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首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >Learning to Generate Radar Image Sequences Using Two-Stage Generative Adversarial Networks
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Learning to Generate Radar Image Sequences Using Two-Stage Generative Adversarial Networks

机译：使用两级生成对抗性网络学习生成雷达图像序列

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摘要

While quantitative precipitation estimation (QPE) using weather radar is widely adopted in operation, precipitation data sets are often highly imbalanced. In particular, extreme precipitation usually lacks representation, which may introduce the bottleneck for radar QPE with machine learning models. Discovering the intrinsic characteristic of extreme precipitation with few samples is challenging. In this letter, we focus on the radar reflectivity data and aim to generate synthetic radar image sequences with respect to extreme precipitation. Considering the relatively long interval between continuous radar images due to radar volume scan, traditional methods in video generation are not suitable. In this letter, we propose Two-stage Generative Adversarial Networks (TsGANs) to address the above-mentioned problem. In general, our TsGAN constructs adversarial process between generators and discriminators: the generator produces samples similar to real data, while the discriminator determines whether or not a sample is eligible. In Stage I, we generate an image sequence containing content and motion features. In Stage II, we design an enhanced net structure to enrich the adversarial processes and further improve the motion features. Experimental testing is performed within the radar coverage in Shenzhen, China, on rainfall events in 2014-2016. Results show that our TsGAN is superior to previous works.
机译：虽然使用天气雷达的定量降水估计（QPE）在操作中广泛采用，但降水数据集通常高度不平衡。特别是，极端降水通常缺乏表示，这可能会引入带有机器学习模型的雷达QPE的瓶颈。发现少量样品极端降水的内在特征是具有挑战性的。在这封信中，我们专注于雷达反射率数据，并旨在相对于极端沉淀产生合成雷达图像序列。考虑到由于雷达体积扫描导致的连续雷达图像之间的相对长的间隔，视频生成中的传统方法不合适。在这封信中，我们提出了两阶段生成的对抗网络（Tsgans）来解决上述问题。通常，我们的Tsgan构建了发电机和鉴别器之间的对抗过程：发电机产生类似于实际数据的样本，而鉴别器确定样本是否有资格。在阶段I中，我们生成包含内容和运动功能的图像序列。在II期，我们设计增强的净结构，以丰富对抗过程，进一步提高运动功能。在2014 - 2016年中国深圳的雷达覆盖范围内进行实验测试。结果表明，我们的赛格优于以前的作品。

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