Texture-based optical flow for wind velocity estimation from water vapor data
Texture-based optical flow for wind velocity estimation from water vapor data
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DOI：
10.1117/12.2663008
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发表时间：
2023-06
期刊：
影响因子：
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通讯作者：
Joel Barnett;A. Bertozzi;L. Vese;I. Yanovsky
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文献类型：
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作者：
Joel Barnett;A. Bertozzi;L. Vese;I. Yanovsky
大气风速的准确估计在天气预报、飞行安全评估和气旋跟踪等方面具有重要意义。红外和微波卫星仪器捕获的大气数据为天气分析提供了全球覆盖范围。从这些数据中提取风速场传统上是通过计算机视觉的特征跟踪，相关/匹配或光流手段来完成的。然而，这些恢复要么稀疏的速度估计，过于光滑的细节，或被设计为准刚体运动，过度惩罚涡度和发散内往往湍流天气系统。我们提出了一个纹理为基础的光流过程量身定制的水汽数据。我们的方法实现了L1数据项和总变分正则化，并采用结构纹理图像分解来识别关键特征，这些特征可以提高恢复率，并有助于保留显着的涡度和发散结构。我们将该过程扩展到多保真度方案，并在模拟的海洋中尺度对流系统以及对流和温带气旋数据集上测试了两种流量估计方法，每个数据集都有相应的地面真实风速，因此我们可以定性地比较性能与现有的光学流量方法。
Accurate estimation of atmospheric wind velocity plays an important role in weather forecasting, flight safety assessment and cyclone tracking. Atmospheric data captured by infrared and microwave satellite instruments provide global coverage for weather analysis. Extracting wind velocity fields from such data has traditionally been done through feature tracking, correlation/matching or optical flow means from computer vision. However, these recover either sparse velocity estimates, oversmooth details or are designed for quasi-rigid body motions which over-penalize vorticity and divergence within the often turbulent weather systems. We propose a texture based optical flow procedure tailored for water vapor data. Our method implements an L1 data term and total variation regularizer and employs a structure-texture image decomposition to identify key features which improve recoveries and help preserve the salient vorticity and divergence structures. We extend this procedure to a multi-fidelity scheme and test both flow estimation methods on simulated over-ocean mesoscale convective systems and convective and extratropical cyclone datasets, each of which have accompanying ground truth wind velocities so we can qualitatively compare performances with existing optical flow methods.