Hydrometor classification from 2 dimensional videodisdrometer data

Abstract. This paper presents a hydrometeor classification technique based on two-dimensional video disdrometer (2DVD) data. The method provides an estimate of the dominant hydrometeor type falling over time intervals of 60 s during precipitation, using as input the statistical behavior of a set of particle descriptors, calculated for each particle image. The employed supervised algorithm is a support vector machine (SVM), trained over precipitation time steps labeled by visual inspection. In this way, 8 dominant hydrometeor classes could be discriminated. The algorithm achieves accurate classification performances, with median overall accuracies (Cohen's K) of 90% (0.88), and with accuracies higher than 84% for each hydrometeor class.