000			02157nam a22002417a 4500
003			ZW-GwMSU
005			20221128144924.0
008			221128b |||||||| |||| 00| 0 eng d
040			_aMSU _cMSU _erda
100	1		_aChen, Chuanfa _eauthor
245	1	0	_aSurface modeling of DEMs based on a sequential adjustment method _ccreated by Chuanfa Chen, Yanyan Li and Tianxiang Yue
264			_aShanghai : _bTaylor and Francis, _c2013.
336			_2rdacontent _atext _btxt
337			_2rdamedia _aunmediated _bn
338			_2rdacarrier _avolume _bnc
440			_vVolume , number ,
520			_aA 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.
650			_asurface modeling
650			_aDEM
650			_ainterpolation
856			_uhttps://doi.org/10.1080/13658816.2012.704037
942			_2lcc _cJA
999			_c160640 _d160640