unmanned aircraft system (uas) 3d product comparisons to airborne lidar
DESCRIPTION
Technical comparison of 3D UAS imaging products to airborne LiDAR data products. Presented by Matt Bethel, Director of Technology for Geospatial Solutions at Merrick & Company (www.merrick.com) on February 18, 2014 in Denver, Colorado. International LiDAR Mapping Forum (ILMF) - http://www.lidarmap.org/internationalTRANSCRIPT
Engineering | Architecture | Design-Build | Surveying | Planning | GeoSpatial Solutions
February 18, 2014
GEOSPATIAL SOLUTIONS
Unmanned Aircraft System (UAS)
3D Product Comparisons to
Airborne LiDAR
Copyright © 2013 Merrick & Company - All rights reserved.
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Matt Bethel, GISP
Director of Technology for Merrick & Company
UAS processing vs. direct georeferencing
Overview of UAS workflow
Influences on UAS accuracy
Discuss and compare 3D UAS imaging products to LiDAR
Presentation Agenda
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Not the purpose of this presentation
Comparing different UAV
systems and their components
Rigorous comparison of UAS
processing software fucntionality
UAS vs. LiDAR costs
UAS vs. traditional digital aerial
photography quality
UAS procedures, best practices,
FAA regulations, etc.
UAS vs. LiDAR collection
efficiencies
UAS sensors other than RGB
cameras
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UAS vs. Direct Georeferencing
GPS seeds the processing
No post processing GPS (no 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 Acquisition
Pre-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
Lens quality
Lens field of view
Camera triggering and image write speed
Shutter speed / motion blur
ISO and aperture
Image compression
Orientation
UAV Flight line geometry
Flight management system
Stability / wind conditions
Above ground level
Environmental Lighting conditions
Land cover
Dust, haze, humidity, smog, etc.
GPS Surprisingly, not AGPS quality
Quality and feature placement of photo id control points
Control point distribution
Software Features
Settings
Robustness
Versions
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Test Area 1
2,105 nadir RGB images
2 UAS missions
300 m AGL
24 MP non-metric digital camera
75% endlap / 50% sidelap
4.5 cm nominal pixel res
2.6 square miles
31 GPS surveyed points
5 Control points
26 Check points
UAS data overlaps existing fixed wing LiDAR
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Dense Point Cloud
A.k.a. Photo Correlated Digital Surface Model (PCDSM)
3D colorized, randomly spaced points
Derived from multi-ray matching using many stereo pairs with excessive overlap/sidelap
Processing times can be hours, days, to weeks per mission
Large to massive amounts of RAM required
Most programs are multi-threaded for this stage
Some programs are GP-GPU enabled for this stage
Densities ranging from 10s ppsm to 1,000s ppsm
Typically used as a Digital Surface Model (DSM)
Does not penetrate through vegetation well
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Dense Point Cloud
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Fixed Wing LiDAR Point Cloud
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Cross Sectional Comparison
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Cross Sectional Comparison
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Dense Point Cloud Comparison
6.1
7.9
24.2
15.1
0
5
10
15
20
25
30
Dense Point Cloud
Vert
ical A
ccu
rac
y R
MS
Ez (
cm
)
Fixed Wing LiDAR
UAS Software 1
UAS Software 2
UAS Software 3
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Gridded Elevation Model
Evenly spaced gridded raster
Gridded from DPC and meshed to fill holes
Processing times can be hours to days per mission
Medium to large amounts of RAM required
Some programs are multi-threaded for this stage
Cell size (GSD) is user defined, typically 2x-10x the pixel res
Typically assumed to be a Digital Elevation Model (DEM)
Does not penetrate through vegetation well – poor quality DEM product in vegetated areas
Much filtering of DPC may be required to “represent” ground
Many times this is impossible due to a lack of feature definition (not density) to determine what ground/above ground truly is
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Gridded Elevation Model
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Cross Sectional Comparison
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Cross Sectional Comparison
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Gridded Elevation Model Comparison
5.2
9.7
20.1
11.2
0
5
10
15
20
25
Gridded Elevation Model
Vert
ical A
ccu
rac
y R
MS
Ez (
cm
)
Fixed Wing LiDAR
UAS Software 1
UAS Software 2
UAS Software 3
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Image Textured 3D Model
Multi-directional Triangulated Irregular Network (TIN)
Created from three dimensionally modeling all visible portions of ground and above ground features
Ray tracing 3D feature positions compared to each image’s focal plane looking for the most perpendicular
Processing times can be hours, days, to weeks per mission – very dependent on software package
Medium to massive RAM required (successful /unsuccessful memory management varies dramatically across software packages)
Most programs are multi-threaded for this stage
Some programs are GP-GPU enabled for this stage
Native format are image textured TINs but when converted to colorized point clouds preserving all voxels, densities can range from 1,000s ppsm to 10,000s ppsm
Typically used for city models
Does not penetrate through vegetation well but can sometimes get around and under trees
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Image Textured 3D Model (nadir)
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Cross Sectional Comparison
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Cross Sectional Comparison
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Image Textured Model (nadir) Comparison
24.9
12.5
32.9
0
5
10
15
20
25
30
35
Image Textured Model
Vert
ical A
ccu
rac
y R
MS
Ez (
cm
)
UAS Software 2
UAS Software 3
ITM Software
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Test Area 2
955 multi-oblique RGB images
1 helicopter mission
375 m AGL
16 MP metric cameras
Excessive endlap and sidelap
4.5 cm nominal pixel resolution
61 GPS surveyed points
5 Control points
59 Check points
0.25 square miles
UAS-like processing used
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Image Textured 3D Model (multi- obliques)
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Helicopter LiDAR Point Cloud
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Cross Sectional Comparison
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Image Textured Model (oblique) Comparison
16.2
4.5
0.9
0
2
4
6
8
10
12
14
16
18
DSM Product
Vert
ical A
ccu
rac
y R
MS
Ez (
cm
)
UAS Software 3
ITM Software
Helicopter LiDAR
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Combined Results
6.15.2
7.9
9.7
24.2
20.1
24.9
15.1
11.2
12.5
16.2
32.9
4.5
0.9
0
5
10
15
20
25
30
35
Dense Point Cloud Gridded Elevation Model Image Textured Model Image Textured Model(multi-oblique cameras)
Vert
ical A
ccu
rac
y R
MS
Ez (
cm
)
Fixed Wing LiDAR
UAS Software 1
UAS Software 2
UAS Software 3
ITM Software
Helicopter LiDAR
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Volumetric Comparisons
2.50%
0.78%
0.21%
0.04%0.19%
1.10%
1.42%1.50%
0.30%
4.42%
5.23%
0.00%
1.00%
2.00%
3.00%
4.00%
5.00%
6.00%
Dense Point Cloud Gridded Elevation Model Image Textured Model Image Textured Model(multi-oblique cameras)
Vo
lum
etr
ic D
iffe
ren
ce C
om
pare
d t
o L
iDA
R
UAS Software 1
UAS Software 2
UAS Software 3
ITM Software
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Current and Future Considerations
How does AGL affect vertical accuracy?
How does the focal length of a nadir or array of oblique cameras affect vertical accuracy?
How will new UAS processing software and/or versions improve these results?
Can direct georeferencing or integrated sensor orientation improve vertical accuracy?
What techniques can minimize the need for costly ground control for UAS processing while still preserving accuracies?
Can image textured 3D model processing yield automatic true orthos for all features?
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Contact Info
Matt Bethel
Director of Technology
Merrick & Company
www.merrick.com
(303) 353-3662
ILMF booth #68