论文标题
基于多模式学习的反转模型，用于卫星衍生的地球物理领域的时空重建
Multimodal learning-based inversion models for the space-time reconstruction of satellite-derived geophysical fields
论文作者
论文摘要
对于众多地球观察应用，可以从各种卫星传感器中受益，以解决某些过程或感兴趣的信息的重建。各种卫星传感器提供的观察数据具有不同的采样模式，因此卫星轨道和/或它们对大气条件的敏感性（例如，Clour覆盖，大雨，...）。除了考虑不规则采样的观测能力之外，模型驱动的反转方法的定义通常仅限于特定的案例研究，在这些案例研究中，人们可以明确地得出物理模型以与不同的观察源相关。在这里，我们研究了端到端学习方案如何提供新的手段来解决多模式反演问题。所提出的方案将差异公式与可训练的观测操作员{\ em a先验}术语和求解器结合在一起。通过对空间海洋学的应用，我们展示了该方案如何从卫星衍生的海面温度图像中成功提取相关信息，并增强从卫星高度学数据发出的海面电流的重建。
For numerous earth observation applications, one may benefit from various satellite sensors to address the reconstruction of some process or information of interest. A variety of satellite sensors deliver observation data with different sampling patterns due satellite orbits and/or their sensitivity to atmospheric conditions (e.g., clour cover, heavy rains,...). Beyond the ability to account for irregularly-sampled observations, the definition of model-driven inversion methods is often limited to specific case-studies where one can explicitly derive a physical model to relate the different observation sources. Here, we investigate how end-to-end learning schemes provide new means to address multimodal inversion problems. The proposed scheme combines a variational formulation with trainable observation operators, {\em a priori} terms and solvers. Through an application to space oceanography, we show how this scheme can successfully extract relevant information from satellite-derived sea surface temperature images and enhance the reconstruction of sea surface currents issued from satellite altimetry data.
