In-situ water level measurement using NIR-imaging video camera

Abstract

Water level is a critical component for observation and management of water resources. Since video surveillance is becoming a standard configuration of gauging stations, more attentions have been paid on the image-based water level measurement techniques in recent years. Instead of human eyes, images of staff gauge can be captured by a camera and automatically processed to detect the readings of water line. However, it is quite unreliable to identify the readings on staff gauges due to low imaging resolution, tilt viewing angle and complex illumination in field conditions. For that reason, most of the existing methods failed to make effective automatic measurements in practice. To resolve difficulties of poor visibility, image distortions and ambient noises in in-situ water level measurement with standard bicolor staff gauges, an all-weather, real-time and automatic flow measurement system using single near infrared (NIR)-imaging video camera is developed. Lab and in-situ experiments demonstrate that NIR-imaging is efficient to enhance image contrast and suppress reflection noises on the water surface, which successfully overcomes the limitation of water line detection with current visible light (VIS)-imaging systems in clear water and low velocity conditions. The proposed water level conversion method is based on image ortho-rectification. It requires no on-site calibration by utilizing natural corresponding points to build the perspective transformation between the staff gauge Region of Interest (ROI) and an orthographic template image. The measurement resolution of water level (1 mm) is controlled by the physical resolution (1 mm/pixel) of the template. The proposed water line detection approach is integrated by a series of algorithms: the Order-Statistic Filtering for adaptive thresholding, the Morphological Opening Operation for local noise suppression, the Multi-points Continuity Criterion for water line locating and the Median Filtering for random noise elimination. It overcomes the deficiency of traditional Otsu method for images with uneven illumination. The reliability of system under various weather and illumination conditions is proved with water level accuracy up to 1 cm and Effective Data Ratios up to 95%.