Reconstructing Sea Surface Temperature Images: A Masked Autoencoder
  Approach for Cloud Masking and Reconstruction
Angelina Agabin J. Xavier Prochaska
This thesis presents a new algorithm to mitigate cloud masking in the
analysis of sea surface temperature (SST) data generated by remote sensing
technologies, e.g., Clouds interfere with the analysis of all remote sensing
data using wavelengths shorter than 12 microns, significantly limiting the
quantity of usable data and creating a biased geographical distribution
(towards equatorial and coastal regions). To address this issue, we propose an
unsupervised machine learning algorithm called Enki which uses a Vision
Transformer with Masked Autoencoding to reconstruct masked pixels. We train
four different models of Enki with varying mask ratios (t) of 10%, 35%, 50%,
and 75% on the generated Ocean General Circulation Model (OGCM) dataset
referred to as LLC4320. To evaluate performance, we reconstruct a validation
set of LLC4320 SST images with random ``clouds'' corrupting p=10%, 20%, 30%,
40%, 50% of the images with individual patches of 4x4 pixel^2. We consistently
find that at all levels of p there is one or multiple models that reconstruct
the images with a mean RMSE of less than 0.03K, i.e. lower than the estimated
sensor error of VIIRS data. Similarly, at the individual patch level, the
reconstructions have RMSE 8x smaller than the fluctuations in the patch. And,
as anticipated, reconstruction errors are larger for images with a higher
degree of complexity. Our analysis also reveals that patches along the image
border have systematically higher reconstruction error; we recommend ignoring
these in production. We conclude that Enki shows great promise to surpass
in-painting as a means of reconstructing cloud masking. Future research will
develop Enki to reconstruct real-world data.