CN107578446A - A method and device for extracting roads from remote sensing images - Google Patents

A method and device for extracting roads from remote sensing images Download PDF

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CN107578446A
CN107578446A CN201710852459.8A CN201710852459A CN107578446A CN 107578446 A CN107578446 A CN 107578446A CN 201710852459 A CN201710852459 A CN 201710852459A CN 107578446 A CN107578446 A CN 107578446A
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road
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李润生
曹帆之
曹闻
万成浩
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PLA Information Engineering University
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Abstract

The present invention relates to a kind of method for extracting remote sensing image road and device, belongs to remote sensing information extractive technique field.The present invention calculates original remote sensing image road probability density first, and the roadway characteristic on original remote sensing image is converted to the feature of road probability density;Then according to feature construction road-center line model of the probable value on road axis higher than the probable value in other positions, and cost function is determined according to road-center line model;The maximum of cost function is finally solved using Dynamic Programming, road axis is used as using the maximum.Complicated and diversified roadway characteristic on original remote sensing image is converted to simple consistent roadway characteristic in road probability distribution graph by the present invention, enable to extract different types of high-resolution remote sensing image road, cost function need not be changed, improves the universality of method.

Description

一种遥感影像道路提取方法及装置A method and device for extracting roads from remote sensing images

技术领域technical field

本发明涉及一种遥感影像道路提取方法及装置,属于遥感信息提取技术领域。The invention relates to a remote sensing image road extraction method and device, belonging to the technical field of remote sensing information extraction.

背景技术Background technique

从高分辨率遥感影像上自动提取道路对地图更新、GIS数据获取、影像匹配和目标检测具有重要意义,是当前遥感测绘领域的研究重点。高分辨率遥感影像道路提取可分为全自动提取和半自动提取,全自动提取方法常见的主要有基于平行线对、基于数学形态学和知识、基于窗口模型特征等,但从目前研究进展来看,现有的全自动算法鲁棒性差,提取结果需要大量人工处理,效果不理想,采用人机交互方式的半自动提取是目前较为实际的选择。Automatically extracting roads from high-resolution remote sensing images is of great significance for map updating, GIS data acquisition, image matching and target detection, and is the current research focus in the field of remote sensing mapping. High-resolution remote sensing image road extraction can be divided into fully automatic extraction and semi-automatic extraction. The common automatic extraction methods are mainly based on parallel line pairs, based on mathematical morphology and knowledge, and based on window model features. , the existing fully automatic algorithm has poor robustness, and the extraction results require a lot of manual processing, and the effect is not ideal. The semi-automatic extraction using human-computer interaction is currently a more practical choice.

在遥感影像道路半自动提取方法中,主动轮廓模型和模板匹配方法被认为是较为实用的两种方法。基于动态规划的道路提取算法是一种常用的主动轮廓模型方法,它根据遥感影像上的道路特征构建代价函数,然后利用动态规划求解代价函数的极大值来提取道路。Armin Gruen根据低分辨率遥感影像上的道路主要为具有高灰度值的光滑曲线这一特征,提出了一种经典的基于动态规划的道路提取算法,但这种方法只适用于低分辨率影像,而在中高分辨率影像上,道路不再是简单的线状特征,变成了具有一定宽度的长条状区域,因子Poz等人在Gruen的基础上,修改了代价函数,加入了道路宽度信息,使该算法能够用于中高分辨率影像的道路提取。但由于高分辨率影像道路特征复杂多变,传统的算法都是直接根据原始影像上的道路灰度特征定义代价函数,因此很难定义具有普适性的代价函数,导致了传统算法只能提取固定灰度特征的简单道路,对于其他类型的道路,只能重新定义相应的代价函数,在实际应用中有很大局限。In the semi-automatic road extraction methods of remote sensing images, the active contour model and the template matching method are considered to be two more practical methods. The road extraction algorithm based on dynamic programming is a commonly used active contour model method. It constructs a cost function based on the road features on remote sensing images, and then uses dynamic programming to solve the maximum value of the cost function to extract roads. Armin Gruen proposed a classic road extraction algorithm based on dynamic programming based on the feature that roads on low-resolution remote sensing images are mainly smooth curves with high gray values, but this method is only suitable for low-resolution images , while in the medium and high resolution images, the road is no longer a simple linear feature, but a long strip area with a certain width. Factor Poz et al. modified the cost function on the basis of Gruen, adding the road width information, so that the algorithm can be used for road extraction in medium and high resolution images. However, due to the complex and changeable road characteristics of high-resolution images, traditional algorithms define the cost function directly based on the road grayscale features on the original image, so it is difficult to define a universal cost function, resulting in traditional algorithms that can only extract For simple roads with fixed grayscale features, for other types of roads, the corresponding cost function can only be redefined, which has great limitations in practical applications.

发明内容Contents of the invention

本发明的目的是提供一种遥感影像道路提取方法,以解决目前的道路提取方法只能提取与道路模型相符的简单道路的问题;本发明还提供了一种遥感影像道路提取装置。The purpose of the present invention is to provide a remote sensing image road extraction method to solve the problem that the current road extraction method can only extract simple roads that match the road model; the present invention also provides a remote sensing image road extraction device.

本发明为解决上述技术问题而提供一种遥感影像道路提取方法,包括六个技术方案,方法方案一,该方法包括以下步骤:In order to solve the above-mentioned technical problems, the present invention provides a method for extracting roads from remote sensing images, including six technical solutions, method solution 1, which includes the following steps:

1)计算原始遥感影像道路概率密度,将原始遥感影像上的道路特征转换为道路概率密度的特征;1) Calculate the road probability density of the original remote sensing image, and convert the road features on the original remote sensing image into the features of the road probability density;

2)根据道路中心线上的概率值高于其它位置上的概率值的特征构建道路中心线模型,并根据道路中心线模型确定代价函数;2) Construct a road centerline model according to the characteristic that the probability value on the road centerline is higher than that on other positions, and determine the cost function according to the road centerline model;

3)利用动态规划求解代价函数的极大值,以该极大值作为道路中心线。3) Use dynamic programming to solve the maximum value of the cost function, and use this maximum value as the road centerline.

本发明将原始遥感影像上复杂多样的道路特征转换为道路概率分布图上简单一致的道路特征,使之能够提取不同类型的高分辨率遥感影像道路,不需要修改代价函数,提高了方法的普适性。The invention converts complex and diverse road features on the original remote sensing image into simple and consistent road features on the road probability distribution map, so that it can extract different types of high-resolution remote sensing image roads without modifying the cost function, and improves the generality of the method. fitness.

方法方案二:在方法方案一的基础上,所述步骤1)的遥感影像道路概率密度是采用支持向量机和核密度估计确定的,具体过程如下:Method scheme two: on the basis of method scheme one, the road probability density of the remote sensing image in step 1) is determined by using support vector machine and kernel density estimation, and the specific process is as follows:

A.采用支持向量机将遥感影像进行分类,得到一系列道路样本点;A. Using support vector machine to classify the remote sensing image, get a series of road sample points;

B.利用核密度估计计算各道路样本点的概率密度。B. Calculate the probability density of each road sample point using kernel density estimation.

本发明采用支持向量机进行分类,所需的训练样本由人工从道路特征数据库中挑选,通过少量人工参与完成高分分辨率遥感影像上具有一定宽度的道路信息采集任务,明显缩短成图周期,大大提高了遥感影像自动化处理程度。The present invention uses a support vector machine for classification, the required training samples are manually selected from the road feature database, and a small amount of manual participation is used to complete the task of collecting road information with a certain width on the high-resolution remote sensing image, which significantly shortens the mapping cycle. The degree of automatic processing of remote sensing images has been greatly improved.

方法方案三:在方法方案二的基础上,所述步骤B中得到的概率密度为:Method scheme three: on the basis of method scheme two, the probability density obtained in the step B is:

其中xi为得到的第i个道路样本点,是点x处的概率密度估值,h为核密度估计的带宽,K(x)为核函数。Where x i is the i-th road sample point obtained, is the probability density estimate at point x, h is the bandwidth of the kernel density estimate, and K(x) is the kernel function.

方法方案四:在方法方案三的基础上,所述的核函数采用高斯核函数。Method scheme four: on the basis of method scheme three, the kernel function adopts a Gaussian kernel function.

方法方案五:在方法方案一或二的基础上,所述步骤2)中构建的道路中心线模型为:Method scheme five: on the basis of method scheme one or two, the road centerline model constructed in said step 2) is:

Ep=∫{G[f(s)]}2ds=maxE p =∫{G[f(s)]} 2 ds=max

Eg=∫[f″(s)]2ds=minE g =∫[f″(s)] 2 ds=min

Cg=|f″(s)|≤T1 C g =|f″(s)|≤T 1

其中G(x)表示道路概率分布函数,f(s)表示道路中心线,T1为设定阈值。Among them, G(x) represents the road probability distribution function, f(s) represents the road centerline, and T 1 is the set threshold.

方法方案六:在方法方案五的基础上,根据道路中心线模型构建的代价函数为:Method 6: On the basis of method 5, the cost function constructed according to the road centerline model is:

其中p={p1,...,pn},pi=(xi,yi)是道路中心线上折线段的n个顶点,S为线段pipi+1的像素集合,为概率分布图上线段pipi+1的灰度函数,ai为线段pipi+1的方向,|Δsi|为线段pipi+1的长度。Where p={p 1 ,...,p n }, p i =( xi ,y i ) are the n vertices of the polyline segment on the center line of the road, S is the pixel set of the line segment p i p i+1 , is the gray function of the line segment p i p i+1 on the probability distribution graph, a i is the direction of the line segment p i p i+1 , |Δs i | is the length of the line segment p i p i+1 .

本发明还提供了一种遥感影像道路提取装置,包括以下六个方案,装置方案一:该道路提取装置包括存储器和处理器以及存储在所述存储器上并在所述处理器上运行的计算机程序,所述处理器与所述存储器相耦合,所述处理器执行所述计算机程序时实现以下指令:The present invention also provides a remote sensing image road extraction device, including the following six solutions, device solution 1: the road extraction device includes a memory, a processor, and a computer program stored in the memory and running on the processor , the processor is coupled to the memory, and the processor implements the following instructions when executing the computer program:

1)计算原始遥感影像道路概率密度,将原始遥感影像上的道路特征转换为道路概率密度的特征;1) Calculate the road probability density of the original remote sensing image, and convert the road features on the original remote sensing image into the features of the road probability density;

2)根据道路中心线上的概率值高于其它位置上的概率值的特征构建道路中心线模型,并根据道路中心线模型确定代价函数;2) Construct a road centerline model according to the characteristic that the probability value on the road centerline is higher than that on other positions, and determine the cost function according to the road centerline model;

3)利用动态规划求解代价函数的极大值,以该极大值作为道路中心线。3) Use dynamic programming to solve the maximum value of the cost function, and use this maximum value as the road centerline.

装置方案二:在装置方案一的基础上,所述步骤1)的遥感影像道路概率密度是采用支持向量机和核密度估计确定的,具体过程如下:Device scheme two: on the basis of device scheme one, the road probability density of the remote sensing image in step 1) is determined by using support vector machine and kernel density estimation, and the specific process is as follows:

A.采用支持向量机将遥感影像进行分类,得到一系列道路样本点;A. Using support vector machine to classify the remote sensing image, get a series of road sample points;

B.利用核密度估计计算各道路样本点的概率密度。B. Calculate the probability density of each road sample point using kernel density estimation.

装置方案三:在装置方案二的基础上,所述步骤B中得到的概率密度为:Device scheme three: on the basis of device scheme two, the probability density obtained in the step B is:

其中xi为得到的第i个道路样本点,是点x处的概率密度估值,h为核密度估计的带宽,K(x)为核函数。Where x i is the i-th road sample point obtained, is the probability density estimate at point x, h is the bandwidth of the kernel density estimate, and K(x) is the kernel function.

装置方案四:在装置方案三的基础上,所述的核函数采用高斯核函数。Device scheme four: on the basis of device scheme three, the kernel function adopts a Gaussian kernel function.

装置方案五:在装置方案一或二的基础上,所述步骤2)中构建的道路中心线模型为:Device scheme five: on the basis of device scheme one or two, the road centerline model constructed in said step 2) is:

Ep=∫{G[f(s)]}2ds=maxE p =∫{G[f(s)]} 2 ds=max

Eg=∫[f″(s)]2ds=minE g =∫[f″(s)] 2 ds=min

Cg=|f″(s)|≤T1 C g =|f″(s)|≤T 1

其中G(x)表示道路概率分布函数,f(s)表示道路中心线,T1为设定阈值。Among them, G(x) represents the road probability distribution function, f(s) represents the road centerline, and T 1 is the set threshold.

装置方案六:在装置方案五的基础上,根据道路中心线模型构建的代价函数为:Installation scheme six: On the basis of installation scheme five, the cost function constructed according to the road centerline model is:

其中p={p1,...,pn},pi=(xi,yi)是道路中心线上折线段的n个顶点,S为线段pipi+1的像素集合,为概率分布图上线段pipi+1的灰度函数,ai为线段pipi+1的方向,|Δsi|为线段pipi+1的长度。Where p={p 1 ,...,p n }, p i =( xi ,y i ) are the n vertices of the polyline segment on the center line of the road, S is the pixel set of the line segment p i p i+1 , is the gray function of the line segment p i p i+1 on the probability distribution graph, a i is the direction of the line segment p i p i+1 , |Δs i | is the length of the line segment p i p i+1 .

附图说明Description of drawings

图1是本发明遥感影像道路提取方法的流程图;Fig. 1 is the flow chart of the remote sensing image road extraction method of the present invention;

图2是本发明遥感影像道路提取装置的事件处理流程图。Fig. 2 is a flow chart of event processing of the remote sensing image road extraction device of the present invention.

具体实施方式detailed description

下面结合附图对本发明的具体实施方式做进一步的说明。The specific embodiments of the present invention will be further described below in conjunction with the accompanying drawings.

本发明根据高分辨率遥感影像道路波普特征,采用支持向量机方法将遥感影像分为道路类和非道路类,获取一系列道路样本点,使用核密度估计计算道路概率分布图;根据道路概率分布图上的中心线上的概率值要明显高于其它位置上的概率值的特征构建道路中心线模型和代价函数,利用动态规划提取遥感影像上的道路中心线。该方法的流程如图1所示,具体实施过程如下:The present invention divides remote sensing images into roads and non-roads by using support vector machine method according to the road pop features of high-resolution remote sensing images, obtains a series of road sample points, and uses kernel density estimation to calculate road probability distribution map; according to the road probability The probability value of the centerline on the distribution map is significantly higher than that of other positions to construct the road centerline model and cost function, and use dynamic programming to extract the road centerline on the remote sensing image. The process flow of this method is shown in Figure 1, and the specific implementation process is as follows:

1.获取遥感影像,并将将遥感影像分为道路类和非道路类,获取一系列道路样本点。1. Obtain remote sensing images, divide the remote sensing images into road and non-road, and obtain a series of road sample points.

由于高分辨率遥感影像存在异物同谱和同物异谱等问题,选用软间隔支持向量机进行分类。支持向量机是由Corinna Cortes和Vapnik等人于1995年提出的一种二类分类模型,它在文本分类、手写数字识别、目标识别以及人脸检测中表现出许多特有的优势,被认为是当前最好的学习算法之一。本发明采用支持向量机将高分辨率的遥感影像划分为道路类和非道路类,以获取一系列道路样本点,其中在采用支持向量机进行分类时,所需的训练样本是由人工从道路特征数据库中挑选的。Due to the problems of different objects in the same spectrum and same objects in different spectra in high-resolution remote sensing images, soft margin support vector machines were selected for classification. Support vector machine is a two-class classification model proposed by Corinna Cortes and Vapnik et al. in 1995. It shows many unique advantages in text classification, handwritten digit recognition, target recognition and face detection, and is considered to be the current One of the best learning algorithms. The present invention uses support vector machine to divide high resolution remote sensing image into road class and non-road class to obtain a series of road sample points. selected from the feature database.

2.计算所获取的道路样本点的道路概率分布图。2. Calculate the road probability distribution map of the obtained road sample points.

道路概率分布图表示遥感影像上每一个像素点是道路的概率,概率值是根据像素灰度及其他因素综合计算出的,处理服从未知分布的观测数据,通常需要从已知数据中估计其概率密度函数,这称为概率密度估计。目前常用的概率密度估计方法有参数估计、直方图估计和核密度估计。基于直方图的概率密度估计虽然能够描述数据内在的分布规律,但仍存在三个主要缺点:①图形不光滑;②直方图的形状易受起始点的位置和区间宽度的影响;③当数据为三维或更高维时,直方图估计存在很大局限。核密度估计是直方图估计的一种推广,但与直方图估计不同的是,核密度估计根据观测数据靠近估计点x的程度给予相应的权重,克服了直方图估计不光滑、依赖起始点等缺点。本发明利用核密度估计计算各道路样本点的概率分布图。核密度估计采用的数学模型为:The road probability distribution map indicates the probability that each pixel on the remote sensing image is a road. The probability value is comprehensively calculated based on the pixel gray level and other factors. To deal with observation data that obeys an unknown distribution, it is usually necessary to estimate its probability from known data. density function, this is called a probability density estimate. At present, the commonly used probability density estimation methods include parameter estimation, histogram estimation and kernel density estimation. Although the probability density estimation based on the histogram can describe the inherent distribution of the data, there are still three main shortcomings: ① The graph is not smooth; ② The shape of the histogram is easily affected by the position of the starting point and the width of the interval; ③ When the data is Histogram estimation has significant limitations in three dimensions or higher. Kernel density estimation is a generalization of histogram estimation, but different from histogram estimation, kernel density estimation gives corresponding weights according to the degree of observation data close to the estimated point x, which overcomes the histogram estimation is not smooth, depends on the starting point, etc. shortcoming. The present invention uses kernel density estimation to calculate the probability distribution graph of each road sample point. The mathematical model used for kernel density estimation is:

其中,xi为第i个道路样本点,是道路样本点x处的概率密度估计值,h为核密度估计的带宽,K(x)为核函数。在核密度估计中,常用的核函数有高斯核函数、Epanechnikov核函数、三角核函数和矩形核函数。本发明采用高斯核函数,其数学表达式与标准正态分布相似:Among them, x i is the i-th road sample point, is the estimated value of the probability density at the road sample point x, h is the bandwidth of kernel density estimation, and K(x) is the kernel function. In kernel density estimation, commonly used kernel functions are Gaussian kernel function, Epanechnikov kernel function, triangular kernel function and rectangular kernel function. The present invention adopts Gauss kernel function, and its mathematical expression is similar to standard normal distribution:

3.根据道路中心线上的概率值明显高于其它位置上的概率值的特征构建道路中心线模型和代价函数。3. Construct the road centerline model and cost function according to the characteristic that the probability value on the road centerline is significantly higher than that on other positions.

本发明选择利用道路概率分布图上的道路特征来构建道路中心线模型和代价函数,根据道路概率分布特征建立的道路中心线模型为:The present invention selects and utilizes the road feature on the road probability distribution map to construct the road centerline model and the cost function, and the road centerline model established according to the road probability distribution feature is:

1)在道路概率分布图上,道路中心线上的道路样本点的概率估计值比其他道路样本点的概率估计值大,因此,道路中心线上所有样本点的概率估计值的平方和将达到一个最大值,即:1) On the road probability distribution map, the estimated probability value of the road sample point on the road centerline is larger than that of other road sample points, so the sum of the squares of the probability estimates of all sample points on the road centerline will reach A maximum value, namely:

Ep=∫{G[f(s)]}2ds=maxE p =∫{G[f(s)]} 2 ds=max

其中G(x)表示道路概率分布函数,f(s)表示道路中心线。Among them, G(x) represents the road probability distribution function, and f(s) represents the road centerline.

2)根据道路的几何特性,道路中心线应为一条光滑曲线,即:2) According to the geometric characteristics of the road, the center line of the road should be a smooth curve, namely:

Eg=∫[f″(s)]2ds=minE g =∫[f″(s)] 2 ds=min

3)根据交通安全法规要求,道路的局部曲线率存在一个上界,即:3) According to the requirements of traffic safety regulations, there is an upper bound on the local curve rate of the road, namely:

Cg=|f″(s)|≤T1 C g =|f″(s)|≤T 1

其中T1为给定阈值。where T 1 is a given threshold.

4.利用动态规划算法来求解道路中心线模型,实现道路的提取。4. Use the dynamic programming algorithm to solve the road centerline model to realize the extraction of roads.

在具体求解过程中,将道路中心线用一条含n个顶点的折线段表示,且折线段上的顶点绕其初始位置(xi,yi)移动,设折线段的顶点为p={p1,...,pn},pi=(xi,yi),道路中心线模型的性质1)离散形式为:In the specific solution process, the road centerline is represented by a polyline segment containing n vertices, and the vertices on the polyline segment move around their initial positions ( xi , y i ), and the vertices of the polyline segment are p={p 1 ,...,p n },p i =( xi ,y i ), the properties of the road centerline model 1) The discrete form is:

其中S为线段pipi+1的像素集合,为概率分布图上线段pipi+1的灰度函数,性质2)和性质3)的离散形式为:Where S is the pixel set of the line segment p i p i+1 , is the gray function of the line segment p i p i+1 on the probability distribution graph, and the discrete forms of properties 2) and 3) are:

Cg=|ai-ai+1|<T1 C g =|a i -a i+1 |<T 1

其中ai为线段pipi+1的方向,|Δsi|为线段pipi+1的长度,即Where a i is the direction of line segment p i p i+1 , |Δs i | is the length of line segment p i p i+1 , namely

根据道路中心线模型,构建如下代价函数:According to the road centerline model, the following cost function is constructed:

其中,代价函数E为一系列函数项Ei的和,每个Ei只依赖于折线段的三个相邻顶点{pi-1,pi,pi+1},pi=(xi,yi),同时代价函数E必须满足限制条件式|ai-ai+1|<T1Among them, the cost function E is the sum of a series of function items E i , each E i only depends on the three adjacent vertices {p i-1 , p i , p i+1 } of the polyline segment, p i =(x i , y i ), and the cost function E must satisfy the constraint condition |a i -a i+1 |<T 1 .

动态规划主要用来解决最优化问题,采用动态规划的求解过程为:Dynamic programming is mainly used to solve optimization problems, and the solution process using dynamic programming is:

对于一个代价函数:For a cost function:

g=g(x1,x2,...,xn),0≤xi≤mi,i=1,2,...,n (1)g=g(x 1 ,x 2 ,...,x n ),0≤x i ≤m i ,i=1,2,...,n (1)

当其自变量(x1,x2,...,xn)为离散值并且代价函数g为如下形式时:When its independent variables (x 1 ,x 2 ,...,x n ) are discrete values and the cost function g is as follows:

g(x1,x2,...,xn)=g1(x1,x2,x3)+g2(x2,x3,x4)+...+gn-2(xn-2,xn-1,xn) (2)g(x 1 ,x 2 ,...,x n )=g 1 (x 1 ,x 2 ,x 3 )+g 2 (x 2 ,x 3 ,x 4 )+...+g n-2 (x n-2 ,x n-1 ,x n ) (2)

该函数最大值M可用动态规划算法求解,其过程为:The maximum value M of this function can be solved by dynamic programming algorithm, and the process is:

①对于任意变量x2,x3,求解函数f1(x2,x3):①For any variable x 2 , x 3 , solve the function f 1 (x 2 , x 3 ):

②仿照第一步继续消除变量x2,即对于任意变量x3,x4,求解函数f2(x3,x4):②Continue to eliminate the variable x 2 according to the first step, that is, for any variable x 3 , x 4 , solve the function f 2 (x 3 ,x 4 ):

③重复上述步骤,最终可得:③Repeat the above steps to finally get:

则代价函数的最大值M为:Then the maximum value M of the cost function is:

本发明一种遥感影像道路提取装置的实施例Embodiment of a remote sensing image road extraction device of the present invention

本实施例中的道路提取装置包括存储器和处理器以及存储在存储器上并在理器上运行的计算机程序,处理器与存储器相耦合,处理器执行计算机程序时实现以下指令:1)计算原始遥感影像道路概率密度,将原始遥感影像上的道路特征转换为道路概率密度的特征;2)根据道路中心线上的概率值高于其它位置上的概率值的特征构建道路中心线模型,并根据道路中心线模型确定代价函数;3)利用动态规划求解代价函数的极大值,以该极大值作为道路中心线。处理器可以采用单片机、DSP、PLC或MCU等,存储器可以采用RAM存储器、闪存、ROM存储器、EPROM存储器、EEPROM存储器、寄存器、硬盘、移动磁盘、CD-ROM或者本领域已知的任何其他形式的存储介质,各指令的具体实现手段已在方法的实施例中进行了说明,这里不再赘述。该装置的事件处理流程如图2所示。装置实施过程中的事件流程主要分为3部分,主框架窗口、遥感影像道路提取装置主控入口、数据库引擎访问部件。首先用户根据需要通过主框架窗口进行相应操作,设置道路采集的具体参数;然后装置调用外部数据库引擎当中的数据或模型,获取相应的道路特征;最后,通过遥感影像道路提取装置主控入口进行实际道路提取处理并对最后的结果进行评价。The road extraction device in this embodiment includes a memory, a processor and a computer program stored on the memory and run on the processor, the processor is coupled with the memory, and the processor implements the following instructions when executing the computer program: 1) calculate the original remote sensing Image road probability density, transforming the road features on the original remote sensing image into the features of road probability density; 2) Constructing the road centerline model according to the feature that the probability value on the road centerline is higher than that on other positions, and according to the road The centerline model determines the cost function; 3) Use dynamic programming to solve the maximum value of the cost function, and use the maximum value as the road centerline. Processor can adopt single-chip microcomputer, DSP, PLC or MCU etc., memory can adopt RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, register, hard disk, mobile disk, CD-ROM or any other form known in the art The storage medium and the specific implementation means of each instruction have been described in the embodiments of the method, and will not be repeated here. The event processing flow of the device is shown in FIG. 2 . The event flow during the implementation of the device is mainly divided into three parts, the main frame window, the main control entry of the remote sensing image road extraction device, and the database engine access component. First, the user performs corresponding operations through the main frame window to set the specific parameters of road collection; then the device invokes the data or models in the external database engine to obtain the corresponding road features; Road extraction is processed and the final result is evaluated.

本发明可应用于高分辨率遥感影像道路信息采集作业当中,改变现有地理信息保障模式,通过少量人工参与完成高分分辨率遥感影像上具有一定宽度的道路信息采集任务,明显缩短成图周期,大大提高遥感影像自动化处理程度。对我国经济建设和社会发展具有重要作用。此外,本发明也可用于间信息对地震灾害监测、GIS更新、地图制图等方面,具有广泛的社会应用前景和重要的应用价值。The present invention can be applied to the road information collection operation of high-resolution remote sensing images, changing the existing geographic information guarantee mode, completing the road information collection tasks with a certain width on high-resolution remote sensing images through a small amount of manual participation, and significantly shortening the mapping cycle , greatly improving the automatic processing of remote sensing images. It plays an important role in my country's economic construction and social development. In addition, the present invention can also be used in inter-information monitoring of earthquake disasters, GIS updating, map drawing, etc., and has broad social application prospects and important application values.

以上给出了具体的实施方式,但本发明不局限于所描述的实施方式,对本领域普通技术人员而言,根据本发明的教导,设计出各种变形的模型、公式、参数并不需要花费创造性劳动,在不脱离本发明的原理和精神的情况下对实施方式进行的变化、修改、替换和变型仍落在本发明的保护范围内。Specific implementations have been given above, but the present invention is not limited to the described implementations. For those of ordinary skill in the art, according to the teachings of the present invention, it does not need to spend money to design various deformation models, formulas, and parameters. Creative work, changes, modifications, replacements and variations to the implementation without departing from the principle and spirit of the present invention still fall within the protection scope of the present invention.

Claims (10)

1. a kind of method for extracting remote sensing image road, it is characterised in that the method for extracting roads comprises the following steps:
1) original remote sensing image road probability density is calculated, it is close that the roadway characteristic on original remote sensing image is converted into road probability The feature of degree;
2) it is higher than the feature construction road-center line model of the probable value in other positions according to the probable value on road axis, And cost function is determined according to road-center line model;
3) maximum of cost function is solved using Dynamic Programming, road axis is used as using the maximum.
2. method for extracting remote sensing image road according to claim 1, it is characterised in that the remote sensing image of the step 1) Road probability density determines that detailed process is as follows using SVMs and Density Estimator:
A. remote sensing image is classified using SVMs, obtains series of road sample point;
B. the probability density of each road sample point is calculated using Density Estimator.
3. method for extracting remote sensing image road according to claim 2, it is characterised in that obtained in the step B general Rate density is:
<mrow> <mover> <mi>f</mi> <mo>^</mo> </mover> <mrow> <mo>(</mo> <mi>x</mi> <mo>)</mo> </mrow> <mo>=</mo> <mfrac> <mn>1</mn> <mrow> <msup> <mi>nh</mi> <mi>d</mi> </msup> </mrow> </mfrac> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>n</mi> </munderover> <mi>K</mi> <mrow> <mo>(</mo> <mfrac> <mrow> <mi>x</mi> <mo>-</mo> <msub> <mi>x</mi> <mi>i</mi> </msub> </mrow> <mi>h</mi> </mfrac> <mo>)</mo> </mrow> </mrow>
Wherein xiFor i-th obtained of road sample point,It is the probability density valuation at point x, h is the band of Density Estimator Width, K (x) are kernel function.
4. method for extracting remote sensing image road according to claim 1 or 2, it is characterised in that built in the step 2) Road-center line model be:
Ep=∫ { G [f (s)] }2Ds=max
Eg=∫ [f " (s)]2Ds=min
Cg=| f " (s) |≤T1
Wherein G (x) represents road probability-distribution function, and f (s) represents road axis, T1For given threshold.
5. method for extracting remote sensing image road according to claim 4, it is characterised in that according to road-center line model structure The cost function built is:
<mrow> <mi>E</mi> <mo>=</mo> <munder> <mo>&amp;Sigma;</mo> <mi>i</mi> </munder> <msub> <mi>E</mi> <mi>i</mi> </msub> <mrow> <mo>(</mo> <msub> <mi>p</mi> <mrow> <mi>i</mi> <mo>-</mo> <mn>1</mn> </mrow> </msub> <mo>,</mo> <msub> <mi>p</mi> <mi>i</mi> </msub> <mo>,</mo> <msub> <mi>p</mi> <mrow> <mi>i</mi> <mo>+</mo> <mn>1</mn> </mrow> </msub> <mo>)</mo> </mrow> <mo>=</mo> <munder> <mo>&amp;Sigma;</mo> <mi>i</mi> </munder> <mo>&amp;lsqb;</mo> <mn>1</mn> <mo>+</mo> <mi>cos</mi> <mrow> <mo>(</mo> <msub> <mi>a</mi> <mi>i</mi> </msub> <mo>-</mo> <msub> <mi>a</mi> <mrow> <mi>i</mi> <mo>+</mo> <mn>1</mn> </mrow> </msub> <mo>)</mo> </mrow> <mo>&amp;rsqb;</mo> <munder> <mo>&amp;Sigma;</mo> <mrow> <mi>s</mi> <mo>&amp;Element;</mo> <mi>S</mi> </mrow> </munder> <msub> <msup> <mi>g</mi> <mn>2</mn> </msup> <mrow> <msub> <mi>P</mi> <mi>i</mi> </msub> <msub> <mi>P</mi> <mrow> <mi>i</mi> <mo>+</mo> <mn>1</mn> </mrow> </msub> </mrow> </msub> <mrow> <mo>(</mo> <mi>s</mi> <mo>)</mo> </mrow> <mo>/</mo> <mo>|</mo> <msub> <mi>&amp;Delta;s</mi> <mi>i</mi> </msub> <mo>|</mo> </mrow>
Wherein p={ p1,...,pn},pi=(xi,yi) be broken line on road axis n summit, S is line segment pipi+1's Pixel set,For line segment p in probability distribution graphipi+1Gamma function, aiFor line segment pipi+1Direction, | Δ si| it is line segment pipi+1Length.
A kind of 6. remote sensing image road extraction element, it is characterised in that the road extraction device include memory and processor with And the computer program run on the memory and on the processor is stored in, the processor and the memory phase Coupling, realized described in the computing device during computer program to give an order:
1) original remote sensing image road probability density is calculated, it is close that the roadway characteristic on original remote sensing image is converted into road probability The feature of degree;
2) it is higher than the feature construction road-center line model of the probable value in other positions according to the probable value on road axis, And cost function is determined according to road-center line model;
3) maximum of cost function is solved using Dynamic Programming, road axis is used as using the maximum.
7. remote sensing image road extraction element according to claim 6, it is characterised in that the remote sensing image of the step 1) Road probability density determines that detailed process is as follows using SVMs and Density Estimator:
A. remote sensing image is classified using SVMs, obtains series of road sample point;
B. the probability density of each road sample point is calculated using Density Estimator.
8. remote sensing image road extraction element according to claim 7, it is characterised in that obtained in the step B general Rate density is:
<mrow> <mover> <mi>f</mi> <mo>^</mo> </mover> <mrow> <mo>(</mo> <mi>x</mi> <mo>)</mo> </mrow> <mo>=</mo> <mfrac> <mn>1</mn> <mrow> <msup> <mi>nh</mi> <mi>d</mi> </msup> </mrow> </mfrac> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>n</mi> </munderover> <mi>K</mi> <mrow> <mo>(</mo> <mfrac> <mrow> <mi>x</mi> <mo>-</mo> <msub> <mi>x</mi> <mi>i</mi> </msub> </mrow> <mi>h</mi> </mfrac> <mo>)</mo> </mrow> </mrow>
Wherein xiFor i-th obtained of road sample point,It is the probability density valuation at point x, h is the band of Density Estimator Width, K (x) are kernel function.
9. the remote sensing image road extraction element according to claim 6 or 7, it is characterised in that built in the step 2) Road-center line model be:
Ep=∫ { G [f (s)] }2Ds=max
Eg=∫ [f " (s)]2Ds=min
Cg=| f " (s) |≤T1
Wherein G (x) represents road probability-distribution function, and f (s) represents road axis, T1For given threshold.
10. remote sensing image road extraction element according to claim 9, it is characterised in that according to road-center line model The cost function of structure is:
<mrow> <mi>E</mi> <mo>=</mo> <munder> <mo>&amp;Sigma;</mo> <mi>i</mi> </munder> <msub> <mi>E</mi> <mi>i</mi> </msub> <mrow> <mo>(</mo> <msub> <mi>p</mi> <mrow> <mi>i</mi> <mo>-</mo> <mn>1</mn> </mrow> </msub> <mo>,</mo> <msub> <mi>p</mi> <mi>i</mi> </msub> <mo>,</mo> <msub> <mi>p</mi> <mrow> <mi>i</mi> <mo>+</mo> <mn>1</mn> </mrow> </msub> <mo>)</mo> </mrow> <mo>=</mo> <munder> <mo>&amp;Sigma;</mo> <mi>i</mi> </munder> <mo>&amp;lsqb;</mo> <mn>1</mn> <mo>+</mo> <mi>c</mi> <mi>o</mi> <mi>s</mi> <mrow> <mo>(</mo> <msub> <mi>a</mi> <mi>i</mi> </msub> <mo>-</mo> <msub> <mi>a</mi> <mrow> <mi>i</mi> <mo>+</mo> <mn>1</mn> </mrow> </msub> <mo>)</mo> </mrow> <mo>&amp;rsqb;</mo> <munder> <mo>&amp;Sigma;</mo> <mrow> <mi>s</mi> <mo>&amp;Element;</mo> <mi>S</mi> </mrow> </munder> <msub> <msup> <mi>G</mi> <mn>2</mn> </msup> <mrow> <msub> <mi>P</mi> <mi>i</mi> </msub> <msub> <mi>P</mi> <mrow> <mi>i</mi> <mo>+</mo> <mn>1</mn> </mrow> </msub> </mrow> </msub> <mrow> <mo>(</mo> <mi>s</mi> <mo>)</mo> </mrow> <mo>/</mo> <mo>|</mo> <msub> <mi>&amp;Delta;s</mi> <mi>i</mi> </msub> <mo>|</mo> </mrow>
Wherein p={ p1,...,pn},pi=(xi,yi) be broken line on road axis n summit, S is line segment pipi+1's Pixel set,For line segment p in probability distribution graphipi+1Gamma function, aiFor line segment pipi+1Direction, | Δ si| it is line segment pipi+1Length.
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Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110175574A (en) * 2019-05-28 2019-08-27 中国人民解放军战略支援部队信息工程大学 A kind of Road network extraction method and device
CN112749453A (en) * 2020-12-16 2021-05-04 安徽三禾一信息科技有限公司 Complex equipment residual service life prediction based on improved SVR
CN113408457A (en) * 2021-06-29 2021-09-17 西南交通大学 Road information intelligent extraction method combining high-resolution image and video image
CN114913144A (en) * 2022-05-06 2022-08-16 国交空间信息技术(北京)有限公司 Method, device and system for determining road center line, computing equipment and storage medium

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101714252A (en) * 2009-11-26 2010-05-26 上海电机学院 Method for extracting road in SAR image
CN103258203A (en) * 2013-05-20 2013-08-21 武汉大学 Method for automatically extracting road centerline of remote-sensing image
CN104915636A (en) * 2015-04-15 2015-09-16 北京工业大学 Remote sensing image road identification method based on multistage frame significant characteristics
CN106709465A (en) * 2016-12-29 2017-05-24 武汉大学 Polarization SAR image road extraction method based on conditional random field
CN106778605A (en) * 2016-12-14 2017-05-31 武汉大学 Remote sensing image road net extraction method under navigation data auxiliary

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101714252A (en) * 2009-11-26 2010-05-26 上海电机学院 Method for extracting road in SAR image
CN103258203A (en) * 2013-05-20 2013-08-21 武汉大学 Method for automatically extracting road centerline of remote-sensing image
CN104915636A (en) * 2015-04-15 2015-09-16 北京工业大学 Remote sensing image road identification method based on multistage frame significant characteristics
CN106778605A (en) * 2016-12-14 2017-05-31 武汉大学 Remote sensing image road net extraction method under navigation data auxiliary
CN106709465A (en) * 2016-12-29 2017-05-24 武汉大学 Polarization SAR image road extraction method based on conditional random field

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
曹帆之,徐杨斌,朱宝山,李润生: "利用动态规划半自动提取高分辨率遥感影像道路中心线", 《测绘科学技术学报》 *

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110175574A (en) * 2019-05-28 2019-08-27 中国人民解放军战略支援部队信息工程大学 A kind of Road network extraction method and device
CN112749453A (en) * 2020-12-16 2021-05-04 安徽三禾一信息科技有限公司 Complex equipment residual service life prediction based on improved SVR
CN112749453B (en) * 2020-12-16 2023-10-13 安徽三禾一信息科技有限公司 Complex equipment residual service life prediction method based on improved SVR
CN113408457A (en) * 2021-06-29 2021-09-17 西南交通大学 Road information intelligent extraction method combining high-resolution image and video image
CN114913144A (en) * 2022-05-06 2022-08-16 国交空间信息技术(北京)有限公司 Method, device and system for determining road center line, computing equipment and storage medium

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