Road blockage information extraction from a single-phase postdisaster image is difficult because roads are narrow and easily covered by vegetation. The traditional object-oriented image analysis method is restrictive, and its detection is slow. A deep learning algorithm, i.e., the convolution neural network (CNN), is applied to rapidly extract road blockage information. An algorithm for sample generation is designed to construct a typical sample library for CNN training, and an appropriate CNN structure and a complete detection process are designed to extract road blockage information. Finally, by taking the Jiuzhaigou earthquake on August 8, 2017, as an example, experimental verification is carried out. The kappa coefficient and the F1 score of the results are 77.60% and 87.95%, respectively. The extraction of road blockages can be completed with an efficiency of 14.59 km² per hour. The requirements for disaster emergency monitoring can be met by the accuracy and efficiency of this method, which are better than those of the traditional object-oriented method.

中文翻译：

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基于卷积神经网络和超高分辨率卫星图像的九寨沟地震路障信息提取

由于道路狭窄且容易被植被覆盖，因此难以从单相灾后图像中提取道路障碍信息。传统的面向对象图像分析方法具有局限性，并且检测速度较慢。深度学习算法，即卷积神经网络（CNN），用于快速提取道路阻塞信息。设计了用于样本生成的算法，以构建用于CNN训练的典型样本库，并设计了适当的CNN结构和完整的检测过程来提取路障信息。最后，以2017年8月8日九寨沟地震为例，进行了实验验证。结果的卡伯系数和F1分数分别为77.60％和87.95％。每小时²个。该方法的准确性和效率可以满足灾害应急监测的要求，优于传统的面向对象方法。