Dear All,

I'm experiencing some counterintuitive results from metashape run, which are worth of noting (at least from my experince and knowledge).

In detail, I attached the shots of two Metashape "variance-covariance" matrices: the first one relates to a BA in free-network mode (i.e., without any external constraint) of a theoretically strong photogrammetric network (orthogonal roll angles, convergence, redundancy, 3d scene, etc); the second one relates to a BA in extended mode (i.e., with external constraint in the form of ground control points) of a theoretically weak photogrammetric network (parallel flight lines, low redundandy, no convergence even if we are in high relief conditions).

As you can see the results are somewhat counterintuitive....

In the first case I would expect high precision and low correlations in the camera model parameters, whereas in the second case I would expect low precision and high correlations. However, the results indicate high precision and high correlations (for the first case), and low precision and low correlations (for the second case).

I noticed this behavuiour (i.e., strong networks typically reach high precision but very high correlations) in a number of other networks.

I missed some indication about the meaning or computation of the variance-covariance matrix, which led to a my mis-interpretation of obtained results, or what?

SOME DETAIL FOR THE FIRST CASE:

"camera" functional model: f, cx, cy, k1, k2, k3, p1, p2

stocasthic model: tie point accuracy set to 0.10 pix (sigma0 = 1, average key point size of about 3)

RMS image residual: about 0.50 pix (without filtering of false mathcing points)

redundacy: on average higher than 9 projections per point

SOME DETAIL FOR THE SECOND CASE:

"camera" functional model: f, cx, cy, k1, k2, k3, k4, p1, p2, b1, b2

stocasthic model: not specified

RMS image residual: about 1.6 pix (filtering details not provided)

redundacy: on average lower than 3.5 projections per point