Identification of Inundation Using Low-Resolution Images from Traffic-Monitoring Cameras: Bayes Shrink and Bayesian Segmentation

Yin Chao Wu,Suyun Ham,Seong Jin Noh

doi:10.3390/w12061725

Yin Chao Wu, Suyun Ham + Show 1 more

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https://doi.org/10.3390/w12061725

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Abstract

This study presents a comparative assessment of image enhancement and segmentation techniques to automatically identify the flash flooding from the low-resolution images taken by traffic-monitoring cameras. Due to inaccurate equipment in severe weather conditions (e.g., raindrops or light refraction on camera lenses), low-resolution images are subject to noises that degrade the quality of information. De-noising procedures are carried out for the enhancement of images by removing different types of noises. For the comparative assessment of de-noising techniques, the Bayes shrink and three conventional methods are compared. After the de-noising, image segmentation is implemented to detect the inundation from the images automatically. For the comparative assessment of image segmentation techniques, k-means segmentation, Otsu segmentation, and Bayesian segmentation are compared. In addition, the detection of the inundation using the image segmentation with and without de-noising techniques are compared. The results indicate that among de-noising methods, the Bayes shrink with the thresholding discrete wavelet transform shows the most reliable result. For the image segmentation, the Bayesian segmentation is superior to the others. The results demonstrate that the proposed image enhancement and segmentation methods can be effectively used to identify the inundation from low-resolution images taken in severe weather conditions. By using the principle of the image processing presented in this paper, we can estimate the inundation from images and assess flooding risks in the vicinity of local flooding locations. Such information will allow traffic engineers to take preventive or proactive actions to improve the safety of drivers and protect and preserve the transportation infrastructure. This new observation with improved accuracy will enhance our understanding of dynamic urban flooding by filling an information gap in the locations where conventional observations have limitations.

Highlights

In the face of natural disasters such as flash flooding, prompt information is crucial to establish a mitigation plan and find the best route for first responders
Fourier transform (FFT), which converts the information from the spatial domain to a frequency image after the fast Fourier transform (FFT), which converts the information from the spatial domain to a frequency domain [27]
We comparatively studied image-processing methods, such as de-noising methods and image segmentation, to automatically detect the flooded areas from the low-resolution images

Summary

Introduction

In the face of natural disasters such as flash flooding, prompt information is crucial to establish a mitigation plan and find the best route for first responders. These rains cause unprecedented flooding and cause severe fatalities and hundreds of billions of US dollars in damages. Such an extreme flood damages roads and bridges and cuts off evacuation routes and rescue paths. In many parts of the US, occurrences of “rare” extreme precipitation and flooding events are a new normal [1]. Typical measurement methods include in-situ water level sensors in streams, remote sensing

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