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Super-Resolution of Sea Surface Temperature Satellite Images

Lambhate, D and Subramani, DN (2020) Super-Resolution of Sea Surface Temperature Satellite Images. In: 2020 Global Oceans: Singapore - U.S. Gulf Coast, OCEANS 2020, 5-30 Oct 2020, Singapore.

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Official URL: https://doi.org/10.1109/IEEECONF38699.2020.9389030


Availability of high-resolution maps of geophysical fields, devoid of data loss due to clouds, is an urgent requirement for operational forecasting. We develop a Bayesian algorithm for super-resolution (or downscaling) of lower resolution geophysical fields observed by satellites. The key novelty in the present algorithm is the development and use of a Generative Adversarial Network (GAN) to learn the prior probability distribution of the high-resolution geophysical fields from historical data and/or model forecasts. The trained GAN is used to sample from the high-resolution prior and a particle filter along with the low-resolution data (as observation) is used to obtain the posterior high-resolution geophysical field. The resultant algorithm has been named the Particle Filter Generative Adversarial Network super-resolution (PF-GAN-SR) algorithm. The new algorithm is applied to downscale sea surface temperature fields in the northwest Atlantic Ocean. Results show consistent performance across different downscaling ratios. Notably, the high-resolution fields obtained from the new algorithm has better similarity score with the true high-resolution field compared to those from bi-cubic interpolation (commonly used in the geophysical community) and the SR-GAN algorithm (used in the computer vision community). © 2020 IEEE.

Item Type: Conference Paper
Publication: 2020 Global Oceans 2020: Singapore - U.S. Gulf Coast
Publisher: Institute of Electrical and Electronics Engineers Inc.
Additional Information: The copyright for this article belongs to Institute of Electrical and Electronics Engineers Inc.
Keywords: Atmospheric temperature; Monte Carlo methods; Optical resolving power; Probability distributions; Submarine geophysics; Surface properties; Surface waters, Adversarial networks; Bayesian algorithms; Bicubic interpolation; Consistent performance; High resolution maps; Operational forecasting; Sea surface temperature (SST); Vision communities, Oceanography
Department/Centre: Division of Interdisciplinary Sciences > Computational and Data Sciences
Date Deposited: 06 Aug 2021 10:06
Last Modified: 06 Aug 2021 10:06
URI: http://eprints.iisc.ac.in/id/eprint/69151

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