基于地貌特征的数字高程模型融合方法

doi:10.12082/dqxxkx.2018.170554

地球信息科学学报 ›› 2018, Vol. 20 ›› Issue (7): 895-905.doi: 10.12082/dqxxkx.2018.170554

• 地球信息科学理论与方法 • 上一篇下一篇

基于地貌特征的数字高程模型融合方法

孟伟¹(), 李润奎^1,², 段峥³, 徐江⁴, 宋现锋^1,^2,^*()

1. 中国科学院大学资源与环境学院, 北京 100049
2. 中国科学院地理科学与资源研究所, 北京 100101
3. 慕尼黑工业大学土木、地学与环境工程系, 慕尼黑 80333
4. 广西融科科技有限公司, 柳州 545000

收稿日期:2017-11-21 修回日期:2018-02-06 出版日期:2018-07-20 发布日期:2018-07-13
通讯作者: 宋现锋 E-mail:18811152566@163.com;xfsong@ucas.ac.cn
作者简介:
作者简介：孟伟(1994-),男,硕士生,研究方向为空间数据挖掘。E-mail：18811152566@163.com
基金资助:
广西科技重大专项项目（桂科AA17202033）;国家重点研发计划项目（2017YFB0503702）;国家重点研发计划项目（2017YFB-0503605）

Digital Elevation Model Fusion by Landform Characteristics

MENG Wei¹(), LI Runkui^1,², DUAN Zheng³, XU Jiang⁴, SONG Xianfeng^1,^2,^*()

1. College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
2. Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
3. Department of Civil, Geo and Environmental Engineering, Technical University of Munich, Munich 80333, Germany
4. Guangxi Rongke Technology Company Limited, Liuzhou 545000, China

Received:2017-11-21 Revised:2018-02-06 Online:2018-07-20 Published:2018-07-13
Contact: SONG Xianfeng E-mail:18811152566@163.com;xfsong@ucas.ac.cn
Supported by:
Guangxi Science and Technology Major Project, No.GK-AA17202033; National Key Research and Development Program of China, No.2017YFB0503702; National Key Research and Development Program of China, No.2017YFB0503605

摘要/Abstract

摘要：

数字高程模型（Digital Elevation Model, DEM）是一种至关重要的空间信息,广泛应用于各行各业。其中,ASTER GDEM与SRTM几乎覆盖了全球陆域,为地学研究提供了非常实用的高程数据支撑,但是由于二者传感器采集数据原理的不同,使得高程数据在不同地貌条件下的高程精度亦存在程度不一的误差。本文提出了一种新型的基于地貌特征的DEM融合方法,使得融合GDEM与SRTM后的DEM数据,消除了地貌特征的影响、显著地提高了DEM质量。该方法主要分为地理配准和高程融合2个步骤：①基于河流线对等线性地貌特征的位置数据,构建了GDEM与SRTM的水平偏移相关的误差评价函数,采用多级网格搜索法求得DEM间的水平偏移距离,实现对DEM的配准;②按照DEM高程值在不同地貌单元及边界线附近的高程变化特征,建立地貌分区的高程融合模型来融合两种地理配准后的DEM高程,尤其是实现了地貌单元边界线附近的高程平滑过渡。本文以怀柔北部地区为实验区,以1:5万地形图为参考,对2种DEM数据进行融合,统计结果表明：① 融合DEM在各地貌单元的误差均显著下降,地形表达较之融合前更加精确;② 高程差呈现正态分布,明显区别于融合前DEM不对称的多峰分布形态,说明地貌影响被有效地剔除;③ GDEM和SRTM数据的精度对坡度有较大依赖性,融合后DEM的精度在不同坡度范围下均优于GDEM和SRTM,显著降低了融合前DEM对坡度的依赖程度;④ 在不同坡向下,GDEM和SRTM的RMSE取值波动较大,融合DEM的RMSE取值在各方向表现稳定,高程精度较GDEM和SRTM有显著提高。

关键词: 数字高程模型, 河流, 地貌单元, 地理配准, 高程融合

Abstract:

The digital elevation model (DEM) is considered as a source of vital spatial information and is widely used in many fields. The ASTER GDEM and SRTM provide almost global coverage and offer practical elevation data for geography research. However, due to the differences of remote sensing mechanism, GDEM and SRTM datasets present different accuracies on same landform units. A novel elevation data fusion approach is proposed in this paper, which eliminates the impact of landform characteristics on two DEMs and significantly improves the quality of fused DEM. This method focuses on two steps, geo-referencing and elevation fusion. An objective function of errors representing the summary of horizontal shifts between two DEMs by referring to stream link pair is first proposed, and correspondingly a multilevel grid search method is suggested to calculate the optimal horizontal offsets between DEMs. Two geo-referenced DEMs are then fused using regression models over different landform units and moreover the elevations nearby the boundaries of two units are specifically treated using a weighed non-linear regression method. This approach was tested in the area of northern Huairou using a 1: 50 000 topographic map. The statistics show that: (1) the RMSE of fused DEM decreases significantly in all landform units, and the representation of terrain is more accurate than GDEM and SRTM; (2) The difference of the elevation points between fused DEM and referenced topographic map also illustrates a normal distribution, which is obviously different from the asymmetric multi-peak distributions of two raw DEMs, indicating that the topographic effects have been effectively eliminated; (3) The accuracy of fused DEM is superior to that of GDEM and SRTM under different slope ranges, meanwhile, the influence of slope factor on the elevation accuracy of DEM is obviously reduced after fusion; (4) The RMSEs of GDEM and SRTM vary greatly with different aspects, while the RMSE of fused DEM keeps homogenous in almost all aspects, and the elevation accuracy of the fused DEM is also significantly improved in comparison with GDEM and SRTM.

Key words: DEM, river, landform, geo-reference, elevation data fusion

孟伟, 李润奎, 段峥, 徐江, 宋现锋. 基于地貌特征的数字高程模型融合方法[J]. 地球信息科学学报, 2018, 20(7): 895-905.DOI:10.12082/dqxxkx.2018.170554

MENG Wei,LI Runkui,DUAN Zheng,XU Jiang,SONG Xianfeng. Digital Elevation Model Fusion by Landform Characteristics[J]. Journal of Geo-information Science, 2018, 20(7): 895-905.DOI:10.12082/dqxxkx.2018.170554

图/表 19

图1

图2

图3

图4

图5

表1

图6

图7

表2

表3

融合DEM的回归方程参数"

地貌分类	$a 0$	$a 1$	$a 2$
组1（I-IV）	28.6760	0.4984	0.5277
组2（V）	-8.4269	0.6537	0.3269
组3（VI）	0.7575	0.3109	0.7557

表3

表4

表5

表6

表7

图8

图9

图10

图11

图12

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DEM类型	配准前/m			配准后/m
	配准前/m			河流线对法			交叉点法
	总体	山区	平原	总体	山区	平原	总体	山区	平原
GDEM	129.05	104.31	203.97	129.02	104.27	203.96	160.18	103.21	236.81
SRTM	161.28	129.17	197.11	137.93	107.05	194.71	168.45	132.05	202.71

高程差中位数/m	山脊（I）	坡肩（II）	背坡（III）	坡脚（IV）	沟谷（V）	平原（VI）
GDEM	-42.68	-40.68	-38.68	-39.84	9.68	-8.68
SRTM	-35.18	-31.34	-25.68	-26.68	15.68	0.32

高程缓冲区（A-B）	A侧缓冲宽度b₁	B侧缓冲宽度b₂
组1-组2	90	0
组1-组3	390	510
组2-组3	0	60

	DEM 类型	整个地区	地貌分组1 （I-IV)	地貌分组2（V）	地貌分组3（VI）
融合前	GDEM	40.89	44.96	19.35	13.84
	SRTM	34.64	38.64	26.65	7.59
回归后	GDEM	18.38	17.13	45.01	10.39
回归后	SRTM	20.44	19.15	51.94	8.43
融合后	GDEM+SRTM	14.74	16.25	17.69	7.54

	DEM 类型	整个地区	地貌分组1 （I-IV）	地貌分组2 （V）	地貌分组3 （VI）
融合前	GDEM	-35.33	-42.28	10.05	-10.76
融合前	SRTM	-26.73	-34.38	17.68	-1.29
回归后	GDEM	-0.57	-4.38	37.57	6.58
	SRTM	-0.94	-5.12	43.52	6.28
融合后	GDEM+SRTM	-1.56	-1.67	1.75	-0.87

基于地貌特征的数字高程模型融合方法

Digital Elevation Model Fusion by Landform Characteristics

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	DEM 类型	整个地区	地貌分组1 （I-IV）	地貌分组2（V）	地貌分组3（VI）
融合前	GDEM	36.26	42.35	13.97	9.28
	SRTM	28.62	33.49	21.14	4.04
回归后	GDEM	13.75	13.39	38.64	9.07
	SRTM	14.23	14.26	44.22	7.68
融合后	GDEM+SRTM	10.19	11.32	12.24	3.91

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