A Framework of Rapid Regional Tsunami Damage Recognition From Post-event TerraSAR-X Imagery Using Deep Neural Networks
A Framework of Rapid Regional Tsunami Damage Recognition From Post-event TerraSAR-X Imagery Using Deep Neural Networks
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DOI：
10.1109/lgrs.2017.2772349
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发表时间：
2018-01-01
影响因子：
4.8
通讯作者：
Koshimura, Shunichi
中科院分区：
文献类型：
--
作者：
Bai, Yanbing;Gao, Chang;Koshimura, Shunichi
近实时的建筑物损坏地图是政府做出救灾决策的必要前提。有了TerraSAR-X等高分辨率合成孔径雷达系统，就有可能快速有效地提供此类产品。在这封信中，提出了一个基于深度学习的框架，用于使用事后SAR图像进行快速区域海啸损害识别。为了执行这样的快速损伤映射，采用一系列基于瓦片的图像分裂分析来生成数据集。接下来，开发了一种使用SqueezeNet网络的选择算法，以快速区分建筑（BU）和非建筑区域。最后，开发了一种改进的宽残差网络识别算法，将BU区域分类为冲刷区域、塌陷区域和轻微损坏区域。在2011年日本东北地震和海啸的TerraSAR-X数据上进行的实验表明，BU区域提取准确率为80.4%，损伤级别识别准确率为74.8%。我们的框架在一个新的区域上训练大约需要2小时，而预测只需几分钟。
Near real-time building damage mapping is an indispensable prerequisite for governments to make decisions for disaster relief. With high-resolution synthetic aperture radar (SAR) systems, such as TerraSAR-X, the provision of such products in a fast and effective way becomes possible. In this letter, a deep learning-based framework for rapid regional tsunami damage recognition using post-event SAR imagery is proposed. To perform such a rapid damage mapping, a series of tile-based image split analysis is employed to generate the data set. Next, a selection algorithm with the SqueezeNet network is developed to swiftly distinguish between built-up (BU) and nonbuilt-up regions. Finally, a recognition algorithm with a modified wide residual network is developed to classify the BU regions into wash away, collapsed, and slightly damaged regions. Experiments performed on the TerraSAR-X data from the 2011 Tohoku earthquake and tsunami in Japan show a BU region extraction accuracy of 80.4% and a damage-level recognition accuracy of 74.8%, respectively. Our framework takes around 2 h to train on a new region, and only several minutes for prediction.