  Recently, the bistatic SAR interferometry mission TanDEM-X provided a global
terrain map with unprecedented accuracy. However, visual inspection and
empirical assessment of TanDEM-X elevation data against high-resolution ground
truth illustrates that the quality of the DEM decreases in urban areas because
of SAR-inherent imaging properties. One possible solution for an enhancement of
the TanDEM-X DEM quality is to fuse it with other elevation data derived from
high-resolution optical stereoscopic imagery, such as that provided by the
Cartosat-1 mission. This is usually done by Weighted Averaging (WA) of
previously aligned DEM cells. The main contribution of this paper is to develop
a method to efficiently predict weight maps in order to achieve optimized
fusion results. The prediction is modeled using a fully connected Artificial
Neural Network (ANN). The idea of this ANN is to extract suitable features from
DEMs that relate to height residuals in training areas and then to
automatically learn the pattern of the relationship between height errors and
features. The results show the DEM fusion based on the ANN-predicted weights
improves the qualities of the study DEMs. Apart from increasing the absolute
accuracy of Cartosat-1 DEM by DEM fusion, the relative accuracy (respective to
reference LiDAR data) ofDEMs is improved by up to 50% in urban areas and 22% in
non-urban areas while the improvement by them-based method does not exceed 20%
and 10% in urban and non-urban areas respectively.
