A machine learning approach for classifying bird and insect radar echoes with S-band Polarimetric Weather Radar
A machine learning approach for classifying bird and insect radar echoes with S-band Polarimetric Weather Radar
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一种利用 S 波段偏振天气雷达对鸟类和昆虫雷达回波进行分类的机器学习方法
DOI：
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发表时间：
2021
影响因子：
2.2
通讯作者：
Tian
中科院分区：
文献类型：
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作者：
P. Jatau;V. Melnikov;Tian
S 波段WSR-88D 气象雷达足够灵敏，可以观察鸟类和昆虫等生物散射体。然而，它们的非球形形状和在雷达分辨率体积中的频繁搭配给识别它们的回波带来了挑战。我们提出了一种通过对多次雷达扫描的双偏振测量进行相干平均来提取鸟类（或昆虫）特征的方法，其中包含鸟类（昆虫）的迁徙。还计算附加特征以捕获方面和范围依赖性，以及这些回波在局部区域上的变化。接下来，训练岭分类器和决策树机器学习算法，首先仅使用平均双 pol 输入，然后添加剩余特征的不同组合。使用根据测试数据计算的指标来分析这两种方法的所有模型的性能。对不同鸟类/昆虫模式的进一步研究，包括栖息鸟类、鸟类迁徙和昆虫迁徙案例，用于进一步研究我们模型的通用性。总体而言，仅使用双偏振变量的脊分类器在所有这些测试中都表现出一致的良好性能。我们的建议是，该分类器可以在美国下一代雷达（NEXRAD）上使用，作为生物回波分类的第一步。它将与现有的水凝物分类算法 (HCA) 结合使用，HCA 首先将生物回波与非生物回波分开，然后我们的算法将应用于进一步将生物回波分离为鸟类和昆虫。据我们所知，这项研究是第一个训练机器学习分类器的研究，该分类器能够基于每个距离门的双偏振变量来检测鸟类和昆虫回声的不同模式。
The S-bandWSR-88D weather radar is sensitive enough to observe biological scatterers like birds and insects. However, their non-spherical shapes and frequent collocation in the radar resolution volume create challenges in identifying their echoes. We propose a method of extracting bird (or insect) features by coherently averaging dual polarization measurements from multiple radar scans, containing bird (insect) migration. Additional features are also computed to capture aspect and range dependence, and the variation of these echoes over local regions. Next, ridge classifier and decision tree machine learning algorithms are trained, first only with the averaged dual pol inputs and then different combinations of the remaining features are added. The performance of all models for both methods, are analyzed using metrics computed from the test data. Further studies on different patterns of birds/insects, including roosting birds, bird migration and insect migration cases, are used to further investigate the generality of our models. Overall, the ridge classifier using only dual polarization variables was found to perform consistently well across all these tests. Our recommendation is that this classifier can be used operationally on the US Next-Generation Radars (NEXRAD), as a first step in classifying biological echoes. It would be used in conjunction with the existing Hydrometeor Classification Algorithm (HCA), where the HCA would first separate biological from non-biological echoes, then our algorithm would be applied to further separate biological echoes into birds and insects. To the best of our knowledge, this study is the first to train a machine learning classifier that is capable of detecting diverse patterns of bird and insect echoes, based on dual polarization variables at each range gate.