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Article: Sentinel-2A image fusion using a machine learning approach

TitleSentinel-2A image fusion using a machine learning approach
Authors
KeywordsImage fusion
Sentinel-2A
sharpening
support vector regression (SVR)
Issue Date2019
Citation
IEEE Transactions on Geoscience and Remote Sensing, 2019, v. 57, n. 12, p. 9589-9601 How to Cite?
AbstractThe multispectral instrument (MSI) carried by Sentinel-2A has 13 spectral bands with various spatial resolutions (i.e., four 10-m, six 20-m, and three 60-m bands). A wide range of applications requires a 10-m resolution for all spectral bands, including the 20- and 60-m bands. To achieve this requirement, previous studies used conventional pansharpening techniques, which require a simulated 10-m panchromatic (PAN) band from four 10-m bands [blue, green, red, and near infrared (NIR)]. The simulated PAN band may not have all the information from the original four bands and may have no spectral response function that overlaps the 20- or 60-m bands to be sharpened, which may degrade fusion quality. This paper presents a machine learning method that can directly use the information from multiple 10-m resolution bands for fusion. The method first learns the spectral relationship between the 20- or 60-m band to be sharpened and the selected 10-m bands degraded to 20 or 60 m using the support vector regression (SVR) model. The model is then applied to the selected 10-m bands to predict the 10-m-resolution version of the 20- or 60-m band. The image degradation process was tuned to closely match the Sentinel-2A MSI modulation transfer function (MTF). We applied our method to three data sets in Guangzhou, China, New South Wales, Australia, and St. Louis, USA, and achieved better fusion results than other commonly used pansharpening methods in terms of both visual and quantitative factors.
Persistent Identifierhttp://hdl.handle.net/10722/329590
ISSN
2023 Impact Factor: 7.5
2023 SCImago Journal Rankings: 2.403
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorWang, Jing-
dc.contributor.authorHuang, Bo-
dc.contributor.authorZhang, Hankui K.-
dc.contributor.authorMa, Peifeng-
dc.date.accessioned2023-08-09T03:33:53Z-
dc.date.available2023-08-09T03:33:53Z-
dc.date.issued2019-
dc.identifier.citationIEEE Transactions on Geoscience and Remote Sensing, 2019, v. 57, n. 12, p. 9589-9601-
dc.identifier.issn0196-2892-
dc.identifier.urihttp://hdl.handle.net/10722/329590-
dc.description.abstractThe multispectral instrument (MSI) carried by Sentinel-2A has 13 spectral bands with various spatial resolutions (i.e., four 10-m, six 20-m, and three 60-m bands). A wide range of applications requires a 10-m resolution for all spectral bands, including the 20- and 60-m bands. To achieve this requirement, previous studies used conventional pansharpening techniques, which require a simulated 10-m panchromatic (PAN) band from four 10-m bands [blue, green, red, and near infrared (NIR)]. The simulated PAN band may not have all the information from the original four bands and may have no spectral response function that overlaps the 20- or 60-m bands to be sharpened, which may degrade fusion quality. This paper presents a machine learning method that can directly use the information from multiple 10-m resolution bands for fusion. The method first learns the spectral relationship between the 20- or 60-m band to be sharpened and the selected 10-m bands degraded to 20 or 60 m using the support vector regression (SVR) model. The model is then applied to the selected 10-m bands to predict the 10-m-resolution version of the 20- or 60-m band. The image degradation process was tuned to closely match the Sentinel-2A MSI modulation transfer function (MTF). We applied our method to three data sets in Guangzhou, China, New South Wales, Australia, and St. Louis, USA, and achieved better fusion results than other commonly used pansharpening methods in terms of both visual and quantitative factors.-
dc.languageeng-
dc.relation.ispartofIEEE Transactions on Geoscience and Remote Sensing-
dc.subjectImage fusion-
dc.subjectSentinel-2A-
dc.subjectsharpening-
dc.subjectsupport vector regression (SVR)-
dc.titleSentinel-2A image fusion using a machine learning approach-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/TGRS.2019.2927766-
dc.identifier.scopuseid_2-s2.0-85075762726-
dc.identifier.volume57-
dc.identifier.issue12-
dc.identifier.spage9589-
dc.identifier.epage9601-
dc.identifier.eissn1558-0644-
dc.identifier.isiWOS:000505701800007-

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