发表机构
Collecte Localisation Satellites (CLS); Centre National d’Études Spatiales (CNES)（卫星定位收集中心; 法国国家空间研究中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对SWOT海面高度观测中的仪器噪声，本研究改进基于U-Net的CNN去噪算法，优化训练流程以减少偏差和伪影，并提出标准化评估基准以验证算法稳健性。
AI 中文摘要
SWOT（地表水与海洋地形）任务目前正在提供前所未有的高分辨率海面高度（SSH）观测数据，揭示了更精细尺度的海洋特征。然而，SWOT的KaRIn高度计二维观测受到仪器误差的影响。这种噪声退化正在改变SWOT信号的高频部分，以及一些海洋学家感兴趣的小尺度至次中尺度动力学。为此，Tréboutte等人（2023）开发了一种基于U-Net架构的卷积神经网络（CNN），用于将SSH中包含的噪声与物理信号分离。他们的方法在模拟SWOT测量数据上展示了巨大潜力，Dibarboure等人（2024）报告了该方法对SWOT实际飞行数据的积极影响。然而，在异常条件下（如非常高的表面波、内潮孤立子），已观察到去噪性能下降以及偶尔的负面副作用。在本研究中，我们展示了其中一些局限性，并提出了一种改进的基于CNN的去噪方法：我们修改了训练流程以获得更稳健的算法版本，以避免去噪SSH中的偏差和伪影。本文还提出了一个更完整的验证流程，包括一个稳健且标准化的评估基准：这些指标可能有助于评估其他SWOT滤波和去噪算法。
英文摘要
The SWOT (Surface Water Ocean Topography) mission is currently providing unpreceded high-resolution measurements of Sea Surface Height (SSH), revealing ocean features at finer scales. Nevertheless, the two-dimensional observations of KaRIn altimeter of SWOT suffer from instrumental errors. This noise degradation is altering the high frequencies of SWOT signal, and the small to sub-mesoscale dynamics of interest for some oceanographers. For this reason, Tréboutte et al. (2023) have developed a convolutional neural network (CNN) based on U-Net architecture to separate the noise from the physical signals contained in the SSH. Their approach has demonstrated great potential on simulated SWOT measurements, and Dibarboure et al. (2024) report a positive influence on actual flight data from SWOT. However, degraded denoising performance, and occasional negative side-effects have been observed in atypical conditions (e.g. very high surface waves, internal tides solitons). In this study, we illustrate some of these limitations and we present an improved approach of the CNN-based denoising: we modified the training procedure to obtain a more robust version of the algorithm, to avoid biases and artifacts in the denoised SSH. This paper also presents a more complete validation process with a robust and standardized evaluation benchmark: these metrics could be of interest to assess other SWOT filtering and denoising algorithms.
CommentsThis Work has been submitted to JTECH (Journal of Atmospheric and Oceanic Technology). Copyright in this Work may be transferred without further notice
