Modeling and estimation of the spatial variation of elevation error in high resolution DEMs from stereo-image processing

The spatial variability of elevation errors in high-resolution digital elevation models (DEMs) derived from stereo-image processing is examined. Error models are developed and evaluated by examining the correlation between various DEM parameters and the magnitude of the observed DEM vertical error. DEM vertical errors were estimated using a dataset of more than 51 000 points of known elevation obtained from a kinematic global positioning satellite (GPS) ground survey. Elevation variability and the quality of the stereo-correlation match over small spatial scales were the dominant factors that determined the magnitude of the DEM error at any given location. The error models are strongly correlated with the magnitude of the DEM vertical error and are shown to adequately represent the full range of the observed error. The error models are used to estimate the magnitude of the vertical error for every point in the DEMs. The models are then used to predict the overall error in the DEMs. The results demonstrate that the error models can accurately quantify and predict the spatial variability of the DEM error.