Region-based automatic mapping of tsunami-damaged buildings using multi-temporal aerial images
Region-based automatic mapping of tsunami-damaged buildings using multi-temporal aerial images
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DOI：
10.1007/s11069-014-1498-4
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发表时间：
2015-03
期刊：
影响因子：
3.7
通讯作者：
J. Susaki
中科院分区：
文献类型：
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作者：
J. Susaki
灾害发生后，迅速分发信息对于国家或地方政府规划灾害应对和恢复措施至关重要。在发生海啸的情况下，需要有关被海浪摧毁的建筑物的信息。在这里，我们提出了一种方法，确定个别受损的建筑物，通过使用获得的航空图像前和海啸后。该方法利用建筑物区域的显著高度变化来评估损坏。立体航空图像用于生成该区域的数字表面模型（DSM）。我们假设两种情况：如果地理信息系统（GIS）数据（建筑物区域数据）可用，则使用它们;如果GIS数据不可用，则使用分段结果和过滤DSM。在每种情况下，在海啸前图像中识别与建筑物相对应的区域。然后通过考虑灾前和灾后图像之间的建筑物区域内的高度变化来提取受损区域。由地震引起的陆地变形导致的水平位移通过诸如尺度不变特征变换的现有算法自动估计（Lowe in Int J Comput维斯，60（2）：91-110，2004）。验证结果表明，该方法提取受损建筑物的准确性高（94- 96%的数量和96- 98%的面积）时，GIS数据是可用的，与较低的准确性（69- 79%的面积）时，GIS数据不可用。此外，我们发现，应考虑灾前和灾后的水平位移，以提取受损建筑物。我们的结论是，我们的方法可以自动生成有效的地图，不仅被海啸破坏的建筑物，但也由其他灾害。
After a disaster, prompt distribution of information is critical for national or local governments to plan the disaster response and recovery measures. In case of a tsunami, information about buildings destroyed by the waves is required. Here, we present a method that identifies individual damaged buildings by using aerial images obtained pre- and post-tsunami. The method utilizes significant height changes in building regions to assess the damage. Stereo aerial images are used to generate a digital surface model (DSM) of the area. We assume two cases: if geographic information system (GIS) data (building region data) are available, we use them and if GIS data are unavailable, we instead use segmented results and a filtered DSM. In each case, regions corresponding to buildings are identified in the pre-tsunami image. Damaged regions are then extracted by considering the height change within a building region between the pre- and post-disaster images. Horizontal shifts resulting from land deformation caused by the earthquake are automatically estimated by an existing algorithm such as scale-invariant feature transform (Lowe in Int J Comput Vis, 60(2):91–110, 2004). Validation showed that the proposed method extracted damaged buildings with high accuracy (94–96 % in number and 96–98 % in area) when GIS data are available and with lower accuracy (69–79 % in area) when GIS data are unavailable. In addition, we found that horizontal shifts between pre- and post-disaster should be considered to extract the damaged buildings. We conclude that our method can automatically generate effective maps of buildings damaged not only by tsunamis but also by other disasters.