Pre-training and Fine-tuning Attention Based Encoder Decoder Improves Sea Surface Height Multi-variate Inpainting
Résumé
The ocean is observed through satellites measuring physical data of various natures. Among them, Sea Surface Height (SSH) and Sea Surface Temperature (SST) are physically linked data involving different remote sensing technologies and therefore different image inverse problems. In this work, we propose to use an Attentionbased Encoder-Decoder to perform the inpainting of the SSH, using the SST as contextual information. We propose to pre-train this neural network on a realistic twin experiment of the observing system and to fine-tune it in an unsupervised manner on real-world observations. We show the interest of this strategy by comparing it to existing methods. Our training methodology achieves state-of-the-art performances, and we report a decrease of 25% in error compared to the most widely used interpolations product.
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