{"id":"https://openalex.org/W2773040796","doi":"https://doi.org/10.1109/igarss.2017.8128158","title":"PAN-Sharpening via residual deep learning","display_name":"PAN-Sharpening via residual deep learning","publication_year":2017,"publication_date":"2017-07-01","ids":{"openalex":"https://openalex.org/W2773040796","doi":"https://doi.org/10.1109/igarss.2017.8128158","mag":"2773040796"},"language":"en","primary_location":{"id":"doi:10.1109/igarss.2017.8128158","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2017.8128158","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","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/A5100642158","display_name":"Li Nie","orcid":"https://orcid.org/0000-0001-9731-0694"},"institutions":[{"id":"https://openalex.org/I36399199","display_name":"Nanjing University of Science and Technology","ror":"https://ror.org/00xp9wg62","country_code":"CN","type":"education","lineage":["https://openalex.org/I36399199"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Nie Li","raw_affiliation_strings":["School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, P. R. China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, P. R. China","institution_ids":["https://openalex.org/I36399199"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014878552","display_name":"Nan Huang","orcid":"https://orcid.org/0000-0003-0871-158X"},"institutions":[{"id":"https://openalex.org/I36399199","display_name":"Nanjing University of Science and Technology","ror":"https://ror.org/00xp9wg62","country_code":"CN","type":"education","lineage":["https://openalex.org/I36399199"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Nan Huang","raw_affiliation_strings":["School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, P. R. China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, P. R. China","institution_ids":["https://openalex.org/I36399199"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5020302879","display_name":"Liang Xiao","orcid":"https://orcid.org/0000-0003-0178-9384"},"institutions":[{"id":"https://openalex.org/I36399199","display_name":"Nanjing University of Science and Technology","ror":"https://ror.org/00xp9wg62","country_code":"CN","type":"education","lineage":["https://openalex.org/I36399199"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liang Xiao","raw_affiliation_strings":["School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, P. R. China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, P. R. China","institution_ids":["https://openalex.org/I36399199"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5100642158"],"corresponding_institution_ids":["https://openalex.org/I36399199"],"apc_list":null,"apc_paid":null,"fwci":1.8834,"has_fulltext":false,"cited_by_count":16,"citation_normalized_percentile":{"value":0.88566447,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"5133","last_page":"5136"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11659","display_name":"Advanced Image Fusion Techniques","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/T11659","display_name":"Advanced Image Fusion Techniques","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/T10689","display_name":"Remote-Sensing Image Classification","score":0.9969000220298767,"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.9965999722480774,"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.7682384252548218},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.7397651672363281},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6820052266120911},{"id":"https://openalex.org/keywords/panchromatic-film","display_name":"Panchromatic film","score":0.6666762828826904},{"id":"https://openalex.org/keywords/sharpening","display_name":"Sharpening","score":0.6467596292495728},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6332133412361145},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.5739514827728271},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5398110747337341},{"id":"https://openalex.org/keywords/multispectral-image","display_name":"Multispectral image","score":0.5335507988929749},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5238571166992188},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.46308237314224243},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.46224522590637207},{"id":"https://openalex.org/keywords/optical-transfer-function","display_name":"Optical transfer function","score":0.4488461911678314},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4082159399986267},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2113335132598877},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.17181655764579773}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7682384252548218},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.7397651672363281},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6820052266120911},{"id":"https://openalex.org/C107445234","wikidata":"https://www.wikidata.org/wiki/Q280995","display_name":"Panchromatic film","level":3,"score":0.6666762828826904},{"id":"https://openalex.org/C2781137444","wikidata":"https://www.wikidata.org/wiki/Q237105","display_name":"Sharpening","level":2,"score":0.6467596292495728},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6332133412361145},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.5739514827728271},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5398110747337341},{"id":"https://openalex.org/C173163844","wikidata":"https://www.wikidata.org/wiki/Q1761440","display_name":"Multispectral image","level":2,"score":0.5335507988929749},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5238571166992188},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.46308237314224243},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.46224522590637207},{"id":"https://openalex.org/C175231954","wikidata":"https://www.wikidata.org/wiki/Q1942321","display_name":"Optical transfer function","level":2,"score":0.4488461911678314},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4082159399986267},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2113335132598877},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.17181655764579773},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss.2017.8128158","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2017.8128158","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W54257720","https://openalex.org/W1522046140","https://openalex.org/W2087943891","https://openalex.org/W2111924917","https://openalex.org/W2154789478","https://openalex.org/W2163677711","https://openalex.org/W2171627515","https://openalex.org/W2171845746","https://openalex.org/W2242218935","https://openalex.org/W2348168847","https://openalex.org/W2388387182","https://openalex.org/W6602211262","https://openalex.org/W6631230401","https://openalex.org/W6685078114","https://openalex.org/W6690498163","https://openalex.org/W6982379313"],"related_works":["https://openalex.org/W1930929277","https://openalex.org/W4254327447","https://openalex.org/W2158394102","https://openalex.org/W2361746014","https://openalex.org/W1502637513","https://openalex.org/W2033186943","https://openalex.org/W2124952510","https://openalex.org/W2375311607","https://openalex.org/W2816335205","https://openalex.org/W2726689079"],"abstract_inverted_index":{"One":[0],"significant":[1],"advantage":[2,132],"of":[3,108,133],"the":[4,37,58,69,96,106,112,120,126,131,134,138,150,157,173],"deep":[5],"convolutional":[6],"neural":[7],"networks":[8],"(DCNN)":[9],"is":[10,28,47],"their":[11],"representational":[12],"ability":[13],"for":[14],"local":[15],"complex":[16],"structures.":[17],"Inspired":[18],"by":[19],"this":[20],"observation,":[21],"a":[22,32,73,143,161],"DCNN":[23,46,70],"based":[24,49,86],"residual":[25,135],"learning":[26,110,136],"model":[27],"proposed":[29,139,158],"to":[30,71,130],"learn":[31],"nonlinear":[33,74],"mapping":[34,75,98],"function":[35,76,99],"between":[36,100],"high-resolution":[38],"(HR)":[39],"and":[40,168],"low-resolution":[41],"(LR)":[42],"image":[43,51,62,94,103,115,124],"patches.":[44],"The":[45],"trained":[48,127],"on":[50,87],"patches,":[52],"which":[53],"are":[54],"only":[55],"sampled":[56],"from":[57,105,119],"HR/LR":[59,78,91,101],"panchromatic":[60],"(PAN)":[61],"without":[63],"other":[64],"training":[65],"images.":[66],"We":[67],"train":[68],"obtain":[72],"with":[77,172],"PAN":[79,102],"patch":[80],"pairs":[81],"using":[82,125],"mini-batch":[83],"gradient":[84],"descent":[85],"back-propagation.":[88],"By":[89],"assuming":[90],"multispectral":[92],"(MS)":[93],"shares":[95],"same":[97],"patches":[104],"viewpoint":[107],"transfer":[109],"methodology,":[111],"HR":[113],"MS":[114,123],"can":[116,141],"be":[117],"reconstructed":[118],"observed":[121],"LR":[122],"DCNN.":[128],"Owing":[129],"mechanism,":[137],"method":[140,159],"achieve":[142],"good":[144],"geometrical":[145],"details":[146],"injection":[147],"while":[148],"preserves":[149],"spectral":[151],"features.":[152],"Experimental":[153],"results":[154],"show":[155],"that":[156],"provides":[160],"better":[162],"performance":[163],"in":[164],"both":[165],"visual":[166],"perception":[167],"numerical":[169],"measures":[170],"compared":[171],"conventional":[174],"methods.":[175]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":5},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}