Aerial Triangulation 2D Block Adjustment

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Aerial Triangulation 2D block adjustment in Noobeed offers 3 options of mathematic model, namely conformal (4 parameters), affine (6 parameters), and 2nd polynomial (12 parameters).  All include in the same class, "AT_2D".  The following are some of its beauty that might be hardly to find in other programs.

• It is very easy to assign a weight to an observation, either photo coordinate or ground control coordinates, by adding a standard deviation next to the point.  Points without a standard deviation will be assigned a default value, which can be as well specified by the user.
• To reject any observation, just put a slash, "/" , in front of that line.  In fact, the slash sign turns that line to a comment line.  Thus, comment lines in an observation file is possible by this technique.

Data and Output

        The photo coordinates measurement data are in the file "obs_xpyp.txt", the same data set as in bundle block adjustment.  See the example of Bundle Block Adjustment.

        The ground control coordinates data are in this file, "obs_gcp_xy.txt".

        These are really all you need.  Now it's time to do data processing.

        It is always possible to type interactively on the screen.  However, writing a program is also a good choice, like this.

set path "c:\WHEREVER_YOUR_DATA_ARE"

Blk = AT_2D()

/ this is default values of SD (image rectangular coordinates)

Blk.sd_xpyp() = 0.030

Blk.loadobs("obs_xpyp")

Blk.loadgcp("obs_gcp_xy")

Blk.adjust("at_2d_out.txt", "poly2nd")

Please notice the option of adjustment in the last statement, it is a 2nd order polynomial transformation.  Other options available are "conformal" and "affine".

Type the above program, using a text editor, then save it in a file, e.g. "AT.prg".  Then load it, and run it.

->load "AT.prg"

->run

Here is the result, "at_2d_out.txt".  Please be advised that the adjustment results might not be as good as those come from a bundle block block adjustment, which is the real correct model of this data set.  The value of the standard deviation of unit weight, a unit-less parameters, tells how good the model fits the data.  This is a good example to appreciate the extreme accuracy of the bundle block adjustment mathematic model.

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