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Section 6.4 provides a comparison of algorithms with varying error magnitudes.
We see that the lower left quadrant exhibits very low error magnitudes, typically below 2%.
See Section 5.2 for discussion regarding the impact of these error magnitudes.
A strong association of aspect with SRTM error was noted, with positive error magnitudes peaking for aspects oriented to the northwest and negative error magnitudes peaking for slopes facing southeast.
When trained with the optimal thermographic data, the RMS error magnitudes from these model predictions were consistently below 3%.
Given the data lineage, a cylindrical uncertainty model was chosen with quantitative error magnitudes in both horizontal and vertical directions.
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RME is the relative error magnitude.
The error magnitude variance is taken as 10-3 so that, probabilistically, only small errors are superimposed.
Similar studies do not mention the random error magnitude [16, 24].
However, this method had the same error magnitude as the global minimization.
Fig. 14 Average registration errors for ground-truth points of all successfully registered image pairs (ascending order of error magnitude).
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