Unmanned Aerial Systems (UAS) Data Quality and Accuracy Realities
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Transcript of Unmanned Aerial Systems (UAS) Data Quality and Accuracy Realities
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Engineering | Architecture | Design-Build | Surveying | Planning | GeoSpatial Solutions
April 26, 2016
GEOSPATIAL SOLUTIONS
Unmanned Aerial System (UAS)Data Quality and Accuracy
Realities
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Matt Bethel, GISP
Director of Technology for Merrick & Company
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Stephen Keen geoResource Technologies, Inc. Northeast Arc User Group (NEARC)Spring Spatial Technologies Conference Monday, May 11, 2015
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UAS vs. Direct Georeferencing
GPS seeds the processing No post processing GPS (no AGPS base station required) No rigorous IMU processing Photo identifiable points are still required Exterior orientation is calculated with little to no GPS/IMU information
Camera model is automatically refined throughout the process Interior is adjusted, typically per image This allows for the use of non-metric cameras
Movement towards more streamlined / black box process Less human time, more computer time (until processes are improved)
1. Relative 3D model is built using computer vision processes
2. Adjusted to ground with control using traditional AT procedures
3. Strengthened and densified using new photogrammetric processes
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UAS Processing Workflow
Flight Planning AcquisitionPre-Processing
• Image reformatting• AGPS reformatting• Processing block selection
Triangulation• Feature detection• Feature matching• Initial 3D model / point cloud built using
Structure from Motion (SfM)• Models each scene• Creates a rough surface for image scaling
during point measurement• Interior orientation calibration
Control Point Measurement
Bundle Adjustment• Adjusts model to control point
measurements• Recalibrates interior and exterior
orientations
Full Processing• Uses multi-ray photogrammetry /SGM• Undistorts images• Creates dense point clouds
Orthophoto Generation• Creates grid• Creates mesh (to fill in holes)• Generates individual orthophotos• Mosaicing, radiometric and color
balancing, and automatic seamline placement
• Mosaic tiling
Image Textured 3D Models
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Influences on UAS Product Accuracies
Camera CCD / pixel size and quality Lens quality Lens field of view Camera triggering / frame rate and image
write speed Shutter speed / motion blur ISO, aperture, and focus (infinity) Image compression and acquisition storage
file format (raw vs. jpg) Orientation (portrait vs. landscape)
UAV Flight line geometry, especially cross flight
lines Image endlap and sidelap Flight management system Stability / wind conditions Above ground level
Environmental Lighting conditions Land cover Dust, haze, humidity, smog, etc.
GNSS Surprisingly, rarely AGPS quality Quality and feature placement of photo id
control points Photo id control points distribution Quantity of photo id control points Use of an inertial measurement system
Software Computer resources (can limit products) Features Settings Robustness Versions
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Test Area
2,105 nadir RGB images2 UAS missions300 m AGL24 MP non-metric digital
camera75% endlap / 50% sidelap4.5 cm nominal pixel res2.6 square miles31 GPS surveyed points
5 Control points 26 Check points
UAS data overlaps existing fixed wing LiDAR
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Horizontal Orthophoto Accuracy
Reno Stead Airport AOI 0.5 square mile 10 cm acquisition / orthophoto
resolution 31 surveyed photo id points
15 used for control 16 used for check
15 control points = 5.5 cm RMSE
16 check points = 4.9 cm RMSE
Typically 1 -1.5 pixel resolutionGuadalajara AOI yielded 5 cm RMSE from 4.5 cm resolutionSome lower resolution, small area collects can yield better than 1:1
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3D Product Accuracy Results
Dense Point Cloud Gridded Elevation Model0
5
10
15
20
25
30
6.15.2
7.9
9.7
24.2
20.1
15.1
11.2
Fixed Wing LiDAR
UAS Software 1
UAS Software 2
UAS Software 3
Vert
ical
Acc
urac
y R
MSE
z (c
m)
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Volumetric Accuracy Results
Dense Point Cloud Gridded Elevation Model0.00%
0.50%
1.00%
1.50%
2.00%
2.50%
3.00%
2.50%
0.78%
0.21%
0.04%
1.10%
1.42%
UAS Software 1
UAS Software 2
UAS Software 3
Volu
met
ric D
iffer
ence
Com
pare
d to
LiD
AR
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3D Model Regional Deformation
Vertical separation raster of UAS DPC compared to the all returns LiDAR
The vertical RMSEz measured to PID control for each SW package are: UAS Software 1 – 7.9 cm UAS Software 2 – 24.2 cm UAS Software 3 – 15.1 cm
Meters
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Summary of Realistic UAS Accuracies
Best possible horizontal (absolute) accuracy in non-obstructed land cover is 0.5 to 1.5 times the captured pixel resolution (RMSE)
Best possible vertical (absolute) accuracy in non-obstructed land cover is 2 to 3 times the captured pixel resolution (RMSE)
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Phoenix Aerial Systems LiDAR UAS Vertical Accuracy
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Phoenix Aerial Systems LiDAR UAS Horizontal Accuracy
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LiDAR UAS
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Contact Info
Matt BethelDirector of Technology
Merrick & Companywww.merrick.com
[email protected](303) 353-3662