Image Segmentation Methods for Flood Monitoring System

Abstract

Flood disasters are considered annual disasters in Malaysia due to their consistent occurrence. They are among the most dangerous disasters in the country. Lack of data during flood events is the main constraint to improving flood monitoring systems. With the rapid development of information technology, flood monitoring systems using a computer vision approach have gained attention over the last decade. Computer vision requires an image segmentation technique to understand the content of the image and to facilitate analysis. Various segmentation algorithms have been developed to improve results. This paper presents a comparative study of image segmentation techniques used in extracting water information from digital images. The segmentation methods were evaluated visually and statistically. To evaluate the segmentation methods statistically, the dice similarity coefficient and the Jaccard index were calculated to measure the similarity between the segmentation results and the ground truth images. Based on the experimental results, the hybrid technique obtained the highest values among the three methods, yielding an average of 97.70% for the dice score and 95.51% for the Jaccard index. Therefore, we concluded that the hybrid technique is a promising segmentation method compared to the others in extracting water features from digital images.

Water Level Fluctuation Using Surveillance Camera

“…A single method of image segmentation could not be used for all types of images and all image segmentation methods could not perform well for one particular image (Muhadi et al, 2020). Therefore, a hybrid technique, which consists of multiple methods of image segmentation could improve the segmentation results (Khan, 2014).…”

“…To date, no general solution has been found to solve the image segmentation problems in order to achieve reliable accuracy for any applications including flood disaster applications. However, previous studies reported that the hybrid technique, a combination of two or more segmentation methods, has more advantages than using an individual segmentation method (Khan, 2014;Lankton et al, 2007;Muhadi et al, 2020;Singh & Misra, 2017).…”

Video Surveillance-Based Urban Flood Monitoring System Using a Convolutional Neural Network

“…The outcome shows that, based on SWAP results, the flood region can be mirrored, and H emission data are more adaptive to V polarization data. Muhadi et al [14] developed a model of a real-time flood water level tracking device using Arduino Uno. The first test tested the total amount of time; the second test determined whether the machine should use three LEDs as its early warning mechanism to alert people from afar.…”

Deep Learning Semantic Segmentation for Water Level Estimation Using Surveillance Camera

“…Due to the limitations in current methods used for flood monitoring, the surveillance camera has become a popular alternative to be used as an input source for flood monitoring events, especially in a small-scale areas [14,27]. The information obtained from the surveillance camera could be in still images [28,29] or video footage [30,31] and could be extracted using various computer vision techniques. Computer vision is the common approach to capture and process the data from a surveillance camera for flood disaster applications by employing a visual sensor [32,33].…”

“…For example, Lo et al [37] proposed a graph-based segmentation method where a virtual seed is used as guidance for flood region detection from images captured by the surveillance camera. Even though the graph-based and other segmentation methods proved to be capable of extracting flood information from the image, it is found that the methods are often handcrafted for specific data as prior knowledge of the data needs to be analyzed [28]. Hence, it may be difficult to apply these methods to other data images.…”