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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Dense Point Cloud Comparison
6.1
7.9
24.2
15.1
0
5
10
15
20
25
30
Dense Point Cloud
VerticalAccuracyRMSEz(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 Comparison
5.2
9.7
20.1
11.2
0
5
10
15
20
25
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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Image Textured Model (nadir) Comparison
24.9
12.5
32.9
0
5
10
15
20
25
30
35
Image Textured Model
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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Image Textured Model (oblique) Comparison
16.2
4.5
0.9
0
2
4
6
8
10
12
14
16
18
DSM Product
UAS Software 3
ITM Software
Helicopter LiDAR

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Combined Results
6.1
5.2
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)
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)
VolumetricDifferenceComparedtoLiDAR
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
matt.bethel@merrick.com
(303) 353-3662
ILMF booth #68

Unmanned Aircraft System (UAS) 3D Product Comparisons to Airborne LiDAR

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