Generation of High Spatial Resolution Terrestrial Surface from Low
  Spatial Resolution Elevation Contour Maps via Hierarchical Computation of
  Median Elevation Regions
Geetika Barman B. S. Daya Sagar
We proposed a simple yet effective morphological approach to convert a sparse
Digital Elevation Model (DEM) to a dense Digital Elevation Model. The
conversion is similar to that of the generation of high-resolution DEM from its
low-resolution DEM. The approach involves the generation of median contours to
achieve the purpose. It is a sequential step of the I) decomposition of the
existing sparse Contour map into the maximum possible Threshold Elevation
Region (TERs). II) Computing all possible non-negative and non-weighted Median
Elevation Region (MER) hierarchically between the successive TER decomposed
from a sparse contour map. III) Computing the gradient of all TER, and MER
computed from previous steps would yield the predicted intermediate elevation
contour at a higher spatial resolution. We presented this approach initially
with some self-made synthetic data to show how the contour prediction works and
then experimented with the available contour map of Washington, NH to justify
its usefulness. This approach considers the geometric information of existing
contours and interpolates the elevation contour at a new spatial region of a
topographic surface until no elevation contours are necessary to generate. This
novel approach is also very low-cost and robust as it uses elevation contours.