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Image Sequence Retrieval for Forecasting Weather Radar Echo Pattern

机译:图像序列检索用于预测天气雷达回波图

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摘要

A novel framework and method are proposed to retrieve image sequences with the goal of forecasting complex and time-varying natural pattern. As a such pattern, we tackled weather radar images representing the spatial distribution of precipitation, the application is the local short-term forecasting of precipitation. In our approach, the forcast is made by retrieving past patterns similar to the present pattern, and the forecast pattern is produced by using its subsequent pattern of the retrieved sequences. In addition to the global distribution feature and velocity field of the pattern, we extract temporal texture features to catch the features of the echo patterns, which are nonrigid and deformable,a nd that appear and disappear. Similar sequences are retrieved based on a distance measure between paths in eigenspaces derived from the feature vectors. Several experiments confirm the performance of our retrieval scheme and indicate the predictability of the pattern.
机译:提出了一种新颖的框架和方法来检索图像序列,以预测复杂且时变的自然模式。作为这种模式,我们处理了代表降水空间分布的天气雷达图像,其应用是本地的短期降水预报。在我们的方法中,通过检索与当前模式相似的过去模式来进行预测,并通过使用其随后的检索序列模式来生成预测模式。除了图案的全局分布特征和速度场外,我们还提取时间纹理特征以捕捉回波图案的特征,这些特征非刚性且不可变形,并且会出现和消失。基于从特征向量派生的本征空间中路径之间的距离度量,检索相似序列。几个实验证实了我们检索方案的性能,并表明了模式的可预测性。

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