结合NASA DEM和AW3D30 DEM的太原市DEM数据融合

doi:10.13474/j.cnki.11-2246.2022.0331

测绘通报 ›› 2022, Vol. 0 ›› Issue (11): 90-95.doi: 10.13474/j.cnki.11-2246.2022.0331

结合NASA DEM和AW3D30 DEM的太原市DEM数据融合

刘娇, 赵尚民

太原理工大学矿业工程学院, 山西太原 030024

收稿日期:2022-07-12 修回日期:2022-09-22 发布日期:2022-12-08
通讯作者: 赵尚民,E-mail:zhaoshangmin@tyut.edu.cn
作者简介:刘娇(1998-),女,硕士,主要从事DEM数据融合与质量改善。E-mail:liujiao0320@163.com
基金资助:
国家自然科学基金面上项目(42271432);山西省自然科学基金(201901D111098)

DEM fusion based on NASA DEM and AW3D30 DEM in Taiyuan

LIU Jiao, ZHAO Shangmin

College of Mining Engineering, Taiyuan University of Technology, Taiyuan 030024, China

Received:2022-07-12 Revised:2022-09-22 Published:2022-12-08

摘要/Abstract

摘要： 本文首先以DEM数据为例,将输入数据与参考数据配准至同一像素位置,然后分别将均方根误差和标准差作为参考指标,在不同坡度等级的区域内,通过权重系数从0至1的遍历探寻最佳加权融合系数,从而确定融合方案并进行NASA DEM与AW3D30 DEM的数据融合,最后对融合效果进行定量评价。结果表明:配准前,NASA DEM沿x、y、z方向的位移分别为-2.65、2.41、0.60m,AW3D30 DEM位移分别为1.04、7.51、-3.33m;配准后,原始数据各项误差均减小,且NASA DEM的系统误差基本消失。融合DEM相较于NASA DEM,平均误差和均方根误差分别减小了25.0%和36.8%;对于AW3D30 DEM,误差降幅分别为86.5%和13.2%。

关键词: NASA DEM, AW3D30 DEM, 最小二乘法配准, 加权融合, 太原市

Abstract: In this paper, taking DEM data as an example, the input data and the reference data are firstly aligned to the same pixel position, then the root mean square error and standard deviation are taken as reference indicators, and the best weighting fusion coefficients are explored by the traversal process of weighting coefficients from 0 to 1 in the regions of different slope classes. To determine the fusion scheme and perform the fusion of NASA DEM and AW3D30 DEM, and finally evaluate the fusion effect quantitatively. The results show that before the alignment, the displacements of NASA DEM along x, y, and z directions are -2.65, 2.41, and 0.60m, and the displacements of AW3D30 DEM are 1.04, 7.51, and -3.33m; after the alignment, all the errors of the original data are reduced, and the systematic errors of NASA DEM disappear. Compared with the NASA DEM, the ME and RMSE of the fused DEM are reduced by 25.0% and 36.8%; for the AW3D30 DEM, the error reduction is 86.5%, and 13.2%.

Key words: NASA DEM, AW3D30 DEM, least squares alignment, weighted fusion, Taiyuan

中图分类号:

刘娇, 赵尚民. 结合NASA DEM和AW3D30 DEM的太原市DEM数据融合[J]. 测绘通报, 2022, 0(11): 90-95.

LIU Jiao, ZHAO Shangmin. DEM fusion based on NASA DEM and AW3D30 DEM in Taiyuan[J]. Bulletin of Surveying and Mapping, 2022, 0(11): 90-95.

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结合NASA DEM和AW3D30 DEM的太原市DEM数据融合

DEM fusion based on NASA DEM and AW3D30 DEM in Taiyuan

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