Abstract
This paper proposes a new approach for meteorology; estimating sea surface temperatures (SSTs) by using deep learning. SSTs are essential information for ocean-related industries but are hard to measure. Although multi-spectral imaging sensors on meteorological satellites are used for measuring SSTs over a wide area, they cannot measure sea temperature in regions covered by clouds, so most of the temperature data will be partially occluded. In meteorology, data assimilation with physics-based simulation is used for interpolating occluded SSTs, and can generate physically-correct SSTs that match observations by satellites, but it requires huge computational cost. We propose a low-cost learning-based method using pre-computed data-assimilation SSTs. Our restoration model employs adversarial physical model loss that evaluates physical correctness of generated SST images, and restores SST images in real time. Experimental results with satellite images show that the proposed method can reconstruct physically-correct SST images without occlusions.
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Hirahara, N., Sonogashira, M., Kasahara, H., Iiyama, M. (2020). Denoising and Inpainting of Sea Surface Temperature Image with Adversarial Physical Model Loss. In: Palaiahnakote, S., Sanniti di Baja, G., Wang, L., Yan, W. (eds) Pattern Recognition. ACPR 2019. Lecture Notes in Computer Science(), vol 12046. Springer, Cham. https://doi.org/10.1007/978-3-030-41404-7_24
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