2D Feature Space for Snow Particle Classification into Snowflake and Graupel

Karolina NURZYNSKA  Mamoru KUBO  Ken-ichiro MURAMOTO  

IEICE TRANSACTIONS on Information and Systems   Vol.E93-D   No.12   pp.3344-3351
Publication Date: 2010/12/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.E93.D.3344
Print ISSN: 0916-8532
Type of Manuscript: PAPER
Category: Pattern Recognition
image processing,  snow particle features,  classification,  

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This study presents three image processing systems for snow particle classification into snowflake and graupel. All of them are based on feature classification, yet as a novelty in all cases multiple features are exploited. Additionally, each of them is characterized by a different data flow. In order to compare the performances, we not only consider various features, but also suggest different classifiers. The best achieved results are for the snowflake discrimination method applied before statistical classifier, as the correct classification ratio in this case reaches 94%. In other cases the best results are around 88%.