Earthquake Damage Assessment of Buildings Using VHR Optical and SAR Imagery
Earthquake Damage Assessment of Buildings Using VHR Optical and SAR Imagery
复制标题
DOI：
10.1109/tgrs.2009.2038274
复制
发表时间：
2010-05-01
影响因子：
8.2
通讯作者：
Bruzzone, Lorenzo
中科院分区：
文献类型：
--
作者：
Brunner, Dominik;Lemoine, Guido;Bruzzone, Lorenzo
在自然灾害（例如地震）和暴力冲突（例如与战争有关的破坏）发生后迅速评估损害，对于发起有效的应急行动至关重要。配备非常高空间分辨率（VHR）多光谱和合成孔径雷达（SAR）成像传感器的遥感卫星可以提供重要信息，因为它们能够以高几何精度和不受干扰的方式绘制受影响地区的地图。在本文中，我们提出了一种利用地震前VHR光学图像和地震后VHR SAR图像检测地震中被毁建筑物的新方法。该方法在单个建筑的层面上运行，并假设它们有一个矩形的足迹并且是隔离的。首先，利用事件前光学图像估计建筑物的三维参数；其次，利用VHR SAR场景的建筑物信息和采集参数，在假设建筑物不受事件影响的情况下，预测事件后SAR场景中建筑物的期望特征；第三，分析了预测图像与实际SAR图像的相似度。如果相似度高，则建筑物可能仍然完好无损，而相似度低则表明建筑物已被摧毁。使用相似阈值对建筑物进行分类。我们以2008年5月12日四川大地震中受灾严重的映秀镇为例，验证了该方法的可行性和有效性。在实验中，我们使用QuickBird和WorldView-1光学图像，以及TerraSAR-X和cosmos - skymed SAR数据。
Rapid damage assessment after natural disasters (e. g., earthquakes) and violent conflicts (e. g., war-related destruction) is crucial for initiating effective emergency response actions. Remote-sensing satellites equipped with very high spatial resolution (VHR) multispectral and synthetic aperture radar (SAR) imaging sensors can provide vital information due to their ability to map the affected areas with high geometric precision and in an uncensored manner. In this paper, we present a novel method that detects buildings destroyed in an earthquake using pre-event VHR optical and post-event detected VHR SAR imagery. The method operates at the level of individual buildings and assumes that they have a rectangular footprint and are isolated. First, the 3-D parameters of a building are estimated from the pre-event optical imagery. Second, the building information and the acquisition parameters of the VHR SAR scene are used to predict the expected signature of the building in the post-event SAR scene assuming that it is not affected by the event. Third, the similarity between the predicted image and the actual SAR image is analyzed. If the similarity is high, the building is likely to be still intact, whereas a low similarity indicates that the building is destroyed. A similarity threshold is used to classify the buildings. We demonstrate the feasibility and the effectiveness of the method for a subset of the town of Yingxiu, China, which was heavily damaged in the Sichuan earthquake of May 12, 2008. For the experiment, we use QuickBird and WorldView-1 optical imagery, and TerraSAR-X and COSMO-SkyMed SAR data.