重大公路灾害遥感监测与评估技术研究
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摘要
道路交通系统是分布区域很广的网状系统,极易遭受自然灾害的破坏,重大公路灾害不仅直接造成巨大的经济损失,还因交通阻断造成救援延误引起更为惨重的继发损失。在发生重大灾害的条件下,以不同平台的遥感数据为基础,不但可以获得灾害的范围、严重程度等信息,进一步的分析还可获得大范围路网中道路堵塞位置与程度、道路损毁位置与程度等信息,为灾后的道路抢修、应急交通组织和救援资源调度决策提供可靠依据。遥感测量是信息技术高度发展的产物,特别适用于对大范围环境的动态变化进行快速综合监测,已成功用于地震、火山、洪水、山体滑坡、冰雪、森林火灾、飓风等灾害的监测。但运用遥感数据对大范围路网中的树木、岩石、车辆、泥石流、冰雪、烟尘、洪水、倒坍建筑物等各类道路堵塞要素以及路基沉降、路基坍塌、路面断裂、桥梁断裂等道路损毁现象进行快速识别,在世界范围内都处于起步阶段,具有较大的研发难度。
本文深入研究了重大公路灾害监测的几项关键技术,包括道路范围检测、多源数据配准、公路损毁信息检测。具体的研究工作可以分为如下几个部分：
(1)本文对在灾害应急状况下,可被用来快速评估公路损毁情况的数据源进行了总结和分析。针对不同数据组合推导出几套可行的公路损毁检测方案,并分析了各种方案所能够提供的损毁信息内容和准度。
(2)本文首先分析了高空间分辨率遥感影像中公路的理想和非理想模型特征,在此基础上讨论了一种基于MonteCalo跟踪方法和改进Snake边缘精确定位结合,检测高空间分辨率遥感影像中公路的方法。这种方法在实际应用中遇到公路边缘模糊情况时,检测效果不好。于是提出了基于公路几何特征约束的道路双边缘检测方法。该方法中引用了两个约束条件：公路不会出现急转弯(约束条件1)；公路的宽度不会发生突变(约束条件2)。公路几何约束条件制定的依据为中国交通运输部公布的公路设计标准规范,该方法能够解决绝大多数公路范围检测。对于少量违反约束条件的公路,也提出了可行的检测方案。
(3)本文针对山区LiDAR数据与光学影像配准问题,尝试了最小二乘法匹配和曲线特征匹配法,并通过实验比较发现后者是一种更可靠的方法。该配准方法采用w-d模型来描述曲线,并通过w-d模型匹配找到同名曲线,接下来通过曲线节点之间的相似关系构建代价矩阵,用动态规划方法确定同名曲线的节点之间的对应关系。
(4)本文利用LiDAR数据来分析公路损毁信息。首先,通过对现有滤波算法的分类比较分析,以及ISPRS网站上提供的各种经典滤波算法处理几组同样实验数据的误差比较分析,得出使用Li的形态学滤波算法最使用于本文的结论,并改进该滤波算法,使其在滤除干扰信息的同时保留了桥梁点。接下来,将滤波后的点云构造的DSM用LBP和VAR纹理测度处理,突出表达起伏和平坦地形,并用面向对象的多尺度分割技术和模糊C均值分类算法被用来区分损毁和完好区域。最后根据损毁区域内每个微分体元的体积的变化积分,精确计算出路段损毁程度(滑坡堆积物或垮塌方量),并通过几种损毁模型,分析出损毁类型(掩埋类损毁、桥梁垮塌损毁、路基塌陷损毁)。
Road network traffic system is extremely easy destructed by the natural disaster. Road disaster not only brings a huge economic loss to disaster areas, but also some more serious loss result from rescue delay. After significant disaster occured, the multisource data is collected by different remote sensing platforms, it will be the foundation of disaster detection. Based on these multisource remote sensing data, several road damage information can be acquired, such as road damaged scope, road damaged degree and road damaged types information etc.. These kind of damage information will help for post-disaster road emergency repair, the emergency transportation organization and decision-making of rescue material schedule. The remote sensing survey is the product of information technology develops highly, it is very suitable in the fast and synthesis monitor of dynamic change of the wide range environment. Now, the technology has been succeeded used for some disaster monitor, such as earthquake, volcano, flood, landslide, snow and ice, forest-fire, hurricane and so on. But, it is difficult that remote sensing data is used to identify roadbed subsidence, roadbed collapse, the bridge break from the wide range road network and several kinds of block factors of road sections. In the world wide, the research has just been begin. This paper researched deeply three key technologies of significant road disaster monitor, including road scope detection, multisource data match and road damage information detection. The concrete research work may be divided into the following several parts:
(1) Some remote sensing data sources can be used to evaluate road damage situation under disaster emergency condition. In the light of the different data combination, several feasible road damage detection schemes were deduced, and damage information content and precision of every kind of scheme were analyzed.
(2) This paper analyzed road's ideal and non-ideal model characteristics in some high spatial resolution remote sensing images. Based on the model characteristics, a method that is combine arithmetic of MonteCalo particle filtering and arithmetic of improved Snake edge location is put forward. But when the road edge blurry, the detection result of the method is not good. In order to overcome the deficiency, a method of double edge tracking based on the road geometry characteristic restraint was proposed. Two restraint conditions were introduced into the method: The road can not present a extreme turn (constraint 1); Road's width can not have the sudden change (constraint 2). This method can solve the overwhelming majority road scope detection problems, for few road sections which violates the constraint conditions, a feasible detection plan was also proposed.
(3) This paper is use for the least squares method and curve characteristics method match LiDAR data and optical image of mountainous area, and the later one is more reliable that is proved by experiment comparison and analysis. The curve match method is described as follows:firstly, it take use of w-d model describe a curve; And then it is use for the method of w-d model matching to find homonymous curves, Next it build price matrix by the similar relations of homonymous curve nodes. lastly, it confirm the corresponding relationships of homonymous curve nodes by the dynamic planning method.
(4) This paper used the LiDAR data to analyze the road damage information. Firstly, a conclusion that the Li's morphology filtering algorithm is the most suitable for dealing with a mountainous area LiDAR data is obtained by analyze. And, a improved method based on Li's was proposed which can retain the bridge points during filter disturbance information. Next, a DSM is structured by point clouds. The LBP and VAR texture measures were used for analyzing DSM, whereupon the smooth and rough terrain can be distinct shown. And then the object-oriented multi-criterion division technology and the fuzzy C average value classify algorithm is taken use for differentiating the damage and the complete region. At last, according to volume change of all differential vexel in damage region, the road section damage degree (Landslide quantity or collapse quantity) can be precisely calculated, in addition according to several kind of damage models, the road section damage type (the burying damage, the bridge collapsing damage, the roadbed collapse damage) can be obtained.
本文深入研究了重大公路灾害监测的几项关键技术,包括道路范围检测、多源数据配准、公路损毁信息检测。具体的研究工作可以分为如下几个部分：
(1)本文对在灾害应急状况下,可被用来快速评估公路损毁情况的数据源进行了总结和分析。针对不同数据组合推导出几套可行的公路损毁检测方案,并分析了各种方案所能够提供的损毁信息内容和准度。
(2)本文首先分析了高空间分辨率遥感影像中公路的理想和非理想模型特征,在此基础上讨论了一种基于MonteCalo跟踪方法和改进Snake边缘精确定位结合,检测高空间分辨率遥感影像中公路的方法。这种方法在实际应用中遇到公路边缘模糊情况时,检测效果不好。于是提出了基于公路几何特征约束的道路双边缘检测方法。该方法中引用了两个约束条件：公路不会出现急转弯(约束条件1)；公路的宽度不会发生突变(约束条件2)。公路几何约束条件制定的依据为中国交通运输部公布的公路设计标准规范,该方法能够解决绝大多数公路范围检测。对于少量违反约束条件的公路,也提出了可行的检测方案。
(3)本文针对山区LiDAR数据与光学影像配准问题,尝试了最小二乘法匹配和曲线特征匹配法,并通过实验比较发现后者是一种更可靠的方法。该配准方法采用w-d模型来描述曲线,并通过w-d模型匹配找到同名曲线,接下来通过曲线节点之间的相似关系构建代价矩阵,用动态规划方法确定同名曲线的节点之间的对应关系。
(4)本文利用LiDAR数据来分析公路损毁信息。首先,通过对现有滤波算法的分类比较分析,以及ISPRS网站上提供的各种经典滤波算法处理几组同样实验数据的误差比较分析,得出使用Li的形态学滤波算法最使用于本文的结论,并改进该滤波算法,使其在滤除干扰信息的同时保留了桥梁点。接下来,将滤波后的点云构造的DSM用LBP和VAR纹理测度处理,突出表达起伏和平坦地形,并用面向对象的多尺度分割技术和模糊C均值分类算法被用来区分损毁和完好区域。最后根据损毁区域内每个微分体元的体积的变化积分,精确计算出路段损毁程度(滑坡堆积物或垮塌方量),并通过几种损毁模型,分析出损毁类型(掩埋类损毁、桥梁垮塌损毁、路基塌陷损毁)。
Road network traffic system is extremely easy destructed by the natural disaster. Road disaster not only brings a huge economic loss to disaster areas, but also some more serious loss result from rescue delay. After significant disaster occured, the multisource data is collected by different remote sensing platforms, it will be the foundation of disaster detection. Based on these multisource remote sensing data, several road damage information can be acquired, such as road damaged scope, road damaged degree and road damaged types information etc.. These kind of damage information will help for post-disaster road emergency repair, the emergency transportation organization and decision-making of rescue material schedule. The remote sensing survey is the product of information technology develops highly, it is very suitable in the fast and synthesis monitor of dynamic change of the wide range environment. Now, the technology has been succeeded used for some disaster monitor, such as earthquake, volcano, flood, landslide, snow and ice, forest-fire, hurricane and so on. But, it is difficult that remote sensing data is used to identify roadbed subsidence, roadbed collapse, the bridge break from the wide range road network and several kinds of block factors of road sections. In the world wide, the research has just been begin. This paper researched deeply three key technologies of significant road disaster monitor, including road scope detection, multisource data match and road damage information detection. The concrete research work may be divided into the following several parts:
(1) Some remote sensing data sources can be used to evaluate road damage situation under disaster emergency condition. In the light of the different data combination, several feasible road damage detection schemes were deduced, and damage information content and precision of every kind of scheme were analyzed.
(2) This paper analyzed road's ideal and non-ideal model characteristics in some high spatial resolution remote sensing images. Based on the model characteristics, a method that is combine arithmetic of MonteCalo particle filtering and arithmetic of improved Snake edge location is put forward. But when the road edge blurry, the detection result of the method is not good. In order to overcome the deficiency, a method of double edge tracking based on the road geometry characteristic restraint was proposed. Two restraint conditions were introduced into the method: The road can not present a extreme turn (constraint 1); Road's width can not have the sudden change (constraint 2). This method can solve the overwhelming majority road scope detection problems, for few road sections which violates the constraint conditions, a feasible detection plan was also proposed.
(3) This paper is use for the least squares method and curve characteristics method match LiDAR data and optical image of mountainous area, and the later one is more reliable that is proved by experiment comparison and analysis. The curve match method is described as follows:firstly, it take use of w-d model describe a curve; And then it is use for the method of w-d model matching to find homonymous curves, Next it build price matrix by the similar relations of homonymous curve nodes. lastly, it confirm the corresponding relationships of homonymous curve nodes by the dynamic planning method.
(4) This paper used the LiDAR data to analyze the road damage information. Firstly, a conclusion that the Li's morphology filtering algorithm is the most suitable for dealing with a mountainous area LiDAR data is obtained by analyze. And, a improved method based on Li's was proposed which can retain the bridge points during filter disturbance information. Next, a DSM is structured by point clouds. The LBP and VAR texture measures were used for analyzing DSM, whereupon the smooth and rough terrain can be distinct shown. And then the object-oriented multi-criterion division technology and the fuzzy C average value classify algorithm is taken use for differentiating the damage and the complete region. At last, according to volume change of all differential vexel in damage region, the road section damage degree (Landslide quantity or collapse quantity) can be precisely calculated, in addition according to several kind of damage models, the road section damage type (the burying damage, the bridge collapsing damage, the roadbed collapse damage) can be obtained.
引文
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