SPOT立体像对提取DEM及其不确定性研究
摘要
当前,空间数据的不确定性研究理论具有十分重要的意义,而DEM作为空间数据基础设施的重要组成部分,其不确定性的研究也很重要。DEM作为地球表面的数字表达,有着广泛的应用。建立DEM的方法有很多种,但传统的方法相当的费时、费力,且容易出现输入错误,具有一定的滞后性。随着航空、航天遥感技术的不断发展,传感器的性能不断提高,可以通过遥感技术获取立体像对,通过摄影测量技术产生DEM。
本文使用SPOT影像,利用VirtuoZo工作站版本来提取DEM。在提取DEM的过程中,会不可避免地存在误差和不确定性。本文以江宁为实验区,用蒙特卡罗方法对所提取的DEM进行不确定性模拟。
提取DEM时,控制点是从1:5万地形图上选择的。控制点的选择和分布对DEM的精度很重要。通过分析,发现利用VirtuoZo提取DEM主要包括以下误差:地形图本身的精度不高,存在误差,所以控制点本身的精度不高;影像分辨率为10米,较低,不利于控制点的选择;江宁区地形较为复杂、破碎,会影响高程的计算,从而影响DEM的精度:匹配窗口的选择会影响DEM的精度;人为误差、系统误差以及随机误差等等。
论文在对DEM作精度评价之前,先对DEM的粗差进行处理。采用了一种基于规则格网DEM的粗差检测和剔除方法。结果表明,在地势较高的地方,粗差点较多,在地势较平坦的地方,粗差点较少。
本文采用检查点法和剖面法对DEM进行精度评价。在用检查点做精度评价时,将所提取的DEM与1:5万的DEM作比较,选取了20个检查点,算出RMSE为11.86m,并得出一个结论:在地势比较平坦、山地起伏较小的地方,提取的DEM精度较好。在用剖面法进行精度评价时,在X方向、Y方向和任意方向分别做了8、9、11条剖面线,并算出不同方向各个剖面曲线的RMSE值以及RMSE的平均值,发现不同方向剖面曲线的RMSE的平均值相差不大,此值与20个检查点的RMSE也相差不大。这些基本满足1:10万DEM的精度要求。对于江宁这样地形较为复杂、破碎的地带,用VirtuoZo提取DEM能够满足1:10万DEM的精度要求,其他地区也应该能满足这样的要求。
本文采用蒙特卡罗方法模拟DEM的不确定性,其中,重点描述了随机场的产生和N值的确定。通过比较各种随机场的生成方法,最后确定用随机法来产生随机场,以确保能模拟最差情况下DEM的不确定性。本文选用两种标准差来模拟DEM的不确定性。当标准差为11.86m时,N值确定为150;当标准差为15.85m时,N值也确定为150。并分别作出模拟的DEM与1:5万DEM的残差图,发现在地形起伏不大的地方,DEM模拟的精度较好。也就是说,在地势比
At the present time, the research of the uncertainty of spatial data is very significant. As an important component of the basic establishment of spatial data, the uncertainty of DEM is also very important. As a digital expression of earth surface, DEM has a widespread application. There are many methods to generate DEM. But the traditional methods take a lot of time and effort. And they are inclined to generate the problem of input error. They have a property of hysteresis quality. With the continuous development of aerospace technology and aerial technology and the continuous increasement the performance of sensors, DEM can be got by photogrammetric surveying after stereo pairs have been got by RS technology.This paper extracts DEM using SPOT images and VirtuoZo Workstation which is popular in our country at present. There are errors and uncertainties inevitably when extracting DEM. This paper makes use of Monte Carlo method to simulate the uncertainty of extracted DEM using Jiangning as an experimental area.The control points are collected from topographical map of 1:50000 when generating DEM. The collection and distribution of the control points are very important for the accuracy of DEM. Using VirtuoZo to generate DEM includes error as follows: the accuracy of topographical map isn't high, thus accuracy of the control points is low. The resolution of the image is 10m, which is not advantage to the collection of the control points. The area of Jiangning is complex and cracked, which will influence the computation of elevation. Then it will influence the accuracy of DEM. The selection of matching window will influence the accuracy of DEM. There are personal error, system error, random error and so on.Before evaluating the accuracy of DEM, this paper disposes the blunder of DEM.It uses the the method of detection and rejection of the blunder which is based on the grid DEM. The result indicates that the number of blunder points is larger where the surface is higher, while the number of blunder points is smaller where the surface is flater.This paper makes use of checkpoint method and profile method to evaluate the accuracy of DEM. When using checkpoint method to evaluate the accuracy of DEM, the extracted DEM is compared with DEM of 1:50000. The author selects 20 checkpoints, and computes that RMSE is 11.86m. The author makes a conclusion: the accuracy of extracted DEM is better where the surface is flat. When using profile method to evaluate the accuracy of DEM, the author draws 8, 9, 11 section lines in X
aspect, Y aspect and arbitrary aspect respectively. And the author computes RMSE of each section line and the mean of RMSE. The author finds that the mean of RMSE of section line of different aspect is similar. And they are similar with RMSE of 20 checkpoints. They are satisfied with the accuracy of DEM of 1:100000 fundamentally. Using VirtuoZo to extract DEM is satisfied with the accuracy of DEM of 1:100000 for Jiangning whose configuration is complex and crushing, then other regions should be also satisfied with these requirements.This paper makes use of Monte Carlo method to simulate the uncertainty of DEM. It emphasizes on the generation of random field and the determination of N. By comparing various methods of the generation of random field, this paper uses random method to generate random field. This can gurantee to simulate the uncertainty of DEM in the worst case. The paper uses two kinds of RMSE to simulate the uncertainty of DEM. When RMSE is 11.86m, N is 150. When RMSE is 15.85m,N is also 150. The paper also paints the error map between simulated DEM and DEM of 1:50000, and finds that the accuracy of simulated DEM is better where the surface is flatter. That is, the simulated DEM is close to the true value where the surface is flatter.This paper improves Monte Carlo method. Considering the relation of mean and slope, the paper fits the linear function between them. By using this function as the mean of random field, the paper considers the influence of land complexity on the simulation of DEM. The paper also compares the method which has been improved with the method which is not improved, and finds that the improved method can simulate DEM whose accuracy is higher.
本文使用SPOT影像,利用VirtuoZo工作站版本来提取DEM。在提取DEM的过程中,会不可避免地存在误差和不确定性。本文以江宁为实验区,用蒙特卡罗方法对所提取的DEM进行不确定性模拟。
提取DEM时,控制点是从1:5万地形图上选择的。控制点的选择和分布对DEM的精度很重要。通过分析,发现利用VirtuoZo提取DEM主要包括以下误差:地形图本身的精度不高,存在误差,所以控制点本身的精度不高;影像分辨率为10米,较低,不利于控制点的选择;江宁区地形较为复杂、破碎,会影响高程的计算,从而影响DEM的精度:匹配窗口的选择会影响DEM的精度;人为误差、系统误差以及随机误差等等。
论文在对DEM作精度评价之前,先对DEM的粗差进行处理。采用了一种基于规则格网DEM的粗差检测和剔除方法。结果表明,在地势较高的地方,粗差点较多,在地势较平坦的地方,粗差点较少。
本文采用检查点法和剖面法对DEM进行精度评价。在用检查点做精度评价时,将所提取的DEM与1:5万的DEM作比较,选取了20个检查点,算出RMSE为11.86m,并得出一个结论:在地势比较平坦、山地起伏较小的地方,提取的DEM精度较好。在用剖面法进行精度评价时,在X方向、Y方向和任意方向分别做了8、9、11条剖面线,并算出不同方向各个剖面曲线的RMSE值以及RMSE的平均值,发现不同方向剖面曲线的RMSE的平均值相差不大,此值与20个检查点的RMSE也相差不大。这些基本满足1:10万DEM的精度要求。对于江宁这样地形较为复杂、破碎的地带,用VirtuoZo提取DEM能够满足1:10万DEM的精度要求,其他地区也应该能满足这样的要求。
本文采用蒙特卡罗方法模拟DEM的不确定性,其中,重点描述了随机场的产生和N值的确定。通过比较各种随机场的生成方法,最后确定用随机法来产生随机场,以确保能模拟最差情况下DEM的不确定性。本文选用两种标准差来模拟DEM的不确定性。当标准差为11.86m时,N值确定为150;当标准差为15.85m时,N值也确定为150。并分别作出模拟的DEM与1:5万DEM的残差图,发现在地形起伏不大的地方,DEM模拟的精度较好。也就是说,在地势比
At the present time, the research of the uncertainty of spatial data is very significant. As an important component of the basic establishment of spatial data, the uncertainty of DEM is also very important. As a digital expression of earth surface, DEM has a widespread application. There are many methods to generate DEM. But the traditional methods take a lot of time and effort. And they are inclined to generate the problem of input error. They have a property of hysteresis quality. With the continuous development of aerospace technology and aerial technology and the continuous increasement the performance of sensors, DEM can be got by photogrammetric surveying after stereo pairs have been got by RS technology.This paper extracts DEM using SPOT images and VirtuoZo Workstation which is popular in our country at present. There are errors and uncertainties inevitably when extracting DEM. This paper makes use of Monte Carlo method to simulate the uncertainty of extracted DEM using Jiangning as an experimental area.The control points are collected from topographical map of 1:50000 when generating DEM. The collection and distribution of the control points are very important for the accuracy of DEM. Using VirtuoZo to generate DEM includes error as follows: the accuracy of topographical map isn't high, thus accuracy of the control points is low. The resolution of the image is 10m, which is not advantage to the collection of the control points. The area of Jiangning is complex and cracked, which will influence the computation of elevation. Then it will influence the accuracy of DEM. The selection of matching window will influence the accuracy of DEM. There are personal error, system error, random error and so on.Before evaluating the accuracy of DEM, this paper disposes the blunder of DEM.It uses the the method of detection and rejection of the blunder which is based on the grid DEM. The result indicates that the number of blunder points is larger where the surface is higher, while the number of blunder points is smaller where the surface is flater.This paper makes use of checkpoint method and profile method to evaluate the accuracy of DEM. When using checkpoint method to evaluate the accuracy of DEM, the extracted DEM is compared with DEM of 1:50000. The author selects 20 checkpoints, and computes that RMSE is 11.86m. The author makes a conclusion: the accuracy of extracted DEM is better where the surface is flat. When using profile method to evaluate the accuracy of DEM, the author draws 8, 9, 11 section lines in X
aspect, Y aspect and arbitrary aspect respectively. And the author computes RMSE of each section line and the mean of RMSE. The author finds that the mean of RMSE of section line of different aspect is similar. And they are similar with RMSE of 20 checkpoints. They are satisfied with the accuracy of DEM of 1:100000 fundamentally. Using VirtuoZo to extract DEM is satisfied with the accuracy of DEM of 1:100000 for Jiangning whose configuration is complex and crushing, then other regions should be also satisfied with these requirements.This paper makes use of Monte Carlo method to simulate the uncertainty of DEM. It emphasizes on the generation of random field and the determination of N. By comparing various methods of the generation of random field, this paper uses random method to generate random field. This can gurantee to simulate the uncertainty of DEM in the worst case. The paper uses two kinds of RMSE to simulate the uncertainty of DEM. When RMSE is 11.86m, N is 150. When RMSE is 15.85m,N is also 150. The paper also paints the error map between simulated DEM and DEM of 1:50000, and finds that the accuracy of simulated DEM is better where the surface is flatter. That is, the simulated DEM is close to the true value where the surface is flatter.This paper improves Monte Carlo method. Considering the relation of mean and slope, the paper fits the linear function between them. By using this function as the mean of random field, the paper considers the influence of land complexity on the simulation of DEM. The paper also compares the method which has been improved with the method which is not improved, and finds that the improved method can simulate DEM whose accuracy is higher.
引文
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