AI-GEOSTATS: filling 'holes' in high-res DEM with low-res data
- Hi everyone:
I have a high-res DEM (SRTM 3-arcsec) which has local 'blackouts'/'holes',
and a low-res DEM (DTED0) of the same area, which is complete. Many of the
holes are too large for meaningful results with simple interpolation
When I merge the two DEMs to fill in the missing data in the high-res DEM,
the generalization of the low-res DEMs elevations leads to 'troughs' (i.e.,
cliffs along the edges) instead of the desired smooth fill-ins.
My idea (taken from GIS literature) is to improve the low-res mesh prior to
merging by using the available high-res data to predict the true elevation
in the areas with holes. My question is, which would be the best available
procedure - local or global? simple (e.g., differencing) or complex (e.g.,
regression models)? I have done a few trials with global approaches but
they didn't provide satisfactory results.
Any advice or pointers are appreciated.
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