Gavrilov, Sergey and Kubo, Mamoru and Tran, Vu Anh and Ngo, Duc Luu and Nguyen, Ngoc G and Nguyen, Lan Anh T. and Lumbanraja, Favorisen R and Phan, Dau and Satou, Kenji (2015) Feature Analysis and Classification of Particle Data from Two-DimensionalVideo Disdrometer. Advances in Remote Sensing, 4 (1). pp. 1-14. ISSN 2169-2688


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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.

Item Type: Article
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Fakultas Matematika dan Ilmu Pengetahuan Alam (FMIPA) > Prodi Ilmu Komputer
Depositing User: Favorisen R Lumbanraja
Date Deposited: 24 Feb 2021 05:04
Last Modified: 24 Feb 2021 05:04

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