Automated substrate characterization using low-cost sidescan sonar

Automated substrate characterization using low-cost sidescan sonar

American Fisheries Society Meeting, Tampa, Florida Aug 2017

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Daniel Buscombe

August 23, 2017
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  1. Automated substrate characterization using low-cost sidescan sonar. Daniel Buscombe Sch.

    Earth Sci. & Environ. Sustainability, Northern Arizona University. American Fisheries Society Annual Meeting, Tampa, Florida, August 2017.
  2. Outline • PyHum program • Sidescan processing and rectification •

    Unsupervised substrate classification • Supervised substrate classification • Challenges & future directions
  3. Big boats ……………………………………. little boats Yellowstone River, Montana Colorado River,

    Arizona Penobscot River, Maine
  4. Different substrate types have unique textures. Opportunity for fully automated

    substrate classification
  5. Colorado River in Grand Canyon. Wide. Deep. Muddy

  6. • Python program for working with Humminbird data 1. Reads

    data 2. Applies corrections 3. Remove shadows (manual or automated) 4. Rectifies imagery to make planform georeferenced imagery (“mapping”) 5. Substrate classification and mapping • Technical details: Buscombe (2017) Environmental Modelling and Software • https://github.com/dbuscombe-usgs/PyHum • http://dbuscombe-usgs.github.io/PyHum/ “PyHum”
  7. “61-mile” data set

  8. Reported sonar depth

  9. Raw data Aerated water

  10. Geometric & radiometric correction

  11. Shadow removal

  12. None
  13. Rectification

  14. Merged sidescan

  15. Texture Lengthscale [m] Unsupervised substrate classification

  16. Buscombe et al. (2016) Automated Riverbed Sediment Classification Using Low-Cost

    Sidescan Sonar. Journal of Hydraulic Engineering 142 (2) = න 2 Mean length of texture elements, in meters: = න 2 cos Normalized variance spectrum Vector of scales [m] Object of given height, length of shadow proportional to grazing angle, θ:
  17. Merged sidescan

  18. Unsupervised substrate classification Texture Lengthscale [m]

  19. None
  20. map Reach – aggregated substrate distributions

  21. Buscombe (2017) Environmental Modelling & Software 89, 1 - 18

  22. Supervised substrate classification

  23. Conditional Random Field: • Sparse labels (my doodles) + image

    (greyscale scan) • Builds a probabilistic model for each label based on 1. Relative locations in the image and 2. Distribution of greyscales • Applies model to fill in gaps. Each iteration, it updates the probabilistic model
  24. Minimal supervision!

  25. None
  26. None
  27. • Challenge: Separation of bedform and grain roughness (also other

    highly discrete textures e.g. wood) • Possible future direction: more sophisticated texture identification ‘beyond variance’ (machine learning) Challenges & Future Directions 1
  28. Challenges & Future Directions 2 • Challenge: sidescan imagery is

    poor quality because acoustic information is lacking • Therefore radiometric correction, better sidescan merging (data fusion) are very rudimentary • Possible future direction: Zhao et al (2017), Remote Sensing 9 (6) • Use a substrate classification to model backscattering by bed = better radiometric corrections = better quality scans
  29. Last word. An “open-source” hardware system? • Black-box sonar: SL?

    TVG? Directivity? • So far, we’ve been lucky that manufacturers make data available and hack the data structure • How long will that last? • Interface with autonomous survey vehicles, ROVs – real-time data Source: https://www.asvglobal.com/ StarFish 453 OEM Sidescan System
  30. • Buscombe (2017) Shallow water benthic imaging and substrate characterization

    using recreational-grade sidescan-sonar. Environmental Modelling and Software 89, 1-18 • Buscombe et al. (2016) Automated Riverbed Sediment Classification Using Low-Cost Sidescan Sonar. Journal of Hydraulic Engineering 142 (2) • PyHum • https://github.com/dbuscombe-usgs/PyHum Thanks! • Funding = USGS, BoR • Daniel Hamill (USU) • Adam Kaeser (FWS) • Paul Grams (USGS) • Ted Melis (USGS) • Sean Smith (UMaine) • Joe Wheaton (USU) • PyHum users!
  31. None
  32. None
  33. PyHum vs. SonarTRX Point cloud Rubbersheeting (?)