The use of satellite imagery to monitor flood areas is essential to determine the damage and prevent related problems in the future. This paper examines thresholding and unsupervised classification for flood mapping using Sentinel-1 SAR image. Thresholding helps us to determine over-detection and under-detection regions in the flooded areas, and so, gamma distribution is used to select the thresholds. Also, the relevance vector machine (RVM) and the object-based classification method have been used for classification. The RVM algorithm obtained better results with overall accuracy = 0.89 and k = 0.95, while for the object-based classification method, these values were 0.87 and 0.91, respectively. According to the results, over- and under-detection occurred in flat areas and man-made structures, respectively. The results demonstrate a great potential of radar imagery for operational detection and delimitation of water in flood risk areas. The automation of satellite radar data processing operation has been tested, and it shows a potential for optimising the system of monitoring and early detection of flood risk.

中文翻译：

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使用相关向量机和 SAR 数据绘制洪水地图：伊朗阿卡拉的案例研究

使用卫星图像监测洪水区域对于确定损害和防止未来出现相关问题至关重要。本文研究了使用 Sentinel-1 SAR 图像进行洪水映射的阈值化和无监督分类。阈值可以帮助我们确定淹没区域的过度检测和检测不足区域，因此使用伽马分布来选择阈值。此外，相关向量机（RVM）和基于对象的分类方法已被用于分类。RVM 算法获得了更好的结果，总体准确度 = 0.89 和 k = 0.95，而对于基于对象的分类方法，这些值分别为 0.87 和 0.91。结果表明，在平坦区域和人造结构中分别发生了过度探测和探测不足。结果表明，雷达图像在洪水风险区的业务检测和水域划界方面具有巨大潜力。卫星雷达数据处理操作的自动化已经过测试，显示出优化洪水风险监测和早期检测系统的潜力。