Structure-from-Motion, Grand Canyon style

Presented at the Pacific Coastal and Marine Science Center USGS Santa Cruz, CA, Dec 7 2016

Transcript

1. Large-scale SfM: Grand Canyon style Daniel Buscombe, U.S. Geological Survey & Northern Arizona University, Flagstaff, AZ Rebecca Rossi, Utah State University, Logan, UT Paul Grams, U.S. Geological Survey, Grand Canyon Monitoring and Research Center, Flagstaff, AZ RSCC Workshop, USGS Santa Cruz, 7 December 2016 RM145, October 2015 ~10M points
2. Photo: Matt Kaplinski, NAU Photo: Matt Kaplinski, NAU RM123, October 2015 ~6M points
3. Poles (no UAVs Allowed) GPS is unreliable GCPs with total station No man-made objects Remote
4. GC Environments I: Sparsely vegetated sandbars RM70, October 2014 ~10M points
5. GC Environments II: Heavily vegetated sandbars RM50, September 2014 ~23M points
6. GC Environments III: Unvegetated sandbars RM30, September 2015 ~15M points
7. GC Environments IV: Debris fans RM166, October 2014 ~38M points
8. Complicated terrain Steep slopes RM213, October 2015 ~2M points RM91, October 2015 ~19M points
9. Fast-growing riparian vegetation, several canopies RM202, October 2015 ~25M points
10. RM145, October 2015 ~10M points Opportunities I: Automated feature extraction (mechanistic segregation) RM119, October 2015 ~43M points “intertidal” zone eroding bluffs vegetated aeolian surfaces water talus flat sand cut bank boulders
11. Opportunities II: What’s the grain size of this debris fan? RM166, October 2015 ~115M points www.digitalgrainsize.org
12. RM119, October 2015 ~43M points Opportunities III: Automated registration (Surveying GCPS is time-consuming … use rocks!?)
13. Challenges I: Automated point cloud cleaning Temporary clutter People Water and boats RM137, October 2015 ~8M points
14. RM91, October 2014 ~8M points RM91, October 2015 ~19M points Challenges II: Shadows can make automated classification difficult
15. RM56, October 2014 ~29M points Shadows continued
16. Spatially distributed uncertainty
17. PySESA • Toolbox for statistical analysis of point clouds • Command-line python routines (with computationally demanding bits compiled) • Generic in design and application For SfM: • Point cloud decimation • Exploratory data analysis • Point cloud cleaning • Point cloud classification http://dbuscombe-usgs.github.io/pysesa/ Buscombe (2016) Computers & Geosciences
18. http://dbuscombe-usgs.github.io/pysesa/ 4 different types of point cloud detrending
19. From each data window Up to 34 metrics (spatial and spectral) http://dbuscombe-usgs.github.io/pysesa/
20. RM202, October 2015 ~25M points
21. 10 cm decimation Elevation
22. Point density
23. Roughness (locally detrended std.dev)
24. Spectral strength
25. 10 cm decimation Red
26. 10 cm decimation Green
27. Kurtosis Red
28. Integral lengthscale Blue
29. Fractal dimension Green
30. Example: RM213, October 2014
31. Range in elevation
32. Roughness (spatial and spectral)
33. Skewness (also kurtosis)
34. Zeroth spectral moment (area under spectrum)
35. Example: RM194, October 2014
36. Thresholded Roughness
37. Example: RM202, October 2015
38. False color (spectral intercept, integral lengthscale and roughness)
39. PySESA - SfM • Works on multiple dependent variables (z, r, g, b) • “red roughness”, “green skewness”, etc • Converts colorspaces (rgb to hsv, ycc, lab, etc) • Building in tools for supervised classification (needs calibration, lots of different types of data)
40. RM145, October 2015 ~10M points Daniel Buscombe, U.S. Geological Survey & Northern Arizona University, Flagstaff, AZ Rebecca Rossi, Utah State University, Logan, UT Paul Grams, U.S. Geological Survey, Grand Canyon Monitoring and Research Center, Flagstaff, AZ Thanks: Joe Wheaton (USU), Joe Hazel, Matt Kaplinski, Ryan Lima (NAU) RSCC Workshop, USGS Santa Cruz, 7 December 2016
43. First spectral moment (weighting higher frequencies)
44. Integral lengthscale (range of roughnesses present)
45. Minimum elevation (0.1 m decimation) (also mean, max)
46. Point density
47. Poles (no UAVs allowed) GPS is unreliable GCPs with total station Photoscan Pro