用神经网络从有缺失数据的卫星海洋色度图中重建完整图像
Observation-only learning of neural mapping schemes for gappy satellite-derived ocean colour parameters
- 基于4DVarNet框架，直接在缺失数据上训练神经网络
- 在地中海数据集上重建效果优于传统方法，峰值信噪比超12%
- 适合缺乏完整观测数据的海洋生态监测研究者使用
监测沿海和开阔海域的光学特性对评估海洋生态系统健康至关重要。深度学习为应对这些生态动态提供了有前景的方法，尤其在缺乏无缺失真实数据的情况下，这给有效训练框架的设计带来了挑战。我们采用先进的神经变分数据同化方案（4DVarNet），提出一个全面的训练框架，可直接在有缺失数据的数据集上进行训练。以地中海为例，实验不仅展示了所选神经网络从有缺失数据集中重建无缺失图像的高性能，还证明其在使用CNN或UNet架构时，均优于当前最优算法DInEOF和直接反演方法。
原文摘要 · Abstract (English)
Monitoring optical properties of coastal and open ocean waters is crucial to assessing the health of marine ecosystems. Deep learning offers a promising approach to address these ecosystem dynamics, especially in scenarios where gap-free ground-truth data is lacking, which poses a challenge for designing effective training frameworks. Using an advanced neural variational data assimilation scheme (called 4DVarNet), we introduce a comprehensive training framework designed to effectively train directly on gappy data sets. Using the Mediterranean Sea as a case study, our experiments not only highlight the high performance of the chosen neural network in reconstructing gap-free images from gappy datasets but also demonstrate its superior performance over state-of-the-art algorithms such as DInEOF and Direct Inversion, whether using CNN or UNet architectures.
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