An automated image dehazing method for flood detection to improve flood alert monitoring system

Abstract

Flood hazard prevention and mitigation is an emergent environmental problem. Traditional flood monitoring devices operated under adverse environmental conditions are typically influenced by changes in weather conditions such as haze, fog and rain. Consequently, the video images obtained from such devices are often blurred or damaged, increasing the possibility of erroneous assessments in hazard mitigation processes. To ensure the efficient use of image analysis technology to improve degraded images captured under hazy weather conditions, this study proposes an automated single image dehazing method for flood monitoring. This method is based on the dark channel prior for the removal of haze from a single image. The concept of the dark channel prior is that most local patches in haze-free outdoor images contain some pixels having extremely low intensities in at least one colour channel. When this dark channel prior is used, the thickness of haze in the image can be directly estimated and a high-quality haze-free image can be obtained. The proposed method can be used to accurately improve flood detection and monitoring results. The ability to detect and remove haze from a single image is a crucial function when applying automated computer vision to disaster-monitoring applications. The experimental results show that the proposed method can efficiently alleviate the degradation of surveillance images and effectively identify flooded regions in particular areas.