Satellite photogrammetry case study
Grant Pearse, Jonathan Dash, Michael Watt, Henrik Persson
Outline
• Background and modern methods
• Data sources and examples
• Case study: Pléiades stereo-satellite trial
• Conclusions and future research
Background - photogrammetry
• 1858: Aime Laussedat - father of photogrammetry
• Early 20th century: first specially designed aerial cameras
• Continuing developments:
– Routine use for topographic analysis
– Dramatic increase in (imperfect) imagery
– Dramatic increase in computational resources
Digital Surface Model
Background - photogrammetry
• Recent developments
– Significant advances in key areas
• SIFT (Lowe 1999) & others (SURF, BRIEF, ORB, etc.)
• Semi-Global Matching (Hirschmüller 2008)
• Powerful processors
ORB key points between original and affine transformed image. Source: Karami et al. 2017
Background - photogrammetry• Pix4D, PhotoScan, SURE etc.
– external and internal camera parameters
– UAV, aerial orthomosaics
Canopy height model developed from Pix4D point cloud.
Satellite photogrammetry
• Stereo imagery – digital surface model and synthetic point cloud
– GeoEye-1, IKONOS, WorldView 2 & 3, Pléiades
Stereo-capture (image ©DigitalGlobe)
Pléiades-1A & 1B
GSD 50 cm
Bands NIR,R,G,B
Swath 20 km
Min order 25-100 km2
Cost $32-50 / km2Pléiades satellite (image: CNES)
Geraldine Case Study
Geraldine Forest
– 195 plots:
Geraldine Case Study
Approach and Methods
• Capture stereo-pair imagery – 80 km2
• Generate synthetic point cloud SURE (nFrames) – Semi-global matching
– Normalise to LiDAR-DTM
– Ground control and LiDAR DTM
Geraldine Case Study
• Contrast ALS and stereo-satellite data
– Basic point metrics from point cloud data (PCD)
– CHMs from ALS and stereo PCD
– Textural metrics
Modelling
– Elastic-net (penalised regression)
– Sparsity (variable selection) and stability
– Selection of hyper-parameters λ & α : repeated sampling (10-CV x 2000)
– RMSE, R2 from LOOCV
Geraldine Case Study: Results
Satellite PCD: Noise and voids (outlier removal, interpolation)
High-quality CHM and good matching across scene
Mean Top Height:
• Highest correlation ALS: p99 (r = 0.94)
• Highest correlation: Satellite PCD p90 (r = 0.76)
Geraldine Case Study: Results
Canopy height models developed from a) ALS and b) satellite point cloud data. Insets show differences in the level of detail obtained over areas with narrow road corridors and variations in stand density.
Geraldine Case Study: Results
Geraldine Case Study: Results
Geraldine Case Study: Results
Geraldine Case Study: Conclusions• Stereo-satellite imagery is a viable source of DSM and PCD data
• Accurately predict key inventory attributes
• Cost-effective and provides imagery as well
– rectification of sensor-level image & pan-sharpening
• Require 1-time LiDAR DTM
• Voids, shadows, and broken terrain do affect stereo matching
Future Topics
• Tri-stereo imagery
• Deeper views through canopy
• Add in spectral indices
• kNNStereo vs. tri-stereo imagery (image: Airbus defence and space)
Acknowledgements
• Aaron Gunn – Blakely Pacific
• GCFF – Funding (FGR & MBIE)
• LINZ - LiDAR funding
• Interpine - Field data
• SLU – Henrik Persson
References
• Hirschmuller, H. (2008). Stereo processing by semiglobal matching and mutual information. IEEE Transactions on pattern analysis and machine intelligence, 30(2), 328-341.
• Karami, E., Prasad, S., & Shehata, M. (2017). Image matching using SIFT, SURF, BRIEF and ORB: Performance comparison for distorted images. arXiv preprint arXiv:1710.02726.
• Lowe, D. G. (1999). Object recognition from local scale-invariant features. In Computer vision, 1999. The proceedings of the seventh IEEE international conference on (Vol. 2, pp. 1150-1157). IEEE.
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Grant PearseGeomatics Scientist
April 2018