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

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. © 2021, Jilin University Press. All right reserved.