Wind field analysis from synthetic aperture radar images allows the estimation of wind direction and speed based on image descriptors. In this paper, we propose a framework to automate wind direction retrieval based on wavelet decomposition associated with spectral processing. We extend existing undecimated wavelet transform approaches, by including à trous with B3 spline scaling function, in addition to other wavelet bases as Gabor and Mexican-hat. The purpose is to extract more reliable directional information, when wind speed values range from 5 to 10 ms−1. Using C-band empirical models, associated with the estimated directional information, we calculate local wind speed values and compare our results with QuikSCAT scatterometer data. The proposed approach has potential application in the evaluation of oil spills and wind farms.
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机译：来自合成孔径雷达图像的风场分析允许基于图像描述符估计风向和风速。在本文中，我们提出了一种基于与频谱处理相关的小波分解的风向自动检索框架。除了包括Gabor和Mexican-hat的其他小波基外，我们还通过将Btr样条缩放功能包括在内来扩展现有的未抽取小波变换方法。目的是在风速值介于5到10 ms -1 范围内时提取更可靠的方向信息。使用与估计的方向信息关联的C波段经验模型，我们计算局部风速值，并将结果与QuikSCAT散射仪数据进行比较。所提出的方法在溢油和风电场评估中具有潜在的应用。
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