A Vision-Based Precipitation Sensor for Detection and Classification of Hydrometeors

Measuring precipitation is an important part of ground observations of the Earth's atmosphere. Existing systems for this task focus mainly on the hydrometeors' microstructure (e.g., shape, size, and velocity), but seldom consider to classify them. This paper proposes a new vision-based system for precipitation observation and type recognition (POTR) comprising a single camera and other commercially available components. The system is efficient in terms of energy use and memory requirements by being able to switch between periodic and continuous monitoring as required, based on a fast detection algorithm for precipitation particles (FDAP). FDAP uses a background model and thresholding strategy to segment precipitation particles. In particular, it applies an area rule and a neighborhood rule to eliminate the influence of noise and external interference (e.g., flying insects) on the field observations. We describe precipitation particles using a composite representation that includes geometric features, Fourier descriptors, and Hu moment invariants, and also adopt gradient boosting trees to classify the type of precipitation. The experimental evaluation of POTR on data sets collected on-site in Beijing from August 2014 to February 2015 shows that FDAP has an accuracy of more than 96% and that type recognition has an accuracy more than 90%.