基于地貌特征的数字高程模型融合方法

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。

Digital Elevation Model Fusion by Landform Characteristics

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.