"algebraic":[93],"multigrid":[94,118],"(AMG)":[95],"combined.":[97],"First,":[98],"FMS":[100],"algorithm":[101,132],"used":[103],"on":[104,185],"HSI":[107,172],"feature":[109],"selection":[110],"produce":[112],"subset.":[115],"Then,":[116],"subset":[122],"AMG":[127],"method.":[128],"Subsequently,":[129],"MHSEG":[131],"exploited":[134],"obtain":[136],"hierarchy":[138],"consist":[139],"series":[142],"maps.":[145,157],"Finally,":[146],"represented":[152],"these":[155],"The":[158],"main":[159],"contributions":[160],"work":[163],"present":[166],"an":[167],"effective":[168],"composite":[169],"utilizing":[175],"scales.":[181],"Experiments":[182],"were":[183],"conducted":[184],"two":[186],"remote":[188],"sensing":[189],"validate":[192],"that":[193],"proposed":[195,218],"can":[197,221],"achieve":[198,222],"better":[199],"results":[201],"than":[202,227],"several":[203],"popular":[204],"kernel-based":[205],"methods":[207],"terms":[209,238],"both":[211],"qualitative":[212],"quantitative":[214],"analysis.":[215],"13.46\u201315.61%":[223],"average":[225],"higher":[226],"standard":[229],"under":[232],"different":[233],"training":[234],"sets":[235],"overall":[240],"accuracy.":[241]},"counts_by_year":[{"year":2025,"cited_by_count":10},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":6},{"year":2022,"cited_by_count":7},{"year":2021,"cited_by_count":8},{"year":2020,"cited_by_count":9},{"year":2019,"cited_by_count":5},{"year":2018,"cited_by_count":2}],"updated_date":"2026-04-18T07:56:08.524223","created_date":"2018-03-29T00:00:00"}