Open AccessShape parameters for automatic classification of snow particles into snowflake and graupel
Author(s) -
Nurzyńska Karolina,
Kubo Mamoru,
Muramoto Kenichiro
Publication year - 2013
Publication title -
meteorological applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.672
H-Index - 59
eISSN - 1469-8080
pISSN - 1350-4827
DOI - 10.1002/met.299
Subject(s) - graupel , snowflake , snow , computer science , remote sensing , environmental science , backscatter (email) , meteorology , artificial intelligence , pattern recognition (psychology) , geology , geography , telecommunications , wireless
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.