Journal of Jilin University(Engineering and Technology Edition) ›› 2021, Vol. 51 ›› Issue (3): 1073-1080.doi: 10.13229/j.cnki.jdxbgxb20200786

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Construction and quality evaluation of digital elevation model based on convolution grid surface fitting algorithm

Wei-gang ZHU1(),Chao ZHU1,Ya-qiu ZHANG1,Hai-bin WEI2()   

  1. 1.College of Prospecting and Surveying Engineering,Changchun Institute of Technology,Changchun 130022,China
    2.College of Transportation,Jilin University,Changchun 130022,China
  • Received:2020-10-15 Online:2021-05-01 Published:2021-05-07
  • Contact: Hai-bin WEI E-mail:519660365@qq.com;weihb@jlu.edu.cn

Abstract:

In view of the complex structure of the current commonly used filtering algorithms, low work efficiency and single Digital Elevation Model (DEM) results evaluation methods, this paper proposes a moving surface fitting DEM filtering algorithm based on spatial grid technology and convolution kernel calculation (CGS) and a new method of DEM quality evaluation based on computer graphics. The results of theoretical research and engineering practice show that the CGS algorithm can better filter out various non-ground points such as vegetation and buildings than the manual intervention of the TIN algorithm. The gray-level co-occurrence matrix, autocorrelation function, image signal-noise ratio, The improved local variance algorithm of Gaussian waveform extraction based on Canny operator are used to compare and analyze the results of DEM. It is found that the DEM image generated by the CGS algorithm has a uniform and smooth texture, which highlights the detailed features of various terrains and can effectively suppress the generation of salt-and-pepper noise. The filtering algorithm and evaluation system proposed in this paper are forward-looking in theory, scientific and repeatable in method, which can be applied to actual project engineering and have strong practical significance.

Key words: road engineering, convolution grid surface fitting algorithm, computer graphics quality assessment, point cloud filtering, digital elevation model(DEM)

CLC Number: 

  • U41

Fig.1

Part of the original point cloud in the study area"

Fig.2

Neighborhood of the seed point"

Fig.3

CGS filtering algorithm workflow"

Fig.4

Digital elevation model"

Table 1

Accuracy evaluation of digital elevation model"

算法最小高差/m最大高差/m

Mean

/m

STD

/m

RMSE/m时间复杂度/h
TIN-0.4500.358-0.0410.0900.0995
CGS-0.3570.336-0.0170.0930.0941

Fig.5

Contour lines under the digital orthophoto map"

Table 2

Calculation results of gray co-occurrence matrix"

算法纹理相关性纹理逆差分矩纹理二阶矩信息熵
TIN2.604 11.685 7387 9305.151 1
CGS3.087 12.262 3910 2904.994 7

Fig.6

Three-dimensional display of autocorrelation function results"

Table 3

Signal-noise ratio of DEM results"

算法通道1通道2通道3
TIN2.629 876.597 31.465 23
CGS11.525 120.088 63.077 11
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