科研之友
微信
新浪微博
Facebook
分享链接
摘要
Many weather features such as precipitation and snow depth can be recorded using automatic surface observation systems. However, automatically observing dew and frost presents several problems. Many studies have used various wetness sensors and passive microwave devices to detect dew. Unfortunately, several of these sensors are complex, and only a few are capable of detecting frost. This paper proposes a novel method for indirectly detecting dew and frost based on computer vision. The setup is simple, inexpensive, and only requires images of several glass substrates near the underlying surface. Images taken during dew or frost formation exhibit distinct changes in hierarchical visual features. These changes are detected by tracking the variations of several low-level statistical features that are extracted from the images in time. Additionally, an effective texture analysis method is proposed to describe the morphology of frost. Field experiments were conducted at several weather stations in Beijing, China. The validation of the method for measuring the onset and duration of dew/frost on short grass shows that 1) the proposed computer-vision-based algorithm achieves an accuracy of approximately 90% in discriminating among dewy, frosty, and dry nights based on the hourly manual observations of the grass surface and 2) the algorithm is also capable of measuring the duration of dew and frost on grass with about 70% accuracy.
- 出版日期2014-12
- 单位华中科技大学
