{"id":"https://openalex.org/W3130666860","doi":"https://doi.org/10.1109/igarss39084.2020.9323505","title":"Pan-Sharpening with a CNN-Based Two Stage Ratio Enhancement Method","display_name":"Pan-Sharpening with a CNN-Based Two Stage Ratio Enhancement Method","publication_year":2020,"publication_date":"2020-09-26","ids":{"openalex":"https://openalex.org/W3130666860","doi":"https://doi.org/10.1109/igarss39084.2020.9323505","mag":"3130666860"},"language":"en","primary_location":{"id":"doi:10.1109/igarss39084.2020.9323505","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss39084.2020.9323505","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2020 - 2020 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/A5023441963","display_name":"Huanyu Zhou","orcid":"https://orcid.org/0000-0001-7584-3828"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Huanyu Zhou","raw_affiliation_strings":["The State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"The State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056847110","display_name":"Qingjie Liu","orcid":"https://orcid.org/0000-0002-5181-6451"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qingjie Liu","raw_affiliation_strings":["Hangzhou Innovation Institute, Beihang University, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"Hangzhou Innovation Institute, Beihang University, Hangzhou, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103190126","display_name":"Qizhi Xu","orcid":"https://orcid.org/0000-0002-0136-4418"},"institutions":[{"id":"https://openalex.org/I75390827","display_name":"Beijing University of Chemical Technology","ror":"https://ror.org/00df5yc52","country_code":"CN","type":"education","lineage":["https://openalex.org/I75390827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qizhi Xu","raw_affiliation_strings":["Beijing University of Chemical Technology, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing University of Chemical Technology, Beijing, China","institution_ids":["https://openalex.org/I75390827"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100398953","display_name":"Yunhong Wang","orcid":"https://orcid.org/0000-0001-8001-2703"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yunhong Wang","raw_affiliation_strings":["The State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"The State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5023441963"],"corresponding_institution_ids":["https://openalex.org/I82880672"],"apc_list":null,"apc_paid":null,"fwci":0.3773,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.69879752,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"12","issue":null,"first_page":"216","last_page":"219"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11659","display_name":"Advanced Image Fusion Techniques","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/T11659","display_name":"Advanced Image Fusion Techniques","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/T11019","display_name":"Image Enhancement Techniques","score":0.9995999932289124,"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/T11105","display_name":"Advanced Image Processing Techniques","score":0.9995999932289124,"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/computer-science","display_name":"Computer science","score":0.8286294937133789},{"id":"https://openalex.org/keywords/sharpening","display_name":"Sharpening","score":0.8028140068054199},{"id":"https://openalex.org/keywords/panchromatic-film","display_name":"Panchromatic film","score":0.7411523461341858},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6931353211402893},{"id":"https://openalex.org/keywords/initialization","display_name":"Initialization","score":0.6116513013839722},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5129453539848328},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4738077223300934},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.41804713010787964},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4016195237636566},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.36159083247184753}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8286294937133789},{"id":"https://openalex.org/C2781137444","wikidata":"https://www.wikidata.org/wiki/Q237105","display_name":"Sharpening","level":2,"score":0.8028140068054199},{"id":"https://openalex.org/C107445234","wikidata":"https://www.wikidata.org/wiki/Q280995","display_name":"Panchromatic film","level":3,"score":0.7411523461341858},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6931353211402893},{"id":"https://openalex.org/C114466953","wikidata":"https://www.wikidata.org/wiki/Q6034165","display_name":"Initialization","level":2,"score":0.6116513013839722},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5129453539848328},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4738077223300934},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.41804713010787964},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4016195237636566},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.36159083247184753},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss39084.2020.9323505","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss39084.2020.9323505","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1263841929","display_name":null,"funder_award_id":"41871283","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":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W54434497","https://openalex.org/W817971873","https://openalex.org/W1522046140","https://openalex.org/W1654063000","https://openalex.org/W1885185971","https://openalex.org/W1984127314","https://openalex.org/W1991460509","https://openalex.org/W2034212924","https://openalex.org/W2099471712","https://openalex.org/W2139529730","https://openalex.org/W2159269332","https://openalex.org/W2171845746","https://openalex.org/W2172185514","https://openalex.org/W2311103336","https://openalex.org/W2462592242","https://openalex.org/W2777033955","https://openalex.org/W2963695810","https://openalex.org/W2964275574","https://openalex.org/W4285719527","https://openalex.org/W4320013936","https://openalex.org/W6631230401","https://openalex.org/W6659073135","https://openalex.org/W6685078114","https://openalex.org/W6698444068","https://openalex.org/W6746722832","https://openalex.org/W6751746117"],"related_works":["https://openalex.org/W2033186943","https://openalex.org/W4254327447","https://openalex.org/W2816335205","https://openalex.org/W2158371478","https://openalex.org/W2158027388","https://openalex.org/W2182807969","https://openalex.org/W2084382156","https://openalex.org/W2019692878","https://openalex.org/W2084915308","https://openalex.org/W4313315820"],"abstract_inverted_index":{"We":[0,100],"propose":[1],"a":[2,30,71],"hybrid":[3],"method":[4,15,37,49,81],"combining":[5],"the":[6,11,24,35,40,45,55,62,68,79,85,97,114,117],"deep":[7,25],"learning":[8,26],"technique":[9,27],"and":[10,82,106],"ratio":[12],"enhancement":[13],"(RE)":[14],"for":[16,34,96],"pansharpening.":[17],"The":[18,48],"intuition":[19],"behind":[20],"is":[21,58,76],"to":[22,28,38,60,66,90,112],"utilize":[23],"synthesize":[29],"panchromatic":[31],"(PAN)":[32],"image":[33,65],"RE":[36,80,98],"reduce":[39],"spectral":[41],"distortion":[42],"while":[43],"keeping":[44],"spatial":[46],"details.":[47],"consists":[50],"of":[51,116],"two":[52],"stages.":[53],"First,":[54],"CNN":[56,75],"synthesizer":[57],"optimized":[59],"generate":[61],"downsampled":[63],"PAN":[64,95],"guarantee":[67],"network":[69],"have":[70],"good":[72],"initialization.":[73],"Second,":[74],"integrated":[77],"into":[78],"supervised":[83],"by":[84],"ground":[86],"truth":[87],"multi-spectral":[88],"(MS)":[89],"produce":[91],"an":[92],"ideal":[93],"synthesized":[94],"method.":[99,119],"conduct":[101],"experiments":[102],"on":[103],"various":[104],"datasets":[105],"compare":[107],"with":[108],"widely":[109],"used":[110],"methods":[111],"demonstrate":[113],"superiority":[115],"proposed":[118]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
