1 research outputs found
Estimation of Variance and Spatial Correlation Width for Fine-scale Measurement Error in Digital Elevation Model
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.Comment: 15 pages, 7 figures, 3 table