1. Bathymetric and Reflectivity-derived Data Fusion
for Preliminary Seafloor Segmentation and Strategic Bottom Sampling
G. Masetti, L.A. Mayer, L.G. Ward, D. Sowers

2. BACKSCATTER PROCESSING
Data Acquisition Pre-Processing Analysis
2
RAW PRE ARA

3. GEOCODER
3
ARA
MOS
RAW PRE
Ref.: Fonseca, L., and Mayer, L.A., Remote estimation of surficial seafloor properties through the application of
Angular Range Analysis to multibeam sonar data, Mar. Geophysical Res., 28 (2), p. 119-126, 2007.

4. 4
ARA
MOS
RAW PRE
GEOCODER
Ref.: Fonseca, L., and Mayer, L.A., Remote estimation of surficial seafloor properties through the application of
Angular Range Analysis to multibeam sonar data, Mar. Geophysical Res., 28 (2), p. 119-126, 2007.

5. A framework of
libraries and tools
for Ocean Mapping
5
Quickly prototype
and test
innovative ideas
Ease the transition
from research to
operation
Ref.: G. Masetti, Wilson, M. J., Calder, B. R., Gallagher, B., and Zhang, C., “Research-driven Tools for Ocean Mappers”, Hydro Int., vol. 21, 5. GeoMares, 2017.

6. 6
OCS-UNH CO-DEVELOPMENT

7. Sound Speed Manager
▪ Manage sound speed casts.
▪ Adopted by UNOLS vessels (MAC)
and many others.
▪ Modified to fit NOAA Coast Survey
needs.
▪ ARA’s pro: Absorption Coefficient.
7Ref.: G. Masetti, Gallagher, B., Calder, B. R., Zhang, C., and Wilson, M. J., “Sound Speed Manager”, Int. Hydr. Review, vol. 17. IHB, pp. 31-40, 2017.

11. Survey Data Monitor
▪ Merge ideas from:
▫ Manda’s svplot
▫ Wilson’s CastTime
▪ Leverage:
▫ SSM database
▫ SSM-SIS interaction
11

12. Survey Data Monitor & Cast Timing
12
1490 1535
0
4
16
8
12
0 10 20 30 40
Comparing the simulated seafloors is an estimate of sounding depth bias
Sound Speed (m/s)
Depth(m)
Horizontal Range (m)
1505 1520
Δd

13. SmartMap
▪ Effects of oceanographic
variability on mapping surveys
▪ Two components:
▫ C++ & Python
▫ GeoServer and OGC services
▪ WebGIS:
▫ www.hydroffice.org/smartmap/
13Ref.: G. Masetti, Kelley, J., Johnson, P., and Beaudoin, J., “A Ray-Tracing Uncertainty Estimation Tool for Ocean Mapping”, IEEE Access. IEEE, pp. 1-9, 2017.

14. SmartMap WebGIS
▪ RTOFS +
WOA13
▪ Animation
▪ Past data
▪ Survey
Planner
14

15. 15

16. StormFix
ARTIFACTS
DETECTION
ARTIFACTS
REDUCTION
BACKSCATTER
MOSAICKING
ANGULAR
RESPONSE
ANALYSIS
Ref.: G. Masetti et al., “How to Improve the Quality and the Reproducibility for Acoustic Seafloor Characterization”, GeoHab 2017. p. Nova Scotia, Canada, 2017.

17. StormFix: How it works?

18. 18

19. 19

20. 20

21. 21
Just Removal vs Randomization Schema

22. QC Tools
▪ Automate QC for Survey Review and
Chart Compilation:
▫ Convert best practices and specs
into code.
▫ Familiarize new personnel to specs.
▪ Routinely used by NOAA OCS.
▪ Improved productivity of the ping-
to-chart workflow.
22Ref.: M. J. Wilson, Masetti, G., and Calder, B. R., “Automated Tools to Improve the Ping-to-Chart Workflow”, Int. Hydr. Review, vol. 17. IHB, pp. 21-30, 2017.

23. QC Tools & Grid Anomalies
▪ ARA’s pro: Artifacts reduction. 23

24. HYDROFFICE APPS
24
PYTHON SCIENTIFIC STACK
OCEAN MAPPING LIBS
& SCRIPTS

25. Distribution
Pydro Universe Stand-alone Apps Python Packages
NOAA website www.hydroffice.org GitHub/PyPi/Conda
25

26. 26
ARA
MOS
PRE
GEOCODER

27. 27Ref.: Fonseca, L. et al., “Angular range analysis of acoustic themes from Stanton Banks Ireland”, Applied Acoustics, vol. 70. pp. 1298-1304, 2009.

28. Bress
▪ Preliminary segmentation
from co-located DEMs and
backscatter mosaics
▪ Based on principles of:
▫ Topographic openness
▫ Pattern recognition
▫ Texture classification
28Ref.: G. Masetti, Mayer, L. A., and Ward, L. G., “A Bathymetry- and Reflectivity-Based Approach for Seafloor Segmentation”, Geosciences, vol. 8(1). MDPI, 2018.

35. - + 0 1 2 3 4 5 6 7 8
0
1 -
2 - -
3 - - -
4 - - - -
5 - - - - -
6 - - - - - -
7 - - - - - - -
8 - - - - - - - -
Ref.: J. Jasiewicz, T.F. Stepinski, “Geomorphons—a pattern recognition approach to classification and mapping of landforms”, Geomorphology, 182, pp.147–156, 2013.

36. FL FL FL FL FL FL FL FL SL SL SL SL SL SL SL SL SL SL SL SL
FS FS FL FL FL FL FL SL SL SL SL SL SL SL SL SL SL FS FS FS
FS FS FS FL FL FL FL SL SL SL SL SL SL SL SL FS FS FS FS FS
FS FS FS FL FL FL FL SL SL SL SL SL SL SL SL FS FS FS FS FS
FS FS FS FL FL FL FL SL SL SL SL SL SL SL SL FS FS FS FS FS
FS FS FS FS FS FS FS SL SL SL SL SL SL SL FS FS FS FS FS FS
FS FS FS FS FS FS FS SL SL SL SL SL SL SL FS FS FS FS FS FS
FS FS FS FS FS FS FS SL SL SL SL SL SL SL FS FS FS FS FS FS
FS FS FS VL VL VL VL VL VL VL VL VL VL VL VL FS FS FS FS FS
FS FS VL VL VL VL VL VL VL VL VL VL VL VL VL FS FS FS FS FS
FS FS VL VL VL VL VL VL VL VL VL VL VL VL VL FS FS FS FS FS
VL VL VL VL VL VL VL VL VL VL VL VL VL VL VL FS FS FS FS FS
VL VL VL VL VL VL VL VL VL VL VL VL VL VL VL FS FS FS FS FS
VL VL VL VL VL VL VL VL VL VL VL VL VL FS FS FS FS FS FS FS
FL FL FL FL FL FL FL FL SL SL SL SL SL SL SL SL SL SL SL SL
FS FS FL FL FL FL FL SL SL SL SL SL SL SL SL SL SL FS FS FS
FS FS FS FL FL FL FL SL SL SL SL SL SL SL SL FS FS FS FS FS
FS FS FS FL FL FL FL SL SL SL SL SL SL SL SL FS FS FS FS FS
FS FS FS FL FL FL FL SL SL SL SL SL SL SL SL FS FS FS FS FS
FS FS FS FS FS FS FS SL SL SL SL SL SL SL FS FS FS FS FS FS
FS FS FS FS FS FS FS SL SL SL SL SL SL SL FS FS FS FS FS FS
FS FS FS FS FS FS FS SL SL SL SL SL SL SL FS FS FS FS FS FS
FS FS FS VL VL VL VL VL VL VL VL VL VL VL VL FS FS FS FS FS
FS FS VL VL VL VL VL VL VL VL VL VL VL VL VL FS FS FS FS FS
FS FS VL VL VL VL VL VL VL VL VL VL VL VL VL FS FS FS FS FS
VL VL VL VL VL VL VL VL VL VL VL VL VL VL VL FS FS FS FS FS
VL VL VL VL VL VL VL VL VL VL VL VL VL VL VL FS FS FS FS FS
VL VL VL VL VL VL VL VL VL VL VL VL VL FS FS FS FS FS FS FS

39. 39
Landform ClassificationLocal Ternary Patterns

40. 40
Output SegmentsArea Kernels

41. 41

42. 42

43. 43
Different Criteria:
• Given a fixed number of
samples, locations with
largest coverage?
• How many samples to
obtain a given percentage
of coverage?
• What are the more
“meaningful” locations
for bottom sampling?
???

44. 44
ARA
MOS
GEOCODER
DTM
BRESS

45. 45

46. 46

47. 47

48. 48

49. 49

50. 50

51. 51

52. 52

53. CONCLUSIONS
53
• Output segments preserve physical intuition:
• Same landform type
• Similar reflectivity texture
• Preliminary segmentation is a building block for:
• Theme-based seafloor characterization
• Strategic bottom sampling
• Habitat modeling (WIP)

54. THANKS!
Any questions?
Visit: https://www.hydroffice.org
You can contact me at: gmasetti@ccom.unh.edu