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.
Highlights
Rapid urbanization, population growth, and extreme climate change have led to frequent flooding events, posing challenges for many countries around the world
Borges et al [23] introduced a probabilistic model for flood detection in videos
The hybrid technique begins with the initial curve, moves each point on the curve based on the analysis of local interior and exterior regions
Summary
Population growth, and extreme climate change have led to frequent flooding events, posing challenges for many countries around the world. Borges et al [23] introduced a probabilistic model for flood detection in videos They combined the statistical characteristics of floods, such as color, texture, and saturation characteristics, using the Bayes classifier along with frame-to-frame changes to determine the flood presence. [24] conducted a study similar to the work of Borges et al [23] using the thresholding method to segment the flood and non-flood regions depending on color, size, and patterns of ripples. This approach offers good flood detection capabilities but is limited by the reflections on the floodwater.
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