地形特征约束下的失真DEM修复方法  

Recovery of Distorted DEM under Constraint of Terrain Feature

作　　者：周波[1,2] 徐俊波[1,2] 

机构地区：[1]安徽省智慧城市与地理国情监测重点实验室,安徽合肥230031 [2]合肥工业大学,安徽宣城242000

出　　处：《测绘通报》2017年第8期56-61,共6页Bulletin of Surveying and Mapping

基　　金：安徽省智慧城市与地理国情监测重点实验室开放性课题基金(2016-K-05Z)

摘　　要：数字高程模型(DEM)在描述地形时会存在一定程度的失真现象,对失真DEM进行修复可以有效地提高其精度及保真度。本文针对DEM在描述规则地形时存在的失真现象,提出了用地形特征约束失真DEM形态结构的失真修复方法,以规则地形中的平地结构地形为例,进行了失真修复试验。首先根据地形特征确定平地地形的DEM应为平面结构,再采用最小二乘法(LS)及随机抽样一致性算法(RANSAC)对其平面方程进行参数估计,进而得到修复的DEM数据。试验结果表明,本文所提的修复方法有效可行,修复后的DEM数据不仅提高了精度,还能充分地反映原地形的地形特征。两种修复算法的修复能力相接近,当存在大量格网点时,都能达到很好的修复效果,但RANSAC更能适应高程异常的情况,鲁棒性更好。There is a certain degree of distortion in Digital Elevation Model （DEM） depicting terrain. It is necessary of recovering distorted DEM to improve the accuracy and fidelity. In this paper, a method of recovering distorted DEM of regular terrain under constraint of terrain feature is proposed by taking a flat terrain object as an example. Firstly, the DEM of flat terrain should be a flat structure according to terrain feature, and it can be described by a plane equation. Then the expression of plane can be estimated by parameter estimation algorithms, this paper selects least square method （LS） and random sample consensus algorithm （RANSAC） to estimate it. And the recovered DEM will be obtained based on the expression. The recovery results reveal that the method is effective and feasible, and the recovered DEM not only has a higher accuracy, but also can reflect the terrain feature fully and exactly. With massive grid points, both algorithms can achieve a great recovery effect. However, RANSAC algorithm has a better robustness than LS and RANSAC can better adapt to the mutations of elevation.

关 键 词：数字高程模型 地形特征 最小二乘法 随机抽样一致性算法 失真修复 

分 类 号：P237[天文地球—摄影测量与遥感]