1 Spatial Modelling of the Terrestrial Environment - Georeferencial

Remotely Sensed Topographic Data for River Channel Research 127
(a)
(b)
Figure 6.8 A visual comparison of applying method 1 (search radius = 20 m, tolerance =
0.5 m) and Method 2 (coarse DEM resolution = 5 m, tolerance = 1m)
of the banding was that it was in the areas where the individual DEMs overlapped with
one another. Initially, after application of the point post-processing described in section
6.4.1, the DEMs were stitched together by combining all dry points that had been deemed
acceptable, and then using bilinear interpolation. The effects of this could be explored by
comparing the surfaces interpolated from the individual DEMs where they overlapped. In
this analysis, it is not possible to determine which independent DEM surface is correct: any
discrepancies imply that either or both of the datasets are in error. The results are shown
in Plate 3(a) and in schematic in Plate 3(b). The effect of the patterns shown in Plate 3(a)
and (b), after bilinear interpolation of the full dataset, is shown in schematic in Plate 3(c).
Systematic error in both surfaces increases the mottling with distance from the midpoint
of the overlap (cf. Plate 3(a)); downstream data points cause more mottling upstream with
distance from the centre of the overlap and upstream data points cause more mottling
downstream with distance from the centre of the overlap. The problem here is that we
do not know which if either surface is in error. It is in the areas of overlap that camera
calibration tends to have greatest effect as the overlap areas will be based upon image
pixels that are furthest from the principal point of symmetry, and where the precision of the
camera calibration parameters will be poorest. The geometry of stereo-imagery in areas of
DEM overlap is such that both DEMs will involve image data that is slightly away from
the principal point.
6.5 Explanation of Errors
Too many of the attempts to manage error in DEMs are based upon an incomplete understanding
of the nature of that error. A classic example of this is the application of smoothing
functions, such as those based upon a 3 × 3 variance filter, as these simply propagate error
from incorrect points into adjacent points without necessarily removing all of the error
in the incorrect points. One of the crucial steps in an error analysis is the explanation of
the causes of that error, so that the data collection process can be improved and the error