Super-Resolution of VIIRS-Measured Ocean Color Products Using Deep Convolutional Neural Network

Liu, Xiaoming; Wang, Menghua

doi:10.1109/TGRS.2020.2992912

i

Super-Resolution of VIIRS-Measured Ocean Color Products Using Deep Convolutional Neural Network

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2021
By Liu, Xiaoming ; Wang, Menghua

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Journal Title:

IEEE Transactions on Geoscience and Remote Sensing
Personal Author:

Liu, Xiaoming ; Wang, Menghua
NOAA Program & Office:

NESDIS (National Environmental Satellite, Data, and Information Service) ; CIRA (Cooperative Institute for Research in the Atmosphere) ; STAR (Center for Satellite Applications and Research)
Description:

Since its launch in October 2011, the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (SNPP) satellite has provided high quality global ocean color products, which include normalized water-leaving radiance spectra nL w (λ) of six moderate (M) bands (M1-M6) at the wavelengths of 410, 443, 486, 551, 671, and 745 nm with a spatial resolution of 750-m, and one imagery (I) band at a wavelength of 638 nm with a spatial resolution of 375-m. Because the high-resolution I-band measurements are highly correlated spectrally to those of M-band data, it can be used as a guidance to super-resolve the M-band nL w (λ) imagery from 750-to 375-m spatial resolution. Super-resolving images from coarse spatial resolution to finer ones have been a field of very active research in recent years. However, no previous studies have been applied to satellite ocean color remote sensing, in particular, for VIIRS ocean color applications. In this study, we employ the deep convolutional neural network (CNN) technique to glean the high-frequency content from the VIIRS I1 band and transfer to super-resolved M-band ocean color images. The network is trained to super-resolve each of the VIIRS six M-bands nL w (λ) separately. In our results, the super-resolved (375-m) nL w (λ) images are much sharper and show finer spatial structures than the original images. Quantitative evaluations show that biases between the super-resolved and original nL w (λ) images are small for all bands. However, errors in the super-resolved nL w (λ) images are wavelength-dependent. The smallest error is found in the superresolved nL w (551) and nL w (671) images, and error increases as the wavelength decreases from 486 to 410 nm. The results show that the networks have the capability to capture the correlations of the M-band and the I1 band images to super-resolved M-band images.
Source:

IEEE Transactions on Geoscience and Remote Sensing, 59(1), 114-127
DOI:

https://doi.org/10.1109/TGRS.2020.2992912
ISSN:

0196-2892 ; 1558-0644
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Institute of Electrical and Electronics Engineers (IEEE)
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Journal Article
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Accepted Manuscript
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