地震地质 ›› 2021, Vol. 43 ›› Issue (4): 1013-1029.DOI: 10.3969/j.issn.0253-4967.2021.04.018

• 研究论文 • 上一篇    下一篇

基于改进蚁群算法与无人机影像的震害识别方法及其在漾濞地震中的应用

杜浩国1)(), 林旭川2),*(), 张建国1), 杜浩标3), 张方浩1), 杜竹泉4), 卢永坤1), 代博洋5)   

  1. 1)云南省地震局, 昆明 650224
    2)中国地震局工程力学研究所, 哈尔滨 150080
    3)中国人民解放军31663部队, 昆明 650224
    4)迪庆州藏文中学, 迪庆 674400
    5)中国地震应急搜救中心, 北京 100049
  • 收稿日期:2021-05-31 修回日期:2021-06-20 出版日期:2021-08-20 发布日期:2021-09-29
  • 通讯作者: 林旭川
  • 作者简介:杜浩国, 男, 1991年生, 2015年于东北大学获通信工程专业学士学位, 主要从事地震应急救援、 地震灾害损失评估研究, E-mail: 1364125834@qq.com
  • 基金资助:
    国家重点研发计划项目 “地震应急全时程灾情汇聚与决策服务技术研究”(2018YFC1504505);国家自然科学基金(U1939210);云南省地震局 “传帮带” 项目(CQ3-2021001)

A SEISMIC DAMAGE IDENTIFICATION METHOD BASED ON IMPROVED ANT COLONY ALGORITHM AND UNMANNED AERIAL VEHICLE IMAGES AND ITS APPLICATION TO YANGBI EARTHQUAKE

DU Hao-guo1)(), LIN Xu-chuan2),*(), ZHANG Jian-guo1), DU Hao-biao3), ZHANG Fang-hao1), DU Zhu-quan4), LU Yong-kun1), DAI Bo-yang5)   

  1. 1) Yunnan Earthquake Agency, Kunming 650224, China
    2) Institute of Engineering Mechanics, CEA, Harbin 150080, China
    3) No. 31663 Unit of PLA, Kunming 650224, China
    4) The Tibetan Middle School of Diqing Tibetan Autonomous Prefecture, Diqing 674400, China
    5) National Earthquake Response Support Service, Beijing 100049, China
  • Received:2021-05-31 Revised:2021-06-20 Online:2021-08-20 Published:2021-09-29
  • Contact: LIN Xu-chuan

摘要:

地震后及时、 精准地获取区域的震害情况, 对科学有效地开展应急救援与灾害损失评估工作具有重要意义。 文中依托蚁群算法以及无人机高清遥感影像, 提出了一种高效识别区域建筑震害的新方法, 并在近期云南漾濞6.4级地震的应急工作中进行了应用与验证。 该方法通过改进蚁群算法中信息素浓度更新策略, 引入优化算子, 建立了更好的识别规则, 提高了震害识别的速度与准确性; 云南漾濞6.4级地震发生后, 以第一时间获取的漾濞县城无人机高分辨率影像为试验数据, 对区域震害的提取效果进行了验证, 并与蚁群算法及最大似然方法进行对比分析。 结果表明, 文中提出的基于改进蚁群算法与无人机高分辨率影像的震害识别方法可有效提高区域内被破坏建筑物的识别精度。

关键词: 漾濞6.4级地震, 无人机遥感影像, 震害识别, 蚁群算法

Abstract:

Earthquake is one of the most destructive natural disasters, it can not only cause heavy casualties and economic losses, but also may even lead to serious secondary disasters. As the main bearing body in earthquake, buildings often suffer serious damage, so they can be used as an important reference for post-earthquake disaster loss assessment. Timely and accurate acquisition of regional earthquake damage information after an earthquake is of great significance for scientific and effective emergency rescue and disaster loss assessment. At present, the main methods for earthquake damage identification can be roughly divided into two categories: 1) Manual visual interpretation investigation method. It takes a lot of time for manual field investigation or manual identification of earthquake damage images to process a large amount of seismic damage information in a short period of time, and it is likely to lead to inconsistent discrimination standards for seismic damage of buildings. 2)Image recognition method based on satellite image or UAV image. The recognition method based on satellite remote sensing image after the quake identifies earthquake damage by the texture, brightness and other characteristics of the image of the seriously collapsed buildings, thus, it can quickly get the seismic damage situation in a large area, but as results of offset, low resolution and poor timeliness of the satellite image, it is hard to identify the slightly overlaying and cracking of tiles on the roof of buildings. The combination of high-resolution image obtained by UAV and machine learning algorithm can not only reduce the labor input, but also bring a high accuracy rate. Therefore, based on ant colony algorithm(ACO)and high-resolution remote sensing image of UAV, this paper proposes a new method to efficiently identify the earthquake damage of buildings in the study area, which was applied and verified in the recent Yangbi M6.4 earthquake in Yunnan Province. By improving the update strategy of pheromone concentration in ant colony algorithm and introducing the optimization operator, the better identification rules are established, and the speed and accuracy of earthquake damage identification are enhanced. The UAV high-resolution image of Yangbi county seat was obtained the first time after the Yangbi, Yunnan Province, M6.4 earthquake took place, and taking the image as experimental data, the extraction effect of regional earthquake damage is verified, and compared with ant colony algorithm and maximum likelihood method. The results show that the proposed earthquake damage identification method based on improved ant colony algorithm and UAV high-resolution image can effectively improve the identification accuracy and efficiency of damaged buildings in the region, which is of great significance for post-earthquake emergency rescue and providing accurate disaster information.

Key words: Yangbi M6.4 earthquake, UAV remote sensing image, damage identification, ant colony algorithm

中图分类号: