  This paper describes an investigation of the source of geospatial error in
digital surface models (DSMs) constructed from multiple satellite images. In
this study the uncertainty in surface geometry is separated into two spatial
components; global error that affects the absolute position of the surface, and
local error that varies from surface point to surface point. The global error
component is caused by inaccuracy in the satellite imaging process, mainly due
to uncertainty in the satellite position and orientation (pose) during image
collection. A key result of the investigation is a new algorithm for
determining the absolute geoposition of the DSM that takes into account the
pose covariance of each satellite during image collection. This covariance
information is used to weigh the evidence from each image in the computation of
the global position of the DSM. The use of covariance information significantly
decreases the overall uncertainty in global position. The paper also describes
an approach to the prediction of local error in the DSM surface. The observed
variance in surface position within a single stereo surface reconstruction
defines the local horizontal error. The variance in the fused set of elevations
from multiple stereo pairs at a single DSM location defines the local vertical
error. These accuracy predictions are compared to ground truth provided by
LiDAR scans of the same geographic region of interest.
