【摘要】 及时准确的洪涝范围提取可以提高应急管理部门对于洪涝灾害的响应能力，减轻灾害影响。SAR遥感不受云雨影响，是洪涝灾害监测的有效工具。然而，由于卫星重返周期的限制，基于SAR的洪涝连续观测较难实现。在应急背景下，如何快速、实时进行洪涝范围提取是急需解决的问题。本文提出了一种结合遥感、VGI等多源数据的洪涝范围提取与模拟方法：(1)构建Albert+CNN的文本分类模型提取社交媒体洪涝信息；(2)基于异常值剔除方法利用社交媒体和OSM等VGI数据代替人工采样对Sentinel-1 SAR数据进行分类，提取洪涝范围；(3)结合社交媒体、水位数据等多源数据基于SNIC分割和成本距离等方法模拟无可用SAR数据时的洪涝淹没情况，提高洪涝的淹没范围提取频次。研究表明，本文基于Albert+CNN与异常值剔除的样本自动生成方法，可以有效辅助SAR数据的洪涝分类；利用VGI数据结合水情、DEM等多源数据进行了洪涝范围的模拟可以增加洪涝监测的时间分辨率。本研究有助于提高洪涝信息提取能力，为VGI支持洪涝灾害的应急管理提供参考。更多还原

【Abstract】 Timely and accurate flood extraction can improve the ability of emergency management departments to respond to flood disasters and thus reduce the impact of disasters. Synthetic Aperture Radar(SAR) remote sensing is not affected by cloud and rain and acts as an effective tool for flood disaster monitoring. However,rapid and automated sample acquisition for accurately obtaining flood information still poses challenges in the post-disaster emergency stage. In addition, due to the satellite’s limited revisit period, flood extent extraction based on remote sensing images has a certain time gap. Therefore, how to delineate flood extent in real time is an urgent problem to be solved. In this paper, combining SAR images, Volunteered Geographic Information(VGI),and other multi-source data, an automated flood inundation mapping and estimation method is proposed to identify the flood in a nearly real-time way. Firstly, the Albert+CNN text classification model is constructed to extract flood information from social media. Secondly, based on the outlier elimination method, the Sentinel-1SAR data are classified using VGI data such as social media and OSM instead of manual sampling, and the flood extent is extracted. Thirdly, in combination with social media, water level data, and other multi-source data, the flood inundation without SAR data is simulated based on SNIC segmentation and cost distance methods. The results show that the text classification model based on Albert + CNN model constructed in this paper can accurately classify the flood information of social media, with the classification accuracy up to 83.6%. The automatic sample generation method based on outlier elimination can effectively assist flood classification from SAR data. The accuracy of flood classification using the generated samples and random forest is 92.7%, which is more than 10% higher than that of the OSTU-based methods. Using VGI data and multi-source data such as water situation and DEM to simulate the flood extent can further increase the temporal resolution of flood monitoring. In this study area, two periods of flood simulation results are provided during the flood process,improving the flood disaster information. This study is helpful to improve the efficiency of flood information extraction and provide reference for VGI to support the emergency management of flood disasters. In the future,the picture information in VGI data can be comprehensively used to extract flood depth and hydrological models could also be used to further improve the flood situation awareness and prediction ability.更多还原