Feature Analysis and Classification of Particle Data from Two-Dimensional Video Disdrometer
Feature Analysis and Classification of Particle Data from Two-Dimensional Video Disdrometer
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DOI：
10.4236/ars.2015.41001
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发表时间：
2015-01
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影响因子：
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通讯作者：
S. Gavrilov;Mamoru Kubo;Vu Anh Tran;D. Ngo;Ngoc Nguyen;L. A. T. Nguyen;F. R. Lumbanraja;Dau Phan
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文献类型：
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作者：
S. Gavrilov;Mamoru Kubo;Vu Anh Tran;D. Ngo;Ngoc Nguyen;L. A. T. Nguyen;F. R. Lumbanraja;Dau Phan
我们研制了一套固体降水二维视频测速仪地面观测系统。在该系统观测到的16，010个粒子中，约10%的粒子被随机抽样并手动分类为雪花，雪花状，中间，谷粒状和谷粒。首先，每个颗粒被表示为一个矢量的72个特征，包含分形维数和盒计数来表示颗粒形状的复杂性。对数据集的特征分析阐明了分形维数和盒数特征对表征从雪花到谷粒的颗粒的重要性。另一方面，两类分类的支持向量机（SVM）的性能进行了评估。实验结果表明，仅从72个特征中选择10个，将颗粒分类为雪花和谷粒的平均准确率可以达到95.4%左右，这是以前的研究所没有达到的。
We developed a ground observation system for solid precipitation using two-dimensional video disdrometer (2DVD). Among 16,010 particles observed by the system, around 10% of them were randomly sampled and manually classified into five classes which are snowflake, snowflake-like, intermediate, graupel-like, and graupel. At first, each particle was represented as a vector of 72 features containing fractal dimension and box-count to represent the complexity of particle shape. Feature analysis on the dataset clarified the importance of fractal dimension and box-count features for characterizing particles varying from snowflakes to graupels. On the other hand, performance evaluation of two-class classification by Support Vector Machine (SVM) was conducted. The experimental results revealed that, by selecting only 10 features out of 72, the average accuracy of classifying particles into snowflakes and graupels could reach around 95.4%, which had not been achieved by previous studies.