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

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DOI: 10.4236/ars.2015.41001    3,266 Downloads   3,761 Views   Citations

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.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

Gavrilov, S. , Kubo, M. , Tran, V. , Ngo, D. , Nguyen, N. , Nguyen, L. , Lumbanraja, F. , Phan, D. and Satou, K. (2015) Feature Analysis and Classification of Particle Data from Two-Dimensional Video Disdrometer. Advances in Remote Sensing, 4, 1-14. doi: 10.4236/ars.2015.41001.

References

[1] Ohigashi, T. and Tsuboki, K. (2005) Structure and Maintenance Process of Stationary Double Snowbands along the Coastal Region. Journal of the Meteorological Society of Japan, 83, 331-349.
http://dx.doi.org/10.2151/jmsj.83.331
[2] Harimaya, T., Kodama, H. and Muramoto, K. (2004) Regional Differences in Snowflake Size Distributions. Journal of the Meteorological Society of Japan, 82, 895-903.
http://dx.doi.org/10.2151/jmsj.2004.895
[3] Brandes, E.A., Ikeda, K., Zhang, G., Schonhuber, M. and Rasmussen, R.M. (2007) A Statistical and Physical Description of Hydrometeor Distributions in Colorado Snowstorms Using a Video Disdrometer. Journal of Applied Meteorology and Climatology, 46, 634-650.
http://dx.doi.org/10.1175/JAM2489.1
[4] Hung, G., Bringi, V.N., Cifelli, R., Hudak, R. and Petersen, W.A. (2010) A Methodology to Derive Radar Reflectivity- Liquid Equivalent Snow Rate Relations Using C-Band Radar and a 2D Video Disdrometer. Journal of Atomospheric and Oceanic Technology, 27, 637-651.
http://dx.doi.org/10.1175/2009JTECHA1284.1
[5] Hung, G., Bringi, V.N., Moisseev, D., Petersen, W.A., Blivend, L. and Hudake, D. (2014) Use of 2D-Video Disdrometer to Derive Mean Density-Size and Ze-SR Relations: Four Snow Cases from the Light Precipitation Validation Experiment. Atmospheric Research, 153, 34-48.
http://dx.doi.org/10.1016/j.atmosres.2014.07.013
[6] Zhang, G., Luchs, S., Ryzhkov, A., Xue, M., Ryzhkova, L. and Cao, Q. (2011) Winter Precipitation Microphysics Characterized by Polarimetric Radar and Video Disdrometer Observations in Central Oklahoma. Journal of Applied Meteorology and Climatology, 50, 1558-1570.
http://dx.doi.org/10.1175/2011JAMC2343.1
[7] Nurzynska, K., Kubo, M. and Muramoto, K. (2010) 2D Feature Space for Snow Particle Classification into Snowflake and Graupel. IEICE Transactions on Information and Systems, E93-D, 3344-3351.
http://dx.doi.org/10.1587/transinf.E93.D.3344
[8] Kruger, A. and Krajewski, W.F. (2002) Two-Dimensional Video Disdrometer: A Description. Journal of Atomospheric and Oceanic Technology, 19, 602-617.
http://dx.doi.org/10.1175/1520-0426(2002)019<0602:TDVDAD>2.0.CO;2
[9] Grazioli, J., Tuia, D., Monhart, S., Schneebeli, M., Raupach, T. and Berne, A. (2014) Hydrometeor Classification from Two-Dimensional Video Disdrometer Data. Atmospheric Measurement Techniques, 7, 2869-2882.
http://dx.doi.org/10.5194/amt-7-2869-2014
[10] Ishimoto, M. (2008) Radar Backscattering Computations for Fractal-Shaped Snowflakes. Journal of the Meteorological Society of Japan, 86, 459-469.
http://dx.doi.org/10.2151/jmsj.86.459
[11] Maruyama, K. and Fujiyoshi, Y. (2005) Monte Carlo Simulation of the Formation of Snowflakes. Journal of the Atmospheric Sciences, 62, 1529-1544.
http://dx.doi.org/10.1175/JAS3416.1
[12] Tolle, C.R., McJunkin, T.R. and Gorsich, D.J. (2003) Suboptimal Minimum Cluster Volume Cover-Based Method for Measuring Fractal Dimension. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25, 32-41.
http://dx.doi.org/10.1109/TPAMI.2003.1159944
[13] Russ, J.C. (2011) The Image Processing Handbook. 6th Edition, CRC Press, Boca Raton, 604-610.
http://www.crcpress.com/product/isbn/9781439840450
[14] Vapnik, V. (1998) Statistical Learning Theory. Wiley, New York.

  
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