Edge detection methods applied to the analysis of spherical raindrop images

Abstract

Optical imaging of raindrops provides important information on the statistical distribution of raindrop size and raindrop shape. These distributions are critical for extracting rainfall rates from both dual- and single-polarization radar signals. A large number of raindrop images are required to obtain these statistics, necessitating automatic processing of the imagery. The accuracy of the measured drop size depends critically on the characteristics of the digital image processing algorithm used to identify and size the drop. Additionally, the algorithm partially determines the effective depth of field of the camera/image processing system. Because a large number of drop images are required to obtain accurate statistics, a large depth of field is needed, which tends to increase errors in drop size measurement. This trade-off between accuracy and depth of field (dof) is also affected by the algorithm used to identify the drop outline. In this paper, eight edge detection algorithms are investigated and compared to determine which is best suited for accurately extracting the drop outline and measuring the diameter of an imaged raindrop while maintaining a relatively large depth of field. The algorithm which overall gave the largest dof along with the most accurate estimate of the size of the drop was the Hueckel algorithm [J. Assoc. Comput. Mach. 20, 634 (1973)].

Automatic Precipitation Measurement Based on Raindrop Imaging and Artificial Intelligence

“…Unfortunately, they suffer from different degrees of raindrop mismatches during sensing [12,13]. In the recent decade, Saylor et al adopted different edge detection algorithms for various depths to acquire more accurate raindrop images [14]. However, measurements by this approach performed outdoors remains uncertainty.…”

Measurements of rainfall rates from videos

“…In the literature, there is very little research work on this discrimination problem. Saylor and Sivasubramanian [14] used the existence of a bright hole in the center of a raindrop to judge whether the drop is in focus or not; however, the system needs a specialized light source to illuminate raindrops which is usually impossible when using CCTV cameras. Garg and Nayar [27] attempted to solve this problem using the velocity-size relationship of raindrops.…”

A video precipitation sensor for imaging and velocimetry of hydrometeors

“…SVI can measure the shapes and size distributions of snowflakes by using a CCD image sensor illuminated by a halogen flood lamp (Newman and Kucera, 2009). Rain Imaging System (RIS) used the similar principle (Saylor et al, 2002;Jones et al, 2003;Saylor and Sivasubramanian, 2007), but they cannot measure the fall velocity of snowflakes/raindrops. This paper presents a new ground-based video precipitation sensor (VPS) for imaging and velocimetry of hydrometeors; it can measure the shape, size, orientation, and fall velocity of naturally falling hydrometeors simultaneously by using a planar array CCD camera with its exposure controlled precisely, which represents a promising alternative to monitor precipitation particles.…”