Hi,

as far as I have understood PS follows in general the same principles as Bundler - the SfM package by Noah Snavely - but PS comes with a more sophisticated implementation of the algorithms. It all starts with automatic detection and matching of tiepoints in order to find homologous points in the images. This is normally done by some kind of feature extraction mechanism (SIFT, SURF or other). The potential tiepoint candidates are then filtered for outliers or error-prone points by statistical means and the resulting best matches are fed into the scene reconstruction process which uses the principles of epiolar-geometry to determine the relative and interior orientation of the cameras. In Bundler this is a sequential process that starts with one pair of images and succsively adds one image after the other.

So, when you have bad luck and your choosen start-image-pair leads to a bad paramaters - for what reason soever -, you will not get happy with the rest.

Therefore in Bundler you can define which images should be taken as initial pair for the calculations.

But this must not also be valid for PS.

The Problem with the PS docu is, that it does not explain what is going on behind the scenes. But when one is concerned with aerial surveying it is absolutely necessary to know what the software is doing behind it's nice GUI.

The reason is simple: You must give a prove to your customer for the accuracy of your calculations. But this seems to be difficult with Photoscan as the other discussion about accuracy show.

Cheers

p.S. I made a similar "camera in Burst mode Test" with a Nikon D50, 55mm focal length, altitude aGR 560 metres, speed aGr 100km/h. It tuned out, that image base distance was to small, or in other words the images had partially too much endlap.

For classical stereoimages (binocular human vision) there is a formular to calculate the optimal image base distance :

nearest point distance * far point distance * 0.02

image base distance = ------------------------------------------------------------

far point distance - nearest point distance

Or you can alternatively use the rule of thumb: Image base distance = 1/30 of nearest point distance