Near Real-Time Flood Detection in Urban and Rural Areas Using High-Resolution Synthetic Aperture Radar Images
Near Real-Time Flood Detection in Urban and Rural Areas Using High-Resolution Synthetic Aperture Radar Images
复制标题
DOI：
10.1109/tgrs.2011.2178030
复制
发表时间：
2012-08-01
影响因子：
8.2
通讯作者：
Bates, Paul D.
中科院分区：
文献类型：
--
作者：
Mason, David C.;Davenport, Ian J.;Bates, Paul D.
一个近实时的洪水检测算法，提供了一个天气概况的洪水在城市和农村地区的程度，并能够在夜间和白天工作，即使有云，可能是一个有用的工具，业务洪水救济管理。本文介绍了一种自动算法，使用高分辨率合成孔径雷达（SAR）卫星数据，建立在现有的方法，包括使用图像分割技术之前，对象分类，以科普这些场景中的大量像素。城市地区的洪水探测是以邻近农村地区的洪水范围为指导的。该算法假设，高分辨率的地形高度数据是可用于至少在城市地区的场景，以便SAR模拟器可用于估计雷达阴影和停留区。该算法被证明能够检测洪水在农村地区使用TerraSAR-X具有良好的准确性，正确分类89%的淹没像素，与相关的假阳性率为6%。在TerraSAR-X可见的城市水体像素中，75%被正确检测到，假阳性率为24%。如果考虑所有城市水像素，包括阴影和停留区，这些数字分别下降到57%和18%。
A near real-time flood detection algorithm giving a synoptic overview of the extent of flooding in both urban and rural areas, and capable of working during night-time and day-time even if cloud was present, could be a useful tool for operational flood relief management. The paper describes an automatic algorithm using high-resolution synthetic aperture radar (SAR) satellite data that builds on existing approaches, including the use of image segmentation techniques prior to object classification to cope with the very large number of pixels in these scenes. Flood detection in urban areas is guided by the flood extent derived in adjacent rural areas. The algorithm assumes that high-resolution topographic height data are available for at least the urban areas of the scene, in order that a SAR simulator may be used to estimate areas of radar shadow and layover. The algorithm proved capable of detecting flooding in rural areas using TerraSAR-X with good accuracy, classifying 89% of flooded pixels correctly, with an associated false positive rate of 6%. Of the urban water pixels visible to TerraSAR-X, 75% were correctly detected, with a false positive rate of 24%. If all urban water pixels were considered, including those in shadow and layover regions, these figures fell to 57% and 18%, respectively.