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Oceanic mesoscale eddy detection and convolutional neural network complexity
International Journal of Applied Earth Observation and Geoinformation ( IF 7.5 ) Pub Date : 2022-08-22 , DOI: 10.1016/j.jag.2022.102973
Oliverio J. Santana, Daniel Hernández-Sosa, Ryan N. Smith

Deep learning has drawn the attention of oceanographic researchers over the past few years, making the research community adopt computer vision techniques for oceanic mesoscale eddy detection on satellite altimetry gridded products. In this paper, we describe a convolutional neural network designed to detect eddies in satellite altimetry maps after being trained using segmentation masks provided by the OpenEddy detection algorithm. Against the current trend, in which increasingly complex neural networks are being proposed to address this problem, our design is relatively simple and yet provides competitive performance when compared to any of the previous deep learning methods reported in the literature. Furthermore, we show that our model is less sensitive to timely variations than the traditional models based on physical and geometric features defined by human experts, making it possible for our model to use the general data context to identify eddies that those traditional models would have missed. These results prove that overly complex neural network architectural designs are not required to solve the eddy detection problem on altimetry maps and generate a sufficiently good model for most practical applications in the field of marine sciences.



中文翻译:

海洋中尺度涡流检测和卷积神经网络复杂度

深度学习在过去几年引起了海洋学研究人员的关注,使得研究界采用计算机视觉技术对卫星测高网格产品进行海洋中尺度涡流检测。在本文中,我们描述了一个卷积神经网络,该网络设计用于在使用 OpenEddy 检测算法提供的分割掩码进行训练后检测卫星测高图中的涡流。针对当前的趋势,其中提出了越来越复杂的神经网络来解决这个问题,我们的设计相对简单,但与文献中报道的任何先前的深度学习方法相比,提供了具有竞争力的性能。此外,我们表明,与基于人类专家定义的物理和几何特征的传统模型相比,我们的模型对及时变化的敏感性较低,这使得我们的模型可以使用一般数据上下文来识别那些传统模型会遗漏的涡流。这些结果证明,不需要过于复杂的神经网络架构设计来解决测高图上的涡流检测问题,并为海洋科学领域的大多数实际应用生成足够好的模型。

更新日期:2022-08-22
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