Surface modeling of DEMs based on a sequential adjustment method

Abstract

A sequential adjustment (SA) method is employed to decrease the computational cost of high-accuracy surface modeling (HASM), and the SA of HASM (HASM-SA) is being developed. A mathematical surface was used to comparatively analyze the computing speed of SA and the classical iterative solvers provided by MATLAB 7.7 for solving the system of linear equations of HASM. Results indicate that SA is much faster than the classical iterative solvers. The computing time of HASM-SA is determined by not only the total number of grid cells but also the number of sampling points in the computational domain. A real-world example of surface modeling of digital elevation models (DEMs) with various resolutions shows that HASM-SA is averagely more accurate and much faster than the commonly used interpolation methods, such as inverse distance weighting (IDW), kriging, and three versions of spline, namely regularized spline (RSpline), thin-plate spline (TPS), and ANUDEM in terms of root mean square error (RMSE), mean absolute error (MAE), and mean error (ME). In particular, the ME of HASM-SA at different spatial resolutions is averagely smaller than those of IDW, kriging, RSpline, TPS, and ANUDEM by 85%, 83%, 83%, 53%, and 19%, respectively. The high speed and high accuracy of HASM-SA can be due to the absence of matrix inversion computation, combined with the perfect fundamental theorem of HASM.