Sea Surface Temperature Image Completion Method Based on Multiscale Fourier Fusion Neural Operator

Abstract

Sea surface temperature (SST) is a crucial metric in marine science, playing a pivotal role in forecasting and analyzing changes in the marine environment. However, remote sensing technologies often encounter issues where SST images are obscured by clouds, leading to data loss, thereby impacting marine environment prediction efficacy. Although many deep learning methods currently exist for reconstructing SST images, most focus on handling this task within the image domain, making it challenging to adapt to the chaotic nature of ocean systems. In addition, most methods only model at a single scale, which limits their ability to effectively capture the complex multiscale features in SST data. Therefore, this study proposes MSF_FNO, an image completion method based on multiscale Fourier fusion neural operator. MSF_FNO integrates multiscale feature fusion and frequency-domain neural operator technology to effectively overcome the limitations of single-scale feature processing and image-domain reconstruction in existing methods. This approach not only captures SST frequency-domain information and extracts structured features of SST images but also extracts critical features across multiple scales, ensuring global consistency and detailed features in reconstruction results. Experiments on the National Satellite Ocean Application Service (NSOAS) datasets demonstrate that MSF_FNO outperforms state-of-the-art (SOTA) methods in terms of reconstruction quality and robustness.