Shape parameters for automatic classification of snow particles into snowflake and graupel

Meteorological applications

article

Karolina Nurzyńska¹  Mamoru Kubo¹  Ken-ichiro Muramoto¹ 

[1] Faculty of Electrical and Computer Engineering, Institute of Science and Engineering, Kanazawa University;Ishikawa National College of Technology

关键词: image processing;    radar meteorology;    shape features;   

DOI  :  10.1002/met.299

学科分类：社会科学、人文和艺术（综合）

来源: Wiley

【 摘 要 】

The meteorological radar backscatter profile depends on the shape of a falling particle. Because there are many shapes of snow particles it is difficult to estimate a precipitation rate in the case of snowfall. This study presents research which aimed to develop an automatic system for snow particle classification into snowflake and graupel. Having the information about the snowfall type during an analysis of snowfall rate and backscatter values allows improved forecasting of snowfall by better understanding of these phenomena. Five novel shape features derived from grey-scale images and designed in order to improve an automatic snow particle classification into snowflake and graupel are introduced. Their performance is compared to statistical and shape features well known from literature. For classification purposes, threshold, k -nearest neighbours, and support vector machine are used. Different classification systems are presented. The most correct classification ratio, of 91%, was achieved for a classifier built from a pair of roughness and Hu first order features. The suggested min max centre distance feature is in second place, with 90% efficiency.

【 授权许可】

CC BY|CC BY-NC|CC BY-NC-ND