Ability to detect and locate gross errors on DEM matching algorithm

Abstract

Digital elevation model (DEM) matching techniques have been extended to DEM deformation detection by substituting a robust estimator for the least squares estimator, in which terrain changes are treated as gross errors. However, all existing methods only emphasise their deformation detecting ability, and neglect another important aspect: only when the gross error can be detected and located, can this system be useful. This paper employs the gross error judgement matrix as a tool to make an in-depth analysis of this problem. The theoretical analyses and experimental results show that observations in the DEM matching algorithm in real applications have the ability to detect and locate gross errors. Therefore, treating the terrain changes as gross errors is theoretically feasible, allowing real DEM deformations to be detected by employing a surface matching technique.

Co-registration and residual correction of digital elevation models: a comparative study

“…Their tests showed that the NK method achieved similar or better accuracy compared to many non-analytical methods, such as the grid search method (Berthier et al, 2007), the LS3D method (Gruen and Akca, 2005), and the subwatershed-based method (Li et al, 2017), and therefore the NK method was recommended for practical applications due to the less computational effort (Paul et al, 2015). This work focuses on the comparison of two analytical algorithms, the NK method and the RT method, which have been widely used in the cryosphere (Geyman et al, 2022;Hugonnet et al, 2021;Maurer et al, 2019) and photogrammetry (Aguilar et al, 2012;Zhang et al, 2010) studies, respectively. The characteristics of the two methods are summarized in Table 6, and the theoretical connections and differences between them are discussed in the following.…”