Occlusion Sensitivity Analysis of Neural Network Architectures for Eddy Detection

Ocean eddies, known as the weather of the ocean, represent gyrating water masses that have horizontal scales from 10 km up to at times 500 km. They transport water mass, heat, nutrition, and carbon and have been identified as hot spots of biological activity. In radar altimetry, they affect alongtrack measurements of sea level height and lead to problems in the subsequent generation of sea level maps. Monitoring eddies is therefore of interest among others to marine biologists, oceanographers, and geodesists. In this paper, using occlusion sensitivity maps (OSMs) we investigate different neural network architectures that address the task of automatic detection of ocean eddies, which is challenging due to their spatio-temporal dynamic behavior. Thus we analyze the importance of the spatial context that is needed to infer correct semantics and compare them between the different architectures. For this, we use data from satellite altimetry since it offers sea surface heights precise enough to expose the presence of eddies. For detection, we utilize a transformer neural network called Teddy which can exploit temporal and spatial information in the data. For evaluating our approach, we use gridded data sets for the area of the western part of the southern Atlantic from 2000 to 2011. Our results are evaluated primarily by employing the dice score metric and show that transformers can infer the semantics with similar performance compared to state-of-the-art CNNs but at the same time are less sensitive towards structural changes due to different modeling of the spatial information of the data.