2.4 STRIP CONNECTION (S4) 
A great number of gross errors, in phototriangulation, comes from 
tie points. This step search for tie points errors. The detection 
procedure can start when two strips have been formed, no matter if 
the strips were obtained analytically or semi-analytically. The size 
of these errors varies in a very large interval. 
The algorithm is estabilished assuming that the form of the strips 
are invariant. So they can be brought together through a movement 
and scale. The functional model chosen is, then, the spatial 
similarity transformation: 
X X Xo 
Y|= À R| y | + | Yo (2.4.1) 
Z 2 Zo 
where X, Y, Z represent a point, say, on the upper strip and x, jy, 
z the same point on the neighbor strip down; R is the orthogonal 
rotation matrix (R = Ë (uw, 6, k)) and A, à, $4, k, Xo, Yo, Zo are the 
parameters of the transformation. 
The tie points are used, in the adjustment, to estimate the 
similarily transformation parameters. It is important a good 
distribution of “tie ^ points, to :;allow . a. rigid geometric 
transformation. The implicit adjustment model is applied, where the 
tie points coordinates X, Y, Z, x, y, z are treated as observations, 
and A, 0, $, k, Xo, Yo, Zo are the parameters. Then the residuals of 
the observations are used to evaluate the quality and detect gross 
errors in the points. 
As it is known, the least squares adjustment scaters the gross 
errors on the observations, making the detection and location 
difficult and not clear. The robust stimation was used, in the 
algorithm, to improve location of gross errors in this phase. The 
weihgt, as sugested in the literature [08], [06], [11], is defined 
in three stages: first P = I = identity matrix; second 
P 
EXP (-0,05(V/00) ^*^) (2.4.2) 
and third 
EXP (-0,05 (V/co) 2*9) (2.4.3) 
rd 
Il 
where V stands for residual and Oo? is the "a posteriori" variance 
factor. 
In the proposed algorithm, weight was chosen: 
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