The document discusses the capabilities and performance metrics of unmanned aerial systems (UAS) in geospatial solutions, focusing on data quality and accuracy. It outlines the UAS processing workflow, factors influencing accuracy, and presents results from various tests comparing UAS-generated data to existing LIDAR datasets. Additionally, it provides metrics on horizontal and vertical accuracy, highlighting that optimal accuracies are often related to pixel resolution in non-obstructed environments.

1. 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 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
 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 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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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 resolution
 Guadalajara AOI yielded 5 cm RMSE from 4.5 cm resolution
 Some lower resolution, small area collects can yield better than 1:1

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3D Product Accuracy Results
6.1
5.2
7.9
9.7
24.2
20.1
15.1
11.2
0
5
10
15
20
25
30
Dense Point Cloud Gridded Elevation Model
VerticalAccuracyRMSEz(cm)
Fixed Wing LiDAR
UAS Software 1
UAS Software 2
UAS Software 3

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Volumetric Accuracy Results
2.50%
0.78%
0.21%
0.04%
1.10%
1.42%
0.00%
0.50%
1.00%
1.50%
2.00%
2.50%
3.00%
Dense Point Cloud Gridded Elevation Model
VolumetricDifferenceComparedtoLiDAR
UAS Software 1
UAS Software 2
UAS Software 3

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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 Bethel
Director of Technology
Merrick & Company
www.merrick.com
matt.bethel@merrick.com
(303) 353-3662