Praca Dyplomowa - Photogrammetry and Remote Sensing - AGH

THEORETICAL BACKGROUND
RMSE – root mean square error – RMS error of adjusted observations with respect to mean
of adjusted observations
√ ∑( ̅)
20
(2.19)
To orient two images or more, tie points have to be defined. These points may be natural
points which are visible on both pictures. However, they may be marked before. These points
may be defined in a specific shape – coded points, where every software may have different
pattern. In some cases the points (or at least few of them) should have known coordinates
(XYZ), for orientation to global coordinate system, but for close range and in the project it is
not necessary. Points should be arranged around the object and/or could be on the object.
Picture should cover as best (as biggest area as possible) the object with marked points around
it. Referenced points should not be located in a common plane, this situation may give errors.
Depending on methods (algorithm, transformation etc.), or step in orientation different
amount of control points is needed. In Figure 2.9 is specification of point determination on
each field.
Figure 2.9 Methods and data flow for orientation and point determination (based on
(Luhmann et al., 2006))
2.2.13 Matching types of digital images
Matching is a process in computer vision that matches pictures to each other. These are
methods which are used to identify and uniquely match identical objects features.
Feature-based matching
This is usually the first step and it is mainly identifying as many corresponding features as
possible in all images. Specifying area of interest or any other information can be used to limit
the search space and minimalize mismatches. This matching is done by extracting distinct