{"id":"https://openalex.org/W2780885700","doi":"https://doi.org/10.1109/dicta.2017.8227429","title":"Exploring Kernel Based Spatial Context for CNN Based Hyperspectral Image Classification","display_name":"Exploring Kernel Based Spatial Context for CNN Based Hyperspectral Image Classification","publication_year":2017,"publication_date":"2017-11-01","ids":{"openalex":"https://openalex.org/W2780885700","doi":"https://doi.org/10.1109/dicta.2017.8227429","mag":"2780885700"},"language":"en","primary_location":{"id":"doi:10.1109/dicta.2017.8227429","is_oa":false,"landing_page_url":"https://doi.org/10.1109/dicta.2017.8227429","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA)","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/A5022330186","display_name":"Jingyu Ji","orcid":"https://orcid.org/0000-0001-6806-7437"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jingyu Ji","raw_affiliation_strings":["School of Electronics and Information, Northwestern Polytechnical University, Xi'an, China"],"affiliations":[{"raw_affiliation_string":"School of Electronics and Information, Northwestern Polytechnical University, Xi'an, China","institution_ids":["https://openalex.org/I17145004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067207818","display_name":"Shaohui Mei","orcid":"https://orcid.org/0000-0002-8018-596X"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shaohui Mei","raw_affiliation_strings":["School of Electronics and Information, Northwestern Polytechnical University, Xi'an, China"],"affiliations":[{"raw_affiliation_string":"School of Electronics and Information, Northwestern Polytechnical University, Xi'an, China","institution_ids":["https://openalex.org/I17145004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089671835","display_name":"Xiao Liu","orcid":"https://orcid.org/0000-0002-1159-9667"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiao Liu","raw_affiliation_strings":["School of Electronics and Information, Northwestern Polytechnical University, Xi'an, China"],"affiliations":[{"raw_affiliation_string":"School of Electronics and Information, Northwestern Polytechnical University, Xi'an, China","institution_ids":["https://openalex.org/I17145004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100342504","display_name":"Xu Li","orcid":"https://orcid.org/0009-0004-7507-4669"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xu Li","raw_affiliation_strings":["School of Electronics and Information, Northwestern Polytechnical University, Xi'an, China"],"affiliations":[{"raw_affiliation_string":"School of Electronics and Information, Northwestern Polytechnical University, Xi'an, China","institution_ids":["https://openalex.org/I17145004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038865738","display_name":"Shan Zeng","orcid":"https://orcid.org/0000-0003-1142-5613"},"institutions":[{"id":"https://openalex.org/I14116566","display_name":"Wuhan Polytechnic University","ror":"https://ror.org/05w0e5j23","country_code":"CN","type":"education","lineage":["https://openalex.org/I14116566"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shan Zeng","raw_affiliation_strings":["College of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan, China"],"affiliations":[{"raw_affiliation_string":"College of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan, China","institution_ids":["https://openalex.org/I14116566"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100443935","display_name":"Zhiyong Wang","orcid":"https://orcid.org/0000-0002-8043-0312"},"institutions":[{"id":"https://openalex.org/I129604602","display_name":"University of Sydney","ror":"https://ror.org/0384j8v12","country_code":"AU","type":"education","lineage":["https://openalex.org/I129604602"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Zhiyong Wang","raw_affiliation_strings":["School of Information Technologies, The University of Sydney, NSW, Australia"],"affiliations":[{"raw_affiliation_string":"School of Information Technologies, The University of Sydney, NSW, Australia","institution_ids":["https://openalex.org/I129604602"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5022330186"],"corresponding_institution_ids":["https://openalex.org/I17145004"],"apc_list":null,"apc_paid":null,"fwci":0.7063,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.77680907,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"7","issue":null,"first_page":"1","last_page":"7"},"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":0.9914000034332275,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9718999862670898,"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":0.8049100041389465},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7434200048446655},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7097055315971375},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.6726868152618408},{"id":"https://openalex.org/keywords/normalization","display_name":"Normalization (sociology)","score":0.6515780687332153},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6333774924278259},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5994031429290771},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.5552223324775696},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.4792459011077881},{"id":"https://openalex.org/keywords/spatial-contextual-awareness","display_name":"Spatial contextual awareness","score":0.45694124698638916},{"id":"https://openalex.org/keywords/spatial-analysis","display_name":"Spatial analysis","score":0.44838500022888184},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.43238192796707153},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3873668909072876},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.2454850673675537},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.09868016839027405},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.08453422784805298}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.8049100041389465},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7434200048446655},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7097055315971375},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.6726868152618408},{"id":"https://openalex.org/C136886441","wikidata":"https://www.wikidata.org/wiki/Q926129","display_name":"Normalization (sociology)","level":2,"score":0.6515780687332153},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6333774924278259},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5994031429290771},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.5552223324775696},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.4792459011077881},{"id":"https://openalex.org/C64754055","wikidata":"https://www.wikidata.org/wiki/Q7574053","display_name":"Spatial contextual awareness","level":2,"score":0.45694124698638916},{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.44838500022888184},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.43238192796707153},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3873668909072876},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2454850673675537},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.09868016839027405},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.08453422784805298},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0},{"id":"https://openalex.org/C19165224","wikidata":"https://www.wikidata.org/wiki/Q23404","display_name":"Anthropology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/dicta.2017.8227429","is_oa":false,"landing_page_url":"https://doi.org/10.1109/dicta.2017.8227429","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.4399999976158142,"display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W625476304","https://openalex.org/W1521436688","https://openalex.org/W1677182931","https://openalex.org/W1836465849","https://openalex.org/W1964541653","https://openalex.org/W1966580635","https://openalex.org/W1974981350","https://openalex.org/W1998030734","https://openalex.org/W2097915756","https://openalex.org/W2104269704","https://openalex.org/W2117463742","https://openalex.org/W2136251662","https://openalex.org/W2150341604","https://openalex.org/W2163922914","https://openalex.org/W2164330327","https://openalex.org/W2172009270","https://openalex.org/W2500751094","https://openalex.org/W2572303978","https://openalex.org/W2611655888","https://openalex.org/W2949117887","https://openalex.org/W4205947740","https://openalex.org/W6731975103"],"related_works":["https://openalex.org/W3148227991","https://openalex.org/W1486593826","https://openalex.org/W2771174107","https://openalex.org/W1536965844","https://openalex.org/W2344941099","https://openalex.org/W4322212724","https://openalex.org/W2106788855","https://openalex.org/W3081561710","https://openalex.org/W2477413883","https://openalex.org/W2463773089"],"abstract_inverted_index":{"Convolutional":[0],"Neural":[1],"Network":[2],"(CNN)":[3],"has":[4,21],"received":[5],"remarkable":[6],"achievements":[7],"in":[8,56,81,97,107,159],"hyperspectral":[9,39,146],"image":[10],"(HSI)":[11],"classification.":[12],"However,":[13],"how":[14],"to":[15,24,48,64,140,172],"effectively":[16],"implement":[17,65],"spatial":[18,67,99,174],"context":[19,68],"that":[20],"been":[22],"demonstrated":[23],"be":[25],"crucial":[26],"for":[27,38,129,176,181],"classification":[28,40,75,130,143,177],"of":[29,76,95,131,145,178,187],"HSI":[30],"is":[31],"still":[32],"an":[33],"open":[34],"issue.":[35],"Current":[36],"CNNs":[37],"are":[41,62,84,110,137,166],"restricted":[42],"into":[43,72],"a":[44,98],"small":[45,149,185],"scale":[46],"due":[47],"small-scale":[49,162],"input":[50],"and":[51,69,92,101,122,134],"limited":[52],"training":[53,113,188],"samples.":[54,189],"Therefore,":[55],"this":[57],"paper,":[58],"two":[59],"different":[60],"ways":[61],"proposed":[63],"both":[66],"spectral":[70],"signature":[71],"CNN":[73,128],"based":[74],"HSI:":[77],"1).":[78],"fixed":[79,164],"kernels":[80,106,165,171],"which":[82,108],"weights":[83,109],"determined":[85],"by":[86],"prior":[87],"information,":[88],"i.e.,":[89,115],"mean,":[90],"mean":[91],"standard":[93],"deviation":[94],"pixels":[96],"neighborhood,":[100],"Gaussian":[102],"kernel;":[103],"2).":[104],"learnable":[105,117,170],"learned":[111],"from":[112],"samples,":[114],"2D":[116],"kernel,":[118,121],"3D":[119],"convolutional":[120],"2-Layer":[123],"kernel.":[124],"In":[125],"the":[126,142,160,182],"successive":[127],"HSI,":[132],"dropout":[133],"batch":[135],"normalization":[136],"also":[138],"used":[139],"improve":[141],"performance":[144],"images":[147],"under":[148],"sample":[150],"conditions.":[151],"Experiments":[152],"on":[153],"two-":[154],"known":[155],"HSIs":[156],"demonstrating":[157],"that,":[158],"considered":[161],"CNN,":[163],"more":[167],"effective":[168],"than":[169],"explore":[173],"information":[175],"HSIs,":[179],"especially":[180],"case":[183],"with":[184],"number":[186]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
