Another improvement to image quality was made while
determining how to handle the overlapping region of two im-
ages during the stitching process. The overlapping region of
the transformed, paired images was determined by selecting
the image which was visually sharper. This was achieved by
comparing the magnitude of the gradient image and selecting
the higher magnitude image to lie on top of the lower magni-
tude image. The primary advantage to this method of han-
dling the overlapping regions is that it minimizes error that is
accumulated during each iteration, due to resampling.
Using video of the
ROV
grasping the rope and frames ex-
tracted from the video, we began calculating the diameter of
the rope. This feature was selected because it could be used as
a scale marker for the debris field due to its consistent, man-
made geometry. The frames were then stitched together into
one mosaicked image with the rope spanning the length of
the debris field in view. We could then relate the size of other
objects back to the rope and produce meaningful measure-
ments and ratios. After the proposed pieces of wreckage were
measured we compared the
CAD
models to historical photos of
the aircraft and a historical photo of rope adjacent to Amelia
Earhart’s aircraft. In the historical photograph of the rope
adjacent to Earhart’s aircraft, and using the same methods
described above, the rope was measured in six locations and
calculated to have an average diameter of 16.26 mm with a
standard deviation of 1.02 mm, which is consistent with the
average diameter of 16.25 mm observed across the three inde-
pendent measurements made from the underwater video (
ROV
claw, near front landing gear, near rear landing gear). The
rope material has not yet been identified, and any age-related
swelling of the rope is currently unknown. This methodology
could be applied to other retrospective video analyses where
an inadvertent, man-made scale marker can be used to ratio-
metrically compare purported objects of known dimensions.
Differentiating man-made objects from protrusions of the
seabed is also an important step in identifying wreckage. The
debris field is located on a steep incline and was searched at a
depth of 150 to 300 m. At the depth of the debris field, known
as the dysphotic zone, the intensity of sunlight is reduced
to by over 99 percent and the visible spectrum is extremely
limited (Lorenzen, 1972). This phenomenon, coupled with
growth and sediment covering, makes identifying specific
objects difficult. Having a view of the context of the landscape
can aid in identifying irregularities in shape, texture, and col-
or for both wreckage recovery as well as ecological research.
Conclusions
Using the method we have outlined in this paper, it is pos-
sible in a retrospective analysis to create a single-perspective
mosaic of a debris field from video, and identify obscured
objects when no scale marker was used. Processing the data
in the manner described can give insight into the relative size
and shape of objects within the context of the entire debris
field. Even when planned scale markers are absent, scale
markers can be potentially applied to the entire debris field
using identifiable man-made objects present in the video,
and used to construct a larger, more accurate representation
of the marine landscape. Ratiometric comparisons between
CAD
models of the purported objects and the inadvertent scale
markers can help assess the likelihood that the underwater
objects are the purported objects. Visual detection of debris
can be greatly improved with a wider field of view than that
of a single camera, which can be created using our stitching
algorithm. The method presented outlines a cost effective and
noninvasive strategy to document an underwater landscape.
The data is detail-rich and accurate compared to other nonin-
vasive search methods. Ultimately, we found that the method
presented in this paper positively identified the potential
landing gear as a good geometric and ratiometric match for
the Earhart Lockheed Electra Model 10E and that the results
merit further retrieval and analysis efforts.
Acknowledgments
Funding for this project was provided by Timothy Mellon.
The authors would like to thank Grace McGuire for her kind-
ness in photographing and lending components from her
Lockheed Electra 10E for our analysis.
References
Agisoft Photoscan, 2016. URL:
Agisoft LLC,
11 Degtyarniy per., St. Petersburg, Russia, 191144 (last date
accessed: 15 January 2016).
Brown, M., and D.G. Lowe, 2007. Automatic panoramic image
stitching using invariant features,
International Journal of
Computer Vision
, 74(1):59–73.
Campos, R., R. Garcia, P. Alliez, and M. Yvinec, 2014. A surface
reconstruction method for in-detail underwater 3D optical
mapping,
The International Journal of Robotics Research
,
34(1):64–89.
Canciani, M., P. Gambogi, F. Romano, G. Cannata, and P. Drap, 2003.
Low cost digital photogrammetry for underwater archaeological
site survey and artifact isertion, The case study of the Dolia
Wreck in secche della meloria-livorno-italia,
International
Archives of Photogrammetry, Remote Sensing Spatial
Information Sciences
, 34:95–100.
Drap, P., J. Seinturier, D. Scaradozzi, P. Gambogi, L. Long, and
F. Gauch, 2007. Photogrammetry for virtual exploration
of underwater archeological sites,
Proceedings of the 21
st
International Symposium, CIPA 2007: AntiCIPAting the Future of
the Cultural Past
, 01–06 October 2007, Athens Greece.
Fischler, M.A. and R.C. Bolles, 1981. Random sample consensus: a
paradigm for model fitting with applications to image analysis
and automated cartography,
Communications of the ACM
,
24(6):381–395.
Gracias, N.R., S. Van der Zwaan, A. Bernardino, and J. Santos-Victor,
2003. Mosaic- based navigation for autonomous underwater
vehicles,
IEEE Journal of Oceanic Engineering
, 28(4):609–624.
Gracias, N.R., and J. Santos-Victor, 2001. Trajectory reconstruction
with uncertainty estimation using mosaic registration,
Robotics
and Autonomous Systems
, 35(3):163–177.
Harris, C., 1993. Tracking with rigid models,
Active Vision
, MIT
Press, pp. 59–73.
Hohle, J., 1971. Reconstruction of underwater object,
Photo-
grammetric Engineering & Remote Sensing
, 37(9):949–954.
Leatherdale, J.D., and D.J. Turner, 1991. Operational experience in
underwater photogrammetry,
ISPRS Journal of Photogrammetry
and Remote Sensing
, 46(2):104–112.
Lirman, D., N.R. Gracias, B.E. Gintert, A.C.R. Gleason, R.P. Reid,
S. Negahdaripour, and P. Kramer, P., 2007. Development and
application of a video-mosaic survey technology to document
the status of coral reef communities,
Environmental Monitoring
and Assessment
, 125(1-3):59–73.
Lorenzen, C.J., 1972. Extinction of light in the ocean by
phytoplankton,
Journal du Conseil
, 34(2):262–267.
Lowe, D.G., 2004. Distinctive image features from scale-invariant
keypoints,
International Journal of Computer vision
, 60(2): 91–110.
Maas, H.G., and U. Hampel, 2006. Photogrammetric techniques in
civil engineering material testing and structure monitoring,
Photogrammetric Engineering & Remote Sensing
, 72(1):39–45.
Moravec, H.P., 1977. Towards automatic visual obstacle avoidance,
Proceedings of the 5
th
International Joint Conference on Artificial
Intelligence - IJCAI’77
, 2:584.
Negahdaripour, S., and A. Khamene, 2000. Motion-based
compression of underwater video imagery for the operations of
unmanned submersible vehicles,
Computer Vision and Image
Understanding
, 79(1):162–183.
PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING
March 2016
231
