The quality of the block parameters strongly depends on the size and
number of gross errors present in the data. The On-Line approach,
estabilished on the basis of an analytical plotter system has
contributed significantly in cleaning the data as it ds. collected.
The Step-by-Step method has established a progressive refining in
terms of size of remaining gross errors. Both approaches seem to be
of great value, as used together with Data Snooping and Robust
Estimation. The combination of these techniques has showed
efficiency in improving reliability and in saving processing time
[06].
The role of the On-Line, in avoiding the cumulative amount of gross
errors in the data is relevant. Its demand of an Analytical Plotter
is still a difficulty for many companies that have only conventional
equipments.
This research investigated a set of algorithms and programs to
overcome the demand of an Analytical Plotter, to assess the data
progressively, as it is observed in a conventional enviroment. The
Step-by-Step approach is combined in the system, to detect larger
errors first, but is used also in a progressive way in time. On the
refined data, available at a certain phase, least squares adjustment
was performed and Robust Estimation was applied in some phases to
improve detection and location of remaining errors.
The choices of the functional mathematical models at the detection
phasis were based not on an optimal fitting, but on the principle of
minimum parameterization, so that the rigid model did not fit any
existing blunder, improving the evidence of any gross error present
at the observations involved at the adjustment.
The system was called a "quasi on line" approach because it may
start as soon as two photogrammetric unities are measured, and
finish the cleaning of data soon after the last unity of the clock
is measured. Then one, two or at most three executions of a block
aerotriangulation are enough to conclude the adjustment of the
block. The completely off line approach frequently requires from
four to seven executions. It is well know that the difficulties of
detection and identification affect also the reliability.
2. PROPOSED ALGORITHMS
Two main treatments are considered: ay the first when the
photogrammetric unity for measurements is the photo; and b) when the
unity for observations is the stereomodel. In the first case, the
Observed data enter the progressive filtering and go through the 6
steps: Si, windowing of photocoordinates; S2, active lenght
invariant transformation of corresponding images; S3, analytical
aerial levelling; S54, strip connection, S5 revision of coding; and
S6, control treatment. In the second case, the steps S1, S2 and $3
are substitued by step Sc, strip formation from independent models.
Each one of the above steps involves little processing effort and
the volume of data is also small. The algorithms of the first step
are simpler than the last ones. They detect only very large errors,
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