Feature Analysis and Classification of Particle Data from Two-Dimensional Video Disdrometer

Sergey Gavrilov¹, Mamoru Kubo², Vu Anh Tran¹, Duc Luu Ngo¹, Ngoc Giang Nguyen¹, Lan Anh T. Nguyen^1,3, Favorisen Rosyking Lumbanraja¹, Dau Phan¹, Kenji Satou²

¹Graduate School of Natural Science and Technology, Kanazawa University, Kanazawa, Japan.
²Institute of Science and Engineering, Kanazawa University, Kanazawa, Japan.
³Department of Computer Science, Hue University of Education, Hue, Vietnam.

DOI: 10.4236/ars.2015.41001                

Abstract

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.

Keywords

Solid Precipitation, Particle Classification, 2DVD, Fractal Dimension, PCA

Conflicts of Interest

The authors declare no conflicts of interest.

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