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地震 ›› 2013, Vol. 33 ›› Issue (2): 71-78.

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基于边线的遥感影像道路震害快速提取方法研究

马海建1, 陆楠2, 李晓璇1   

  1. 1.地壳运动监测工程研究中心, 北京 100036;
    2.北京大学遥感与地理信息系统研究所, 北京 100871
  • 收稿日期:2013-01-03 出版日期:2013-04-30 发布日期:2020-09-27
  • 作者简介:马海建(1984-), 男, 江苏宿迁人, 博士, 主要从事遥感信息提取,以及遥感与GIS的地震灾害应用研究。
  • 基金资助:
    地震科技星火计划项目(NO.XH1036)和高分辨率对地观测系统重大专项(NO. E0311/1112/JS06)

Rapid Extraction of Earthquake Road Damages Based on Sidelines from Remote Sensing Images

MA Hai-jan1, LU Nan2, LI Xiao-xuan1   

  1. 1. National Earthquake Infrastructure Service, Beijing 100036, China;
    2. Institute of Remote Sensing and GIS,Peking University, Beijing 100871, China
  • Received:2013-01-03 Online:2013-04-30 Published:2020-09-27

摘要: 重大地震灾害发生以后, 快速准确地提取灾区的道路震害信息对于应急救援工作具有重要意义。 由于地震造成的破坏非常复杂, 没有固定的光谱特征变化模式, 而且影像中存在大量同物异谱和同谱异物现象。 因此, 传统基于光谱特征的遥感影像道路震害提取方法, 不仅提取精度较低, 而且通用性不强, 需要根据不同影像调整参数。 道路边线是一种稳定的道路几何特征, 其特征变化能够准确地表现道路的状态变化。 基于此, 本文研究了一种基于道路边线的震害信息快速提取方法。 该方法利用震后遥感影像提取完好道路边线, 与震前道路线分布数据进行变化检测, 从而实现震害路段的提取。 最后, 利用汶川震区的遥感影像进行实例验证, 与人工解译的结果进行比较。 结果表明, 该方法能够快速准确地提取道路震害信息。

关键词: 道路震害, 信息提取, 遥感, 汶川地震

Abstract: Roads are the traffic lifeline for emergency rescue and disaster relief. After huge earthquakes, it is very important to rapidly extract road damages from remote sensing images for disaster emergency rescue. However, because damages caused by earthquakes is different at different places, it is very complicated to extract in a common way. Usually, spectral features are used to discriminate damaged and undamaged pixels or objects in images. Because thresholds have to be chosen by manual operation dependent on different situations, methods based on spectral features are low in accuracy and efficiency. This paper proposes an approach to rapidly extract road damages by earthquake based on sidelines. The approach extracts double sidelines of undamaged road from images acquired after earthquakes, and then compare them with vector road lines before earthquakes. The damaged segments of roads can be extracted through comparison. The performance of the method is evaluated by an experiment with QuickBird images in the disaster area of the 2008 Wenchuan earthquake.

Key words: Sidelines, Road Damage, Extraction, Remote Sensing

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