Geometry: 
Resampling has a twofold effect: It degrades spatial resolution and 
reduces locational (measurement) accuracy (Gp = A:3.46). Gridding 
of the external information into image-raster does not necessarily 
cause significant loss (but it is quantifiable). Geometric correc- 
tions for deterministic errors can be accurate enough. Locational 
errors after pre-processing can be analyzed by means of statistical 
tools. 
111,4 Image match 
At matching, pictorial and geometric data are merged, which makes 
theoretical assessment of matching accuracy very complex. The cor- 
responding quality indicators depend on the matching technique [4]. 
In the case of a sequential search the common indicators are the 
maximum of similarity assessment function (i.e., peak of correlation 
function) and its maximum slope (at the point of inflection). 
Matching accuracy depends also on the step size between successive 
trials, and, further, whether or not a curve is fitted to discrete 
points (i.e., results of trials) and the maximum is determined by 
calculation. 
If the least squares fit is applied, then the quality of match is 
indicated by the descrepancies in intensity [1] or in location [3], 
and the corresponding standard errors. 
Overall accuracy of matching can be assessed from the statistics of 
single matches involved, e.g. hystrograms and standard errors. 
If resampling is applied iteratively during matching, image data are 
degraded gradually, which can affect adversely the matching accura- 
cy. 
Reliability depends strongly on the input images, matching strategy, 
acceptance threshold and the a-priori exclusion of anomalous 
regions. Reliability is quantifiable in statistical terms (e.g., as 
percentage of successful matches). 
Time efficiency is not essential in off-line systems, though it is 
not negligible. It can be expressed by cycle-times per trial, per 
match, per full patch, and/or per full model. 
  
  
111.5 Parallax data 
Parallaxes are the gemometric by-product of image matching [4]. The 
corresponding quality indicators are parallax accuracy of individual 
points, parallax fidelity of a neighbourhood (i.e. a parallax "pro- 
file" or a "surface"), reliability, and time-efficiency. 
Parallax accuracy is the most important criterion for performance 
assessment. It depends on the accuracy of matching segments and on 
definition of representative conugate points in the segments (annex 
3), geometric distortions (caused by model deformation), and on 
thresholding at data compression rot 
Indicators for parallax accuracy are standard parallax error 
(annex 6) and error distributions (spatial, statistical). 
Accuracy can be upgraded by collective processing of local parallax 
data. The fidelity of such data (e.g., a parallax profile or sur- 
face) depends on the interval between the adjacent (representative) 
points, overlap of the adjacent image (target) segments, and the 
smoothing filter (if applied). 
348