Optically Enhanced Super-Resolution of Sea Surface Temperature Using Deep Learning
David T. Lloyd, Aaron Abela, Reuben A. Farrugia, Anthony Galea, Gianluca Valentino
Abstract
Sea surface temperature (SST) can be measured from space using infrared sensors on Earth-observing satellites. However, the tradeoff between spatial resolution and swath size (and hence revisit time) means that SST products derived from remote sensing measurements commonly only have a moderate resolution (>1 km). In this article, we adapt the design of a super-resolution neural network architecture [specifically very deep super-resolution (VDSR)] to enhance the resolution of both top-of-atmosphere thermal images of sea regions and bottom-of-atmosphere SST images by a factor of 5. When tested on an unseen dataset, the trained neural network yields thermal images that have an RMSE <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2-3\times $ </tex-math></inline-formula> smaller than interpolation, with a 6–9 dB improvement in PSNR. A major contribution of the proposed neural network architecture is that it fuses optical and thermal images to propagate the high-resolution information present in the optical image to the restored thermal image. To illustrate the potential benefits of using super-resolution (SR) in the context of oceanography, we present super-resolved SST images of a gyre and an ocean front, revealing details and features otherwise poorly resolved by moderate resolution satellite images.