{"id":"https://openalex.org/W2986474637","doi":"https://doi.org/10.1109/igarss.2019.8899771","title":"Improving Spectral Resolution of Multispectral Data Using Convolutional Neural Network","display_name":"Improving Spectral Resolution of Multispectral Data Using Convolutional Neural Network","publication_year":2019,"publication_date":"2019-07-01","ids":{"openalex":"https://openalex.org/W2986474637","doi":"https://doi.org/10.1109/igarss.2019.8899771","mag":"2986474637"},"language":"en","primary_location":{"id":"doi:10.1109/igarss.2019.8899771","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2019.8899771","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium","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/A5075209190","display_name":"M. Peng","orcid":"https://orcid.org/0000-0002-9187-8120"},"institutions":[{"id":"https://openalex.org/I4210128053","display_name":"Institute of Remote Sensing and Digital Earth","ror":"https://ror.org/02cjszf03","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210128053"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Mingyuan Peng","raw_affiliation_strings":["Chinese Academy, Institute of Remote Sensing and Digital Earth, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Chinese Academy, Institute of Remote Sensing and Digital Earth, Beijing, China","institution_ids":["https://openalex.org/I4210128053"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5115603648","display_name":"Lifu Zhang","orcid":"https://orcid.org/0000-0002-3533-9966"},"institutions":[{"id":"https://openalex.org/I4210128053","display_name":"Institute of Remote Sensing and Digital Earth","ror":"https://ror.org/02cjszf03","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210128053"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lifu Zhang","raw_affiliation_strings":["Chinese Academy, Institute of Remote Sensing and Digital Earth, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Chinese Academy, Institute of Remote Sensing and Digital Earth, Beijing, China","institution_ids":["https://openalex.org/I4210128053"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102795630","display_name":"Xuejian Sun","orcid":"https://orcid.org/0000-0002-5111-7466"},"institutions":[{"id":"https://openalex.org/I4210128053","display_name":"Institute of Remote Sensing and Digital Earth","ror":"https://ror.org/02cjszf03","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210128053"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xuejian Sun","raw_affiliation_strings":["Chinese Academy, Institute of Remote Sensing and Digital Earth, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Chinese Academy, Institute of Remote Sensing and Digital Earth, Beijing, China","institution_ids":["https://openalex.org/I4210128053"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5111539412","display_name":"Yi Cen","orcid":"https://orcid.org/0000-0002-1500-8350"},"institutions":[{"id":"https://openalex.org/I4210128053","display_name":"Institute of Remote Sensing and Digital Earth","ror":"https://ror.org/02cjszf03","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210128053"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yi Cen","raw_affiliation_strings":["Chinese Academy, Institute of Remote Sensing and Digital Earth, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Chinese Academy, Institute of Remote Sensing and Digital Earth, Beijing, China","institution_ids":["https://openalex.org/I4210128053"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5075209190"],"corresponding_institution_ids":["https://openalex.org/I4210128053"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.19305131,"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":"3145","last_page":"3148"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9998000264167786,"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":0.9998000264167786,"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/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9973999857902527,"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/T12157","display_name":"Geochemistry and Geologic Mapping","score":0.991599977016449,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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":0.9573072195053101},{"id":"https://openalex.org/keywords/multispectral-image","display_name":"Multispectral image","score":0.8170276284217834},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7216508388519287},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6979479789733887},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.5867610573768616},{"id":"https://openalex.org/keywords/full-spectral-imaging","display_name":"Full spectral imaging","score":0.5515817403793335},{"id":"https://openalex.org/keywords/sensor-fusion","display_name":"Sensor fusion","score":0.5362545847892761},{"id":"https://openalex.org/keywords/image-resolution","display_name":"Image resolution","score":0.5051558613777161},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5042082071304321},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.46913406252861023},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.455544650554657},{"id":"https://openalex.org/keywords/land-cover","display_name":"Land cover","score":0.4210495948791504},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.359115868806839},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.15381136536598206},{"id":"https://openalex.org/keywords/land-use","display_name":"Land use","score":0.06620842218399048}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.9573072195053101},{"id":"https://openalex.org/C173163844","wikidata":"https://www.wikidata.org/wiki/Q1761440","display_name":"Multispectral image","level":2,"score":0.8170276284217834},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7216508388519287},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6979479789733887},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.5867610573768616},{"id":"https://openalex.org/C78660771","wikidata":"https://www.wikidata.org/wiki/Q5508206","display_name":"Full spectral imaging","level":3,"score":0.5515817403793335},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.5362545847892761},{"id":"https://openalex.org/C205372480","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"Image resolution","level":2,"score":0.5051558613777161},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5042082071304321},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.46913406252861023},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.455544650554657},{"id":"https://openalex.org/C2780648208","wikidata":"https://www.wikidata.org/wiki/Q3001793","display_name":"Land cover","level":3,"score":0.4210495948791504},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.359115868806839},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.15381136536598206},{"id":"https://openalex.org/C4792198","wikidata":"https://www.wikidata.org/wiki/Q1165944","display_name":"Land use","level":2,"score":0.06620842218399048},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C147176958","wikidata":"https://www.wikidata.org/wiki/Q77590","display_name":"Civil engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss.2019.8899771","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2019.8899771","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.4300000071525574,"id":"https://metadata.un.org/sdg/15","display_name":"Life in Land"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":11,"referenced_works":["https://openalex.org/W229472839","https://openalex.org/W1836465849","https://openalex.org/W1921523184","https://openalex.org/W1993699518","https://openalex.org/W2037236246","https://openalex.org/W2082185517","https://openalex.org/W2506684654","https://openalex.org/W2949117887","https://openalex.org/W6638667902","https://openalex.org/W6640185926","https://openalex.org/W6659717275"],"related_works":["https://openalex.org/W2022304901","https://openalex.org/W1987483041","https://openalex.org/W2988577871","https://openalex.org/W2911259277","https://openalex.org/W4386427838","https://openalex.org/W2800956885","https://openalex.org/W2533019003","https://openalex.org/W2057283258","https://openalex.org/W1788560349","https://openalex.org/W2018850895"],"abstract_inverted_index":{"Hyperspectral":[0],"data,":[1,88],"despite":[2],"of":[3,28,32,40,49,121,132,142,163],"possessing":[4],"high":[5],"spectral":[6,60],"resolution,":[7],"suffers":[8],"from":[9,101,110],"narrow":[10],"swath,":[11],"which":[12,71],"hinders":[13],"its":[14],"wider":[15],"applications.":[16],"Till":[17],"now,":[18],"there":[19,78],"are":[20,36],"many":[21],"fusion":[22,34],"methods":[23,35],"to":[24,159],"improve":[25],"the":[26,33,41,47,50,75,91,95,97,102,106,111,116,119,122,125,129,140,161,167],"resolution":[27,61],"data.":[29,52,144],"However,":[30],"most":[31],"focused":[37],"on":[38,74],"enhancement":[39],"overlapping":[42],"area":[43,104],"and":[44,86,89],"cannot":[45],"extend":[46,139,160],"swath":[48,141,162],"hyperspectral":[51,84,130,143,164],"Thus,":[53],"in":[54,136,148],"this":[55,149],"paper,":[56],"a":[57,152],"multispectral":[58,87],"image":[59],"improving":[62],"method":[63,126,150],"using":[64,118],"convolutional":[65],"neural":[66],"network":[67,117],"(CNN)":[68],"is":[69,72,94,105,151],"proposed,":[70],"based":[73],"hypothesis":[76],"that":[77,109],"exists":[79],"learnable":[80],"nonlinear":[81],"mapping":[82,98],"between":[83,99],"data":[85,100,120,131,165],"when":[90],"land":[92],"cover":[93],"same,":[96],"overlapped":[103,123],"same":[107],"as":[108,166],"non-overlapped":[112],"area.":[113],"By":[114],"training":[115],"area,":[124,134],"can":[127],"predict":[128],"nonoverlapping":[133],"or":[135],"other":[137],"words,":[138],"The":[145],"architecture":[146],"used":[147],"relatively":[153],"simple":[154],"three-layered":[155],"structure":[156],"yet":[157],"powerful":[158],"experimental":[168],"results":[169],"shows.":[170]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
