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

doi:10.3969/j.issn.0253-4967.2021.04.018

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

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)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) 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/Abstract

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:

P315.9

杜浩国, 林旭川, 张建国, 杜浩标, 张方浩, 杜竹泉, 卢永坤, 代博洋. 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[J]. 地震地质, 2021, 43(4): 1013-1029.

DU Hao-guo, LIN Xu-chuan, ZHANG Jian-guo, DU Hao-biao, ZHANG Fang-hao, DU Zhu-quan, LU Yong-kun, DAI Bo-yang. A SEISMIC DAMAGE IDENTIFICATION METHOD BASED ON IMPROVED ANT COLONY ALGORITHM AND UNMANNED AERIAL VEHICLE IMAGES AND ITS APPLICATION TO YANGBI EARTHQUAKE[J]. SEISMOLOGY AND EGOLOGY, 2021, 43(4): 1013-1029.

图/表 14

图1 基于改进蚁群算法的高分辨率影像震害识别流程图

Fig. 1 The flowchart of earthquake damage identification based on improved ant colony algorithm.

图2 识别规则对应的路径

Fig. 2 Route corresponding to classification rule.

图3 a 漾濞县城无人机影像图; b 漾濞县城数字表面模型

Fig. 3 The UAV image(a) and the DSM(b) of Yangbi county seat.

表1 多光谱影像采集设备参数

Table1 Multispectral image acquisition equipment parameters

飞行器性能	技术参数	飞行器性能	技术参数
型号	精灵4多光谱版	航拍架次	4架次
总重量	1 487g	航向重叠度	70%
飞行升限	200m	旁向重叠图	65%
续航时间	≤27min	成像总面积	2km²
悬停精度	垂直方向±0.1m 水平方向±0.1m	照片格式	JPEG(可见光成像) TIFF(多光谱成像)
倾斜角度	-88°	航拍照片数量	918张
飞行速度	≥20km/h	单色传感器增益	1~8倍
影像传感器	6个1/2.9英寸CMOS, 包括1个用于可见光成像的彩色传感器和5个用于多光谱成像的单色传感器	滤光片	蓝(B): (450±16)nm 绿(G): (560±16)nm 红(R): (650±16)nm 红边(RE): (730±16)nm 近红外(NIR): (840±26)nm

表2 训练样本和验证样本

Table2 The training data and test data samples

序号	震害等级	训练样本	验证样本
1	严重	217	404
2	中度	1 094	441
3	轻度	120	372
4	无破坏	250	831
合计		1 680	2 048

图4 部分训练样本的实地调研影像

Fig. 4 The field survey images of some training samples.

图5 a 建筑物轮廓检测结果; b 建筑物灰度化结果; c 建筑物损坏识别结果; d 建筑物震害等级识别结果

Fig. 5 Building contour test results(a), building grayscale results(b), building damage identification results(c), and identification result of building earthquake damage grade(d).

图6 训练样本的平均灰度值与建筑物数量统计

Fig. 6 The average grayscale value of training samples and building number statistics.

表3 改进的蚁群算法和蚁群算法挖掘的部分识别规则

Table3 Part of classification rules from improved ant colony algorithm and ant-miner algorithm

改进的蚁群算法		蚁群算法
规则1:	if 110<B₁ and B₂<120 and 100<B₃ then 严重破坏	规则1:	if 110<B₁<119 and 140<B₂<149 and 120<B₃<129 then 严重破坏
规则2:	if 110<B₁ and 120<B₃ then 中等破坏	规则2:	if 190<B₁<199 and 100<B₂<109 and 190<B₃<199 then 中等破坏
规则3:	if 140<B₁ and 130<B₂ and B₃<89 then 轻微破坏	规则3:	if 150<B₁<159 and 90<B₂<99 and 60<B₃<69 then 轻微破坏
规则19:	if 100<B₁ and 100<B₂<159 and 120<B₃ then 中等破坏	规则35:	if 180<B₁<189 and 150<B₂<99 and 60<B₃<150 then 中等破坏
规则20:	if 99<B₂<129 and B₃<129 then 严重破坏	规则36:	if 90<B₁<99 and 140<B₂<149 and 120<B₃<129 then 严重破坏

图7 a 改进的蚁群算法震害识别结果图; b 蚁群算法震害识别结果图; c 最大似然法震害识别结果图; d 目视解译结果

Fig. 7 Seismic damage identification results of improved ant colony algorithm(a), seismic damage identification results of ant colony algorithm(b), seismic damage identification results of maximum likelihood method(c), and visual interpretation results(d).

图8 a 改进的蚁群算法震害识别局部放大; b 蚁群算法震害识别局部放大; c 最大似然法震害识别局部放大; d 目视解译局部放大

Fig. 8 An improved ant colony algorithm for local amplification of seismic damage identification(a), ant colony algorithm for seismic damage identification local amplification(b), local amplification of earthquake damage identification by maximum likelihood method(c), and local magnification of visual interpretation(d).

表4 漾濞县城区基于改进的蚁群优化算法震害识别精度评价结果

Table4 Accuracy evaluation results of seismic damage identification of Yangbi county seat based on improved ant colony optimization algorithm

震害识别等级	严重破坏/栋	中等破坏/栋	轻微破坏/栋	无破坏/栋	总和	生产精度/%
严重破坏	353	15	8	18	394	89.59
中等破坏	13	384	21	21	439	87.47
轻微破坏	21	31	320	13	385	83.11
无破坏	17	11	23	779	830	93.85
总和	404	441	372	831	2 048
使用精度/%	87.37	87.07	86.02	93.74
总体精度	89.64%	Kappa系数	0.855

表5 漾濞县城区基于蚁群算法震害识别精度评价结果

Table5 Evaluation results of earthquake damage identification of Yangbi county seat based on ant colony algorithm

震害识别等级	严重破坏/栋	中等破坏/栋	轻微破坏/栋	无破坏/栋	总和	生产精度/%
严重破坏	352	20	8	24	404	87.13
中等破坏	15	370	23	19	427	86.65
轻微破坏	22	33	323	15	393	82.18
无破坏	15	18	18	773	824	93.81
总和	404	441	372	831	2 048
使用精度/%	87.12	83.9	86.82	93.02
总体精度	88.76%	Kappa系数	0.843

表6 漾濞县城区基于最大似然法震害识别精度评价结果

Table6 Accuracy evaluation results of earthquake damage identification of Yangbi county seat based on maximum likelihood method

震害识别等级	严重破坏/栋	中等破坏/栋	轻微破坏/栋	无破坏/栋	总和	生产精度/%
严重破坏	324	21	15	27	387	83.72
中等破坏	39	363	23	25	450	80.66
轻微破坏	24	33	315	35	407	77.39
无破坏	17	24	19	744	804	92.53
总和	404	441	372	831	2 048
使用精度/%	80.19	82.31	84.67	89.53
总体精度	85.25%	Kappa系数	0.795

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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

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

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