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.

中文翻译：

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洪水监测系统的图像分割方法

洪水灾害在马来西亚被认为是一年一度的灾害，因为它们经常发生。它们是该国最危险的灾难之一。洪水事件期间缺乏数据是改进洪水监测系统的主要制约因素。随着信息技术的快速发展，使用计算机视觉方法的洪水监测系统在过去十年中受到了关注。计算机视觉需要图像分割技术来理解图像的内容并促进分析。已经开发了各种分割算法来改善结果。本文介绍了用于从数字图像中提取水信息的图像分割技术的比较研究。对分割方法进行了视觉和统计评估。为了统计地评估分割方法，计算骰子相似系数和Jaccard指数来衡量分割结果与ground truth图像的相似度。根据实验结果，混合技术在三种方法中获得了最高值，骰子得分平均为 97.70%，Jaccard 指数平均为 95.51%。因此，我们得出结论，与其他技术相比，混合技术是一种很有前途的分割方法，可以从数字图像中提取水特征。