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Masetti et al. - Bathymetric and reflectivity-derived data fusion for Preliminary Seafloor Segmentation and Strategic Bottom Sampling

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Presentation given at GeoHab 2018 - Santa Barbara, CA, USA
Authors: G. Masetti, L.A. Mayer, L.G. Ward, D. Sowers

Published in: Science
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Masetti et al. - Bathymetric and reflectivity-derived data fusion for Preliminary Seafloor Segmentation and Strategic Bottom Sampling

  1. 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. 2. BACKSCATTER PROCESSING Data Acquisition Pre-Processing Analysis 2 RAW PRE ARA
  3. 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. 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. 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. 6 OCS-UNH CO-DEVELOPMENT
  7. 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.
  8. 8. Survey Data Monitor ▪ Merge ideas from: ▫ Manda’s svplot ▫ Wilson’s CastTime ▪ Leverage: ▫ SSM database ▫ SSM-SIS interaction 11
  9. 9. 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
  10. 10. 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.
  11. 11. SmartMap WebGIS ▪ RTOFS + WOA13 ▪ Animation ▪ Past data ▪ Survey Planner 14
  12. 12. 15
  13. 13. 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.
  14. 14. StormFix: How it works?
  15. 15. 18
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  18. 18. 21 Just Removal vs Randomization Schema
  19. 19. 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.
  20. 20. QC Tools & Grid Anomalies ▪ ARA’s pro: Artifacts reduction. 23
  21. 21. HYDROFFICE APPS 24 PYTHON SCIENTIFIC STACK OCEAN MAPPING LIBS & SCRIPTS
  22. 22. Distribution Pydro Universe Stand-alone Apps Python Packages NOAA website www.hydroffice.org GitHub/PyPi/Conda 25
  23. 23. 26 ARA MOS PRE GEOCODER
  24. 24. 27Ref.: Fonseca, L. et al., “Angular range analysis of acoustic themes from Stanton Banks Ireland”, Applied Acoustics, vol. 70. pp. 1298-1304, 2009.
  25. 25. 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.
  26. 26. - + 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.
  27. 27. 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
  28. 28. 39 Landform ClassificationLocal Ternary Patterns
  29. 29. 40 Output SegmentsArea Kernels
  30. 30. 41
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  32. 32. 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? ???
  33. 33. 44 ARA MOS GEOCODER DTM BRESS
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  42. 42. 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)
  43. 43. THANKS! Any questions? Visit: https://www.hydroffice.org You can contact me at: gmasetti@ccom.unh.edu

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