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.

www.fgr.nzwww.gcff.nz

www.scionresearch.com

Grant PearseGeomatics Scientist

grant.pearse@scionresearch.com

April 2018