{"id":"https://openalex.org/W2154789478","doi":"https://doi.org/10.1109/lgrs.2014.2376034","title":"A New Pan-Sharpening Method With Deep Neural Networks","display_name":"A New Pan-Sharpening Method With Deep Neural Networks","publication_year":2015,"publication_date":"2015-01-22","ids":{"openalex":"https://openalex.org/W2154789478","doi":"https://doi.org/10.1109/lgrs.2014.2376034","mag":"2154789478"},"language":"en","primary_location":{"id":"doi:10.1109/lgrs.2014.2376034","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2014.2376034","pdf_url":null,"source":{"id":"https://openalex.org/S126920919","display_name":"IEEE Geoscience and Remote Sensing Letters","issn_l":"1545-598X","issn":["1545-598X","1558-0571"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Geoscience and Remote Sensing Letters","raw_type":"journal-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/A5006226649","display_name":"Wei Huang","orcid":"https://orcid.org/0000-0002-0095-1354"},"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":"Wei Huang","raw_affiliation_strings":["School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China","institution_ids":["https://openalex.org/I36399199"]}]},{"author_position":"middle","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, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China","institution_ids":["https://openalex.org/I36399199"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070343691","display_name":"Zhihui Wei","orcid":"https://orcid.org/0000-0002-4841-6051"},"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":"Zhihui Wei","raw_affiliation_strings":["School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China","institution_ids":["https://openalex.org/I36399199"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100622133","display_name":"Hongyi Liu","orcid":"https://orcid.org/0000-0001-8941-4346"},"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":"Hongyi Liu","raw_affiliation_strings":["School of Science, Nanjing University of Science and Technology, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"School of Science, Nanjing University of Science and Technology, Nanjing, China","institution_ids":["https://openalex.org/I36399199"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5062542061","display_name":"Songze Tang","orcid":null},"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":"Songze Tang","raw_affiliation_strings":["School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China","institution_ids":["https://openalex.org/I36399199"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5006226649"],"corresponding_institution_ids":["https://openalex.org/I36399199"],"apc_list":null,"apc_paid":null,"fwci":26.5022,"has_fulltext":false,"cited_by_count":333,"citation_normalized_percentile":{"value":0.99675698,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":"12","issue":"5","first_page":"1037","last_page":"1041"},"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.9980999827384949,"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"}},{"id":"https://openalex.org/T11019","display_name":"Image Enhancement Techniques","score":0.9941999912261963,"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":0.8182272911071777},{"id":"https://openalex.org/keywords/sharpening","display_name":"Sharpening","score":0.8154956698417664},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7147367596626282},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5952299237251282},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5899043679237366},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5540539026260376},{"id":"https://openalex.org/keywords/image-resolution","display_name":"Image resolution","score":0.4996509552001953},{"id":"https://openalex.org/keywords/panchromatic-film","display_name":"Panchromatic film","score":0.48015159368515015},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.44865697622299194},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.44196370244026184},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.4402086138725281},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.44013553857803345},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.43105554580688477},{"id":"https://openalex.org/keywords/image-fusion","display_name":"Image fusion","score":0.41743290424346924}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.8182272911071777},{"id":"https://openalex.org/C2781137444","wikidata":"https://www.wikidata.org/wiki/Q237105","display_name":"Sharpening","level":2,"score":0.8154956698417664},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7147367596626282},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5952299237251282},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5899043679237366},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5540539026260376},{"id":"https://openalex.org/C205372480","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"Image resolution","level":2,"score":0.4996509552001953},{"id":"https://openalex.org/C107445234","wikidata":"https://www.wikidata.org/wiki/Q280995","display_name":"Panchromatic film","level":3,"score":0.48015159368515015},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.44865697622299194},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.44196370244026184},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.4402086138725281},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.44013553857803345},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.43105554580688477},{"id":"https://openalex.org/C69744172","wikidata":"https://www.wikidata.org/wiki/Q860822","display_name":"Image fusion","level":3,"score":0.41743290424346924}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lgrs.2014.2376034","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2014.2376034","pdf_url":null,"source":{"id":"https://openalex.org/S126920919","display_name":"IEEE Geoscience and Remote Sensing Letters","issn_l":"1545-598X","issn":["1545-598X","1558-0571"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Geoscience and Remote Sensing Letters","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G103590238","display_name":null,"funder_award_id":"61301215","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5393695307","display_name":null,"funder_award_id":"11431015","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5445517457","display_name":null,"funder_award_id":"61171165","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G653588136","display_name":null,"funder_award_id":"61101194","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W1654063000","https://openalex.org/W1973794531","https://openalex.org/W1986629820","https://openalex.org/W2014316959","https://openalex.org/W2015380729","https://openalex.org/W2020679958","https://openalex.org/W2088538848","https://openalex.org/W2111924917","https://openalex.org/W2129953395","https://openalex.org/W2144436897","https://openalex.org/W2145094598","https://openalex.org/W2146337213","https://openalex.org/W2149720806","https://openalex.org/W2152254169","https://openalex.org/W2171627515","https://openalex.org/W2171845746","https://openalex.org/W2217217682","https://openalex.org/W2997574889","https://openalex.org/W6681096077","https://openalex.org/W6681686951","https://openalex.org/W6685078114","https://openalex.org/W6688879542"],"related_works":["https://openalex.org/W4254327447","https://openalex.org/W2033186943","https://openalex.org/W2158371478","https://openalex.org/W2816335205","https://openalex.org/W2158027388","https://openalex.org/W2182807969","https://openalex.org/W2084382156","https://openalex.org/W2733652285","https://openalex.org/W2019692878","https://openalex.org/W2084915308"],"abstract_inverted_index":{"A":[0],"deep":[1],"neural":[2],"network":[3],"(DNN)-based":[4],"new":[5],"pansharpening":[6],"method":[7,183],"for":[8],"the":[9,26,36,58,73,82,111,119,121,136,145,154,162,168,181],"remote":[10],"sensing":[11],"image":[12,66,77,142,152,157,166],"fusion":[13],"problem":[14],"is":[15,54,124,144],"proposed":[16,55,182],"in":[17,114,188],"this":[18,45],"letter.":[19],"Research":[20],"on":[21],"representation":[22],"learning":[23],"suggests":[24],"that":[25,135,148,180],"DNN":[27,123],"can":[28,69,108],"effectively":[29,109],"model":[30],"complex":[31],"relationships":[32],"between":[33,60,138,149],"variables":[34],"via":[35],"composition":[37],"of":[38,41,99,190],"several":[39,175],"levels":[40],"nonlinearity.":[42],"Inspired":[43],"by":[44,72,127],"observation,":[46],"a":[47,97,103,128],"modified":[48],"sparse":[49],"denoising":[50],"autoencoder":[51],"(MSDA)":[52],"algorithm":[53,130],"to":[56,116],"train":[57,118],"relationship":[59,137],"high-resolution":[61],"(HR)":[62],"and":[63,193],"low-resolution":[64],"(LR)":[65],"patches,":[67,153],"which":[68,107],"be":[70,159],"represented":[71],"DNN.":[74,112,170],"The":[75],"HR/LR":[76,83,139,150],"patches":[78,143],"only":[79],"sample":[80],"from":[81,161],"panchromatic":[84],"(PAN)":[85],"images":[86],"at":[87],"hand,":[88],"respectively,":[89],"without":[90],"requiring":[91],"other":[92,185],"training":[93],"images.":[94],"By":[95],"connecting":[96],"series":[98],"MSDAs,":[100],"we":[101],"obtain":[102],"stacked":[104],"MSDA":[105],"(S-MSDA),":[106],"pretrain":[110],"Moreover,":[113],"order":[115],"better":[117],"DNN,":[120],"entire":[122],"again":[125],"trained":[126,169],"back-propagation":[129],"after":[131],"pretraining.":[132],"Finally,":[133],"assuming":[134],"multispectral":[140],"(MS)":[141],"same":[146],"as":[147],"PAN":[151],"HR":[155],"MS":[156,165],"will":[158],"reconstructed":[160],"observed":[163],"LR":[164],"using":[167],"Comparative":[171],"experimental":[172],"results":[173],"with":[174],"quality":[176],"assessment":[177],"indexes":[178],"show":[179],"outperforms":[184],"pan-sharpening":[186],"methods":[187],"terms":[189],"visual":[191],"perception":[192],"numerical":[194],"measures.":[195]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":20},{"year":2024,"cited_by_count":26},{"year":2023,"cited_by_count":20},{"year":2022,"cited_by_count":51},{"year":2021,"cited_by_count":59},{"year":2020,"cited_by_count":50},{"year":2019,"cited_by_count":42},{"year":2018,"cited_by_count":36},{"year":2017,"cited_by_count":22},{"year":2016,"cited_by_count":4},{"year":2015,"cited_by_count":1}],"updated_date":"2026-04-14T08:04:32.555800","created_date":"2025-10-10T00:00:00"}