# Improving Autonomous Estimates of DEM Uncertainties by Exploiting Computer Matching Asymmetries

#### Abstract

We consider the problem of estimating the vertical uncertainty in Digital Elevation Models when results from asymmetric computer matches are used. The use of asymmetric matches doubles the height estimates available for creating a fused DEM. But if the asymmetric matches are perfectly correlated the variance would not drop by a factor of $1/\sqrt{2}$ as they would for uncorrelated measurements. We present an error model that uses the observed height estimates to measure the average correlation between the asymmetric matches absent any knowledge of the true heights in the DEM. It requires at least three photographs to autonomously estimate the correlation between asymmetric pairs. Experimental results with a specific set of aerial photographs show that the correlation coefficient varies from $0.5$ to $0.9$. This demonstrates that for any algorithm used to fuse DEMs from multiple photographs a better result would be obtained by employing the extra information in asymmetric pairs.

#### Suggested Citation

Andres Corrada-Emmanuel, Brian Pinette, Andrey Ostapchenko, and Howard Schultz. "Improving Autonomous Estimates of DEM Uncertainties by Exploiting Computer Matching Asymmetries" Proceedings of the 8th Conference on Optical 3-D Measurement Techniques (2007).