Estimation of Variance and Spatial Correlation Width for Fine-scale Measurement Error in Digital Elevation Model

TL;DR

This paper introduces a novel no-reference method, adapted from image processing, to estimate the variance and spatial correlation of errors in digital elevation models without needing a reference DEM.

Contribution

It develops a multivariate BNPE-based approach to estimate local DEM error parameters, accounting for factors like stacking and epipolar line errors.

Findings

Good agreement with existing literature on ASTER GDEM2 and ALOS DEMs

Effective estimation of local error variance and autocorrelation width

Potential for extending to more predictors in remote sensing accuracy

Abstract

In this paper, we borrow from blind noise parameter estimation (BNPE) methodology early developed in the image processing field an original and innovative no-reference approach to estimate Digital Elevation Model (DEM) vertical error parameters without resorting to a reference DEM. The challenges associated with the proposed approach related to the physical nature of the error and its multifactor structure in DEM are discussed in detail. A suitable multivariate method is then developed for estimating the error in gridded DEM. It is built on a recently proposed vectorial BNPE method for estimating spatially correlated noise using Noise Informative areas and Fractal Brownian Motion. The newly multivariate method is derived to estimate the effect of the stacking procedure and that of the epipolar line error on local (fine-scale) standard deviation and autocorrelation function width of photogrammetric DEM measurement error. Applying the new estimator to ASTER GDEM2 and ALOS World 3D DEMs, good agreement of derived estimates with results available in the literature is evidenced. In future works, the proposed no-reference method for analyzing DEM error can be extended to a larger number of predictors for accounting for other factors influencing remote sensing (RS) DEM accuracy.