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

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 Comparison

5.2

9.7

20.1

11.2

0

5

10

15

20

25


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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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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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

[email protected]

(303) 353-3662

ILMF booth #68

http://www.merrick.com/

mailto:[email protected]

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

Engineering

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