Underwater photogrammetry
HN 116 — 06/2020
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Fig. 5: Coloured dense point cloud of starboard side
132,516 tie points and used for all further process
ing steps ©,g...4). Using unprocessed imagery, only
440 Images were aligned with a significantly small
er covered area on the ship hull. Thus, for further
processing, the enhanced imagery was used.
It is observable that the exterior orientations
form a long stretched trajectory along the ship
hull, starting on the right hand side. On the far left
side, the distance to the object Increases signifi
cantly, resulting in several images that could not
be aligned due to turbid visibility and thus Insuf
ficient valid observed tie points In the Images.
The RMS (root mean sguare) of the reprojection
error was 1.50 px, eguivalent to 1.5 mm in object
space, assuming an average acgulsltlon distance of
1 m. This rather low accuracy for photogrammetric
applications probably originated from high turbid
ity and low contrast in the underwater imagery
and a long stretched object that posed a dead
reckoning problem. However, this postulated ac
curacy only refers to the internal accuracy of the
image bundle after adjustment. An exterior ac
curacy by comparing length measurement errors
or cloud-to-cloud distances to a reference is not
possible, as only a monocular system is used. Thus,
neither an independent absolute scale can be pro
vided nor was it possible to position a static refer
ence object near the wreck with which to control
results. However, if no significant deviations exist in
a cloud-to-cloud comparison, it can be concluded
that the photogrammetric point cloud achieves at
least the same accuracy as the MBES data does,
which is to be expected.
Using the aligned sparse point cloud, dense im
age matching was performed. According to the
work of Remondlno et al. (2013), a method simi
lar to semi-global matching (Hirschmuller 2008)
is used, though this has not been confirmed by
Agisoft. Using the integrated algorithm, a dense
point cloud consisting of 12.3 million points is cre
ated using »High« quality settings in the software.
Processing, Including the calculation of depth
maps, took approximately four hours on an Intel
Core ¡7 with 16 GB RAM. The resulting point cloud
is shown in .Fig.©.
3 Data fusion
F.ig..6 shows the flow chart of the data fusion in
cluding all preprocessing steps before registration.
Both photogrammetric and MBES data sets were
adjusted Individually in their respective coordinate
systems and then combined by point cloud analy
sis methods. Absolute scaling was provided by
the MBES data, thus providing absolute scale for
photogrammetric data as well. In a practical solu
tion and in order to control results, an independ
ent scaling would be desirable by e.g. using a sec
ond camera for stereo analysis. Two independent
point clouds can then be fused, deviations in scale
evaluated, and possible errors identified.
Firstly, the two point clouds were coarsely
aligned by selecting salient points. For that, the
front tip of the wreck's bow and other salient points
on the top of the wreck's starboard side were
manually measured in the imagery and 3D coordi
nates determined. This process could be automa
tised by automatically identifying salient features
in a resulting point cloud or applying a reference
Fig. 6: Data processing and fusion to combine data from both photogrammetric analysis and MBES