Urban Flood Detection with Sentinel-1 Multi-Temporal Synthetic Aperture Radar (SAR) Observations in a Bayesian Framework: A Case Study for Hurricane Matthew
Urban Flood Detection with Sentinel-1 Multi-Temporal Synthetic Aperture Radar (SAR) Observations in a Bayesian Framework: A Case Study for Hurricane Matthew
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在贝叶斯框架中使用 Sentinel-1 多时相合成孔径雷达 (SAR) 观测进行城市洪水检测：飓风马修的案例研究
DOI：
10.3390/rs11151778
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
E. Hill
中科院分区：
文献类型：
--
作者：
Yunung Nina Lin;S. Yun;A. Bhardwaj;E. Hill
在本研究中，我们探索了合成孔径雷达（SAR）强度时间序列在城市洪水检测中的应用。我们的测试案例是 2016 年 10 月 8 日马修飓风登陆美国北卡罗来纳州兰伯顿引发的洪水，其中与 SAR 立交桥同一天拍摄的机载图像可用于验证我们的技术。为了绘制洪水图，我们首先根据时间序列的统计数据对 SAR 强度观测值进行归一化，然后分别针对强度降低（由于信号的镜面反射）和强度增加（由于双反射）情况构建贝叶斯概率函数。然后，我们形成了一个洪水概率图，并用它来使用 0.5 的全局截止概率来创建我们首选的洪水范围图。我们在市区的洪水地图显示了复杂的镶嵌图案，其中像素显示 SAR 强度下降，像素显示强度增加，像素没有显着强度变化。与对数强度比的全局阈值相比，我们的方法显示出改进的性能，因为基于时间序列的归一化通过考虑每个像素的不同历史记录来解释一定程度的空间变化。这提高了城市和植被区域的性能。我们将沥青路等光滑表面和 SAR 阴影确定为预测不足的主要来源，而水生植物和土壤湿度变化是预测过高的主要来源。
In this study we explored the application of synthetic aperture radar (SAR) intensity time series for urban flood detection. Our test case was the flood in Lumberton, North Carolina, USA, caused by the landfall of Hurricane Matthew on 8 October 2016, for which airborne imagery—taken on the same day as the SAR overpass—is available for validation of our technique. To map the flood, we first carried out normalization of the SAR intensity observations, based on the statistics from the time series, and then construct a Bayesian probability function for intensity decrease (due to specular reflection of the signal) and intensity increase (due to double bounce) cases separately. We then formed a flood probability map, which we used to create our preferred flood extent map using a global cutoff probability of 0.5. Our flood map in the urban area showed a complicated mosaicking pattern of pixels showing SAR intensity decrease, pixels showing intensity increase, and pixels without significant intensity changes. Our approach shows improved performance when compared with global thresholding on log intensity ratios, as the time series-based normalization has accounted for a certain level of spatial variation by considering the different history for each pixel. This resulted in improved performance for urban and vegetated regions. We identified smooth surfaces, like asphalt roads, and SAR shadows as the major sources of underprediction, and aquatic plants and soil moisture changes were the major sources of overprediction.
DOI：
10.1109/tgrs.2011.2178030
发表时间：
2012-08-01
影响因子：
8.2
作者：
Mason, David C.;Davenport, Ian J.;Bates, Paul D.
通讯作者：
Bates, Paul D.