{"id":"https://openalex.org/W3172287679","doi":"https://doi.org/10.1145/3447587.3447610","title":"Discussion on Information Overlap in Hyperspectral Image Classification Based on Machine Learning","display_name":"Discussion on Information Overlap in Hyperspectral Image Classification Based on Machine Learning","publication_year":2021,"publication_date":"2021-01-01","ids":{"openalex":"https://openalex.org/W3172287679","doi":"https://doi.org/10.1145/3447587.3447610","mag":"3172287679"},"language":"en","primary_location":{"id":"doi:10.1145/3447587.3447610","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3447587.3447610","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 The 4th International Conference on Image and Graphics Processing","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/A5060036647","display_name":"Qingjie Yang","orcid":"https://orcid.org/0000-0001-5817-7106"},"institutions":[{"id":"https://openalex.org/I126520041","display_name":"University of Science and Technology of China","ror":"https://ror.org/04c4dkn09","country_code":"CN","type":"education","lineage":["https://openalex.org/I126520041","https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Qingjie Yang","raw_affiliation_strings":["University of Science and Technology of China, China"],"affiliations":[{"raw_affiliation_string":"University of Science and Technology of China, China","institution_ids":["https://openalex.org/I126520041"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5109274667","display_name":"Rong Zhang","orcid":"https://orcid.org/0009-0005-9350-5888"},"institutions":[{"id":"https://openalex.org/I126520041","display_name":"University of Science and Technology of China","ror":"https://ror.org/04c4dkn09","country_code":"CN","type":"education","lineage":["https://openalex.org/I126520041","https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Rong Zhang","raw_affiliation_strings":["University of Science and Technology of China, China"],"affiliations":[{"raw_affiliation_string":"University of Science and Technology of China, China","institution_ids":["https://openalex.org/I126520041"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5067418255","display_name":"Yi Fang","orcid":"https://orcid.org/0000-0002-1538-249X"},"institutions":[{"id":"https://openalex.org/I126520041","display_name":"University of Science and Technology of China","ror":"https://ror.org/04c4dkn09","country_code":"CN","type":"education","lineage":["https://openalex.org/I126520041","https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yi Fang","raw_affiliation_strings":["University of Science and Technology of China, China"],"affiliations":[{"raw_affiliation_string":"University of Science and Technology of China, China","institution_ids":["https://openalex.org/I126520041"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5060036647"],"corresponding_institution_ids":["https://openalex.org/I126520041"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.09093656,"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":"156","last_page":"161"},"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":0.9976999759674072,"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/T11659","display_name":"Advanced Image Fusion Techniques","score":0.973800003528595,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.8649039268493652},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7514086365699768},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7476431131362915},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.720004677772522},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.663140594959259},{"id":"https://openalex.org/keywords/principal-component-analysis","display_name":"Principal component analysis","score":0.6302223205566406},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.5186476111412048},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.49166780710220337},{"id":"https://openalex.org/keywords/spatial-analysis","display_name":"Spatial analysis","score":0.46169647574424744},{"id":"https://openalex.org/keywords/test-set","display_name":"Test set","score":0.4565010964870453},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.4348280429840088},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4196895360946655},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.28189224004745483},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1801798939704895}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.8649039268493652},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7514086365699768},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7476431131362915},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.720004677772522},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.663140594959259},{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.6302223205566406},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.5186476111412048},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.49166780710220337},{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.46169647574424744},{"id":"https://openalex.org/C169903167","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Test set","level":2,"score":0.4565010964870453},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.4348280429840088},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4196895360946655},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.28189224004745483},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1801798939704895},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3447587.3447610","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3447587.3447610","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 The 4th International Conference on Image and Graphics Processing","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":23,"referenced_works":["https://openalex.org/W1521436688","https://openalex.org/W1903029394","https://openalex.org/W2029316659","https://openalex.org/W2090424610","https://openalex.org/W2097117768","https://openalex.org/W2097915756","https://openalex.org/W2102605133","https://openalex.org/W2103094532","https://openalex.org/W2114819256","https://openalex.org/W2122111042","https://openalex.org/W2136251662","https://openalex.org/W2144151128","https://openalex.org/W2341545927","https://openalex.org/W2507011716","https://openalex.org/W2548791488","https://openalex.org/W2572303978","https://openalex.org/W2732412926","https://openalex.org/W2767805377","https://openalex.org/W2782517596","https://openalex.org/W2894165434","https://openalex.org/W2898381489","https://openalex.org/W2963703618","https://openalex.org/W4240485910"],"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/W2070598848","https://openalex.org/W2044184146","https://openalex.org/W4313014865","https://openalex.org/W2019190440","https://openalex.org/W598569869"],"abstract_inverted_index":{"Principal":[0],"component":[1],"analysis(PCA)":[2],"and":[3,44,61,86,136,180,203],"spectral-spatial":[4],"methods":[5,18],"are":[6,91,152],"usually":[7],"used":[8],"in":[9,95,146,157,192,200],"hyperspectral":[10,27,208],"images(HSI)":[11],"classification":[12,22,119,124,144,184,194,209],"based":[13,160],"on":[14,72,118,161,189],"machine":[15,134,162],"learning.":[16],"These":[17],"have":[19,51],"achieved":[20],"high":[21,33],"accuracy":[23,34],"for":[24,93,205],"several":[25],"publicly":[26],"datasets.":[28],"However,":[29],"we":[30,50,121],"believe":[31],"that":[32,143],"is":[35,70],"mainly":[36],"due":[37],"to":[38,78,111,175],"information":[39,56,81,107,116,129,149,190],"overlap":[40,57,82,117,130,150,191],"between":[41,83],"the":[42,53,66,73,79,84,96,101,113,123,158,164,172,177,183,187],"training":[43,85,102,173],"test":[45,87,165],"set.":[46],"In":[47,109,155],"this":[48],"paper,":[49],"discussed":[52],"problem":[54],"of":[55,115,171,182],"caused":[58],"by":[59,100],"PCA":[60,67],"neighborhood":[62,97],"region":[63],"extraction.":[64],"First,":[65],"transformation":[68],"matrix":[69],"fitted":[71],"whole":[74],"image,":[75],"which":[76,104],"leads":[77],"spectral":[80],"samples.":[88],"Second,":[89],"there":[90],"pixels":[92],"testing":[94],"regions":[98],"extracted":[99],"pixels,":[103],"cause":[105],"spatial":[106],"overlap.":[108],"order":[110],"explore":[112],"influence":[114],"results,":[120],"compare":[122],"results":[125,145],"with":[126],"or":[127],"without":[128],"using":[131],"support":[132],"vector":[133],"(SVM)":[135],"convolutional":[137],"neural":[138],"network":[139],"(CNN).":[140],"Experiments":[141],"indicate":[142],"above":[147],"two":[148],"cases":[151],"less":[153],"reliable.":[154],"fact,":[156],"method":[159,195],"learning,":[163],"set":[166,174],"should":[167],"be":[168],"completely":[169],"independent":[170],"ensure":[176],"accuracy,":[178],"reliability":[179],"universality":[181],"results.":[185],"Therefore,":[186],"discussion":[188],"HSIs":[193],"creates":[196],"a":[197],"new":[198],"approach":[199],"future":[201],"research":[202],"design":[204],"more":[206],"reliable":[207],"methods.":[210]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}