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Morphological measurement using close range photogrammetry- guidance
from ISPRS V6
Jim Chandler1, Rene Wackrow1 and Dirk Rieke-Zapp2
1Loughborough University, UK2University of Berne, Switzerland
AAG, Seattle- April 2011
(with notable input from: Tom Dijkstra, Simon Buckley, Marko Tuominen, Stephen Bird and members of ISPRS Working Group V6)
Overview
Why produce “tips”? Who contributed? Key points? An illustration!
AAG, Seattle- April 2011
Why are “tips” necessary?
Stated activity! Past experiences-
particularly postgraduates seeking advice
Two sets!
AAG, Seattle- April 2011
Stated activity! Past experiences-
particularly postgraduates seeking advice
Two sets!
Who contributed?
AAG, Seattle- April 2011
Primarily WG Officers
Also notable input from industry!
including:
Marko Tuomien, Datapix Pty Ltd, Queensland, AustraliaStephen Bird, Fluvial Systems Research Inc, Whiterock, Canada
Start simple- start small!
150 small format images of a river channel in Spain- I fly next week and my supervisor doesn’t know about photogrammetry!
AAG, Seattle- April 2011
Common sense!
Familiarisation- techniques and how software works!
Start with: Small test area captured with perhaps two pairs Ideally use a pre-calibrated camera Establish abundant photo control- easy restitution plus checks
Which camera?
All cameras have spatial measurement potential!
AAG, Seattle- April 2011
Digital SLR!
Fixed or variable zoom lens?
Never purchase “top of the range”
Camera calibration
Necessary for accurate spatial data acquisition!
AAG, Seattle- April 2011
Difficulties!
What software? Matlab, PhotoModeler, IWitness, etc.
• camera focal length (f)• principal point offset (xP, yP)• radial lens distortion (K1, K2, K3)• tangential distortion (P1, P2)
-300
-250
-200
-150
-100
-50
0
0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0(m
icro
ns)
Radial Dist (mm)
Radial lens distortion
Fieldwork/control
AAG, Seattle- April 2011
Photo acquisition:• Infinity focus/f8
• Fastest shutter speed possible/tripod
• Obtain redundant imagery
Control Don’t skimp! Well distributed Withhold 20% and use as “checkpoints”- RMSE or mean error and
standard deviation?
Visit site prior capture images “in pairs/triplets”
RMSE or mean error + standard deviation?
AAG, Seattle- April 2011
Provide measures of:
• Bias- true reflection of systematic error
• Variation- true reflection of “random error”
𝑅𝑀𝑆𝐸 ሺ∆ℎሻ= ඨσ∆ℎ𝑖2𝑛
where: ℎ𝑖 = 𝐸𝑙𝑒𝑣.𝑖− 𝐸𝑙𝑒𝑣. 𝑇𝑟𝑢𝑒𝑖 Conventional measure:
Better measure?: 𝑀𝑒𝑎𝑛 𝐸𝑟𝑟𝑜𝑟 ሺ𝑢ሻ= σ∆ℎ𝑖𝑛
𝑆𝑡𝑎𝑛𝑑𝑎𝑟𝑑 𝑑𝑒𝑣𝑖𝑎𝑡𝑖𝑜𝑛 ሺ∆ℎሻ= ටσሺ∆ℎ𝑖−𝑢ሻ2𝑛
Benefits of convergent imagery!
Former Ph.D. Student- Dr Rene Wackrow
AAG, Seattle- April 2011
Test object- planar test field with added blocks Automated DEM extraction- compare elevations with planar “truth”
Normal imagery convergent imagery!
AAG, Seattle- April 2011
Test object- planar test field with added blocks Automated DEM extraction- compare elevations with planar “truth”
DEM of differences- Nikon D80 (10MP)
AAG, Seattle- April 2011
Normal case
Mean error: 0.4 mm
Standard deviation: +/-0.4mm
Convergent case
Mean error: 0.1 mm
Standard deviation: +/- 0.2mm
Zhouqu- China 8th Aug. 2010
AAG, Seattle- April 2011
“Tips” document, freely available- more later!
Zhou Qu debris flow: Zhouqu mudslides death toll rose to 337 people, 1148 people missing
Measurement potential of imagery!
AAG, Seattle- April 2011
Measurement potential of imagery!
AAG, Seattle- April 2011
Video fly-thru!
Gradient of debris slope!
Conclusion
Prime purpose, raise awareness of the activities of ISPRS Working Group V6: http://isprsv6.lboro.ac.uk/
Special Issue of the Photogrammetric Record: http://onlinelibrary.wiley.com/doi/10.1111/j.1477-9730.2010.00590.x/full
“Tips” document, providing simple guidance.
Also some recent work/interests: Potential of convergent image geometry
Potential of digital imagery using consumer grade digital cameras
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