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首页> 外文期刊>IEEE Transactions on Geoscience and Remote Sensing. >Layered Estimation of Atmospheric Mesoscale Dynamics From Satellite Imagery
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Layered Estimation of Atmospheric Mesoscale Dynamics From Satellite Imagery

机译：卫星影像大气中尺度动力学的分层估计

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In this paper, we address the problem of estimating mesoscale dynamics of atmospheric layers from satellite image sequences. Due to the great deal of spatial and temporal distortions of cloud patterns and because of the sparse 3-D nature of cloud observations, standard dense-motion field-estimation techniques used in computer vision are not well adapted to satellite images. Relying on a physically sound vertical decomposition of the atmosphere into layers, we propose a dense-motion estimator dedicated to the extraction of multilayer horizontal wind fields. This estimator is expressed as the minimization of a global function including data and spatio-temporal smoothness terms. A robust data term relying on the integrated-continuity equation mass-conservation model is proposed to fit sparse-transmittance observations related to each layer. A novel spatio-temporal smoother derived from large eddy prediction of a shallow-water momentum-conservation model is used to build constraints for large-scale temporal coherence. These constraints are combined in a global smoothing framework with a robust second-order smoother, preserving divergent and vorticity structures of the flow. For optimization, a two-stage motion estimation scheme is proposed to overcome multiresolution limitations when capturing the dynamics of mesoscale structures. This alternative approach relies on the combination of correlation and optical-flow observations in a variational context. An exhaustive evaluation of the novel method is first performed on a scalar image sequence generated by direct numerical simulation of a turbulent 2-D flow. By qualitative comparisons, the method is then assessed on a METEOSAT image sequence.
机译：在本文中，我们解决了根据卫星图像序列估算大气层中尺度动力学的问题。由于云模式的大量时空扭曲以及云观测的稀疏3D性质，计算机视觉中使用的标准密集运动场估计技术无法很好地适应卫星图像。依靠大气中垂直的物理分解为多层的分解，我们提出了一种密集运动估计器，专门用于提取多层水平风场。该估计量表示为包括数据和时空平滑度项的全局函数的最小化。提出了一种基于积分连续性方程质量守恒模型的鲁棒数据项，以拟合与每一层有关的稀疏透射率观测值。从浅水动量守恒模型的大涡流预测推导的新型时空平滑器用于建立大规模时间相干性的约束条件。这些约束在全局平滑框架中与健壮的二阶平滑器结合在一起，可保留流的发散和涡度结构。为了进行优化，提出了一种两阶段运动估计方案，以克服捕获中尺度结构动力学时的多分辨率限制。这种替代方法依赖于变体情况下相关性和光流观测的结合。首先对由湍流二维流的直接数值模拟生成的标量图像序列进行详尽评估。通过定性比较，然后在METEOSAT图像序列上评估该方法。

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