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Development of a method for flood detection based on Sentinel-1 images and classifier algorithms

机译：基于Sentinel-1图像和分类器算法的洪水检测方法的开发

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摘要

Floods are one of the most devastating natural disasters in the world, displacing millions of people each year and causing severe damage to people's lives and infrastructure. It is the most widespread hydrological hazard worldwide that affects water management, nature protection, economic activities, hydromorphological alterations on ecosystem services and human health. Real-time monitoring systems play a key role in flood risk reduction and disaster response decisions. Studies have shown that using earth observation data for flood monitoring and timely actions based on good quality information reduces damages. In this paper, after thresholding, a machine learning algorithm and an object-based classification method were used to classify the SAR data. Thresholding helps detect regions in the flooded areas. A comparison of the results showed that the machine learning algorithm obtained significant results. Because of the results obtained, the usefulness of Sentinel-1 images as a baseline data for the improvement of the methodological guide is appreciated and should be used as a new source to monitor the flood risks.
机译：洪水是世界上最毁灭性的自然灾害之一，每年衡量数百万人，并对人们的生活和基础设施造成严重损害。全球最广泛的水文危害，影响水管理，自然保护，经济活动，生态系统服务和人类健康的水平改变。实时监测系统在洪水风险减少和灾害响应决策中发挥关键作用。研究表明，基于良好质量信息，使用地球观测数据进行洪水监测和及时行动减少了损害。在本文中，在阈值化之后，使用机器学习算法和基于对象的分类方法来分类SAR数据。阈值化有助于检测淹水区域的区域。结果表明，机器学习算法获得了显着的结果。由于获得的结果，Sentinel-1图像作为改善方法指导的基线数据的有用性，并且应用作监测洪水风险的新来源。

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