Accuracy comparison of Digital Surface Models created by UAS ...

Accuracy comparison of Digital Surface Models created byUAS imagery and Terrestrial Laser Scanner

Matthias Naumann, Michael Geist, Ralf Bill, Frank

Niemeyer, Görres Grenzdörffer

Rostock University

Professorship for Geodesy & Geoinformatics

UAV-g2013, 4 – 6 September 2013, Rostock, Germany

Agenda

� Motivation

� Study area

� Methods and implementation

� Accuracy comparison

� Summary

UAV-g2013, 4 – 6 September 2013, Rostock, Germany 2

Motivation

� UAS: rapid autonoumous image acquisition of small areas from low altitudes

� UAS photogrammetry: largely automatedgeneration of DSM and ortho photos

� Compared to Terrestrial Laserscanning (TLS) there are some advantages:

� Less influence of objects in the survey area due to the bird's eye view

� Less time required on site for the measurement (in our case by factor 0.5)

� Easy to use Web processing services

� What is the precision of UAV-DSM compared to TLS-DSM?


Study area

� Test site – a pilot dike (40 x 140 m)

� Research project “DredgDike” (initiated by the University of Rostock, Prof. Dr.-Ing. Fokke Saathoff)

� Connects 3 polders to simulate different hydraulic loads

� Multi-year large-scale field trials

� Dredged materials as future cover layer for dike constructions?

� Monitoring of subsidence, consolidation and increased surface erosion

� High-resolution DSM from different times are needed


Simplified 3D-Modell of the pilot dike Copyright:

Rostock University, Chair of Environmental Geotechnics, Landscape Construction and Coastal Engineering)

Polder 1 of the Pilot dikeCopyright: Rostock University, Geodesy and Geoinformatics, Matthias Naumann

Methods of DSM measurement

� UAS photogrammetry and TLS scanning are several methods for the generation of 3D models in geodetic 3D accuracy

� TLS is an established method for the determination of deformation and changes on buildings and natural surfaces

� TLS creates 3D point clouds in situ and in accordance with the measuring principle of a Total Station (tacheometer)

� Terrestrial method – View mainly from the side (oblique), only rarely from above

� UAS do have potential for rapid image acquisition.

� Sophisticated computation processes (photogrammetry, computer vision) enable automatic generation of 3D point clouds, DSM and ortho photos.

� Needs some Ground Control Points

� Aerial method – View from above, and possibly also oblique


� Size and extent of the pilot dike is well suited for both, TLS and UAS imagery

UAS –camera

� Microdrones MD4-1000

� Flight time about 25 min with 1 kg Payload

� Digital camera Olympus PEN e-P2

� Resolution: 4032 x 3024 Pixel

� Chip size: 17.3 x 13 mm²

� Focal length: 17 mm

� Roll / Pitch: 54°/ 42°

� Pixel size: 4.3 µm

� Target Ground Sampling Distance (GSD): ca. 2 cm

� Resulting flight altitude: ca. 80-85 m

UAV-g2013, 4 – 6 September 2013, Rostock, Germany

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Copyright: Rostock University, Geodesy and Geoinformatics,

focal

Altitude

pixelsize

GSD=

UAS –Image overlapping

� Size of image footprint (Nadir) at 85 m:

� 86.5 x 65 m²

� Calculation of overlapping (landscape format):

� Along 90%: 65 m – (0.9 * 65 m) = 6.5 mImage sequence distance: 6.5 m

� Sideways 80%: 86.5 m – (0.8 * 86.5 m) = 17.3 mImage overlap between strips: 17.3 m

� Storage time of camera (SD-card class 10):

� ca. 1 sec.

� Max. Speed of MD 4-1000:

� 5 m/sec.

� Image trigger intervall = 6.5 m / 5 m/sec = 1.3 sec.


UAS –Flight path

� Strongly overlapping images (90%, 70%)

� 5 stripes (3.14 ha), trigger 1.3 sec. (6.5 m)

� 11 Ground Control Points by RTK-GPS

� 0.013 m ± 0.004 m (Leica SmartWorx)

� 0.019 m ± 0.011 m (Pix4UAV)

� Average GSD: 2.1 cm (Pix4UAV)

� Average altitude: 81.2 m (Pix4UAV)


Flight plan

Distribution of GCPs

UAS –Ground Control Points

� 11 Ground Control Points (GCPs)

� Temporarily arranged on and around the dike

� Laminated cardboard and aluminium disc

� 3D position determined by RTK-GPS in ETRS89-UTM33N

� Overall positioning mean error (determined by Leica Smart Worx):

� 0.013 m (std. dev.: 0.004 m)

� Mean 3D localisation error (determined byPix4UAV):

� 0.019 m (std. dev. 0.011 m)


UAS-DSM

� Geoprocessing the UAS-imagery with web processing service Pix4UAV Cloud

� Average GSD: 0.021 m

� 5649 matches per calibrated image

� Mean reprojection error: 0.14 px


Terrestrial Laserscanning(TLS)

� Delay of 3 weeks

� Zoller+Fröhlich Imager 5010i (Fraunhofer AGP Rostock)

� 16 Scans: 9 times around the dike, 7 times on top

� Scan resolution of 3 mm per 10 meters

� Temporarily targets: 13 flat disks, 5 spherical targets

� 3D-Position determined by Total Station (Leica TCRP 1205):

� Standard deviation: ca. 10 mm (to the benchmark survey network)

� Pre-registration using these targets:

� Standard deviation: 3 mm, Max. deviation: 9 mm (Z+F LaserControl)

� Co-registration using best-fit solution on identical areas:

� Standard deviation: ca. 2-3 mm (InnovMetric Polyworks)


Copyright: Zoller+Fröhlich

TLS-DSM

� All scans filtered by preprocessingfilters of Z+F LaserControl to remove outliers

� E.g. Mitigation against abrasive cuts

� Only points with distance up to 30 m to the scanner

� Meshing of the points to the surface model (InnovMetric Polyworks)

� Density of TLS-DSM is about factor 5 higher than UAV-DSM


Overflow area 4 in Polder 2 (TLS-DEM)

Overflow area 4 in Polder 2 (UAV-DEM)

Effort comparision

� UAS DEM is in our case about factor 2 faster (5 h : 10 h)

� Factor may increase for larger or more complex object

� increase number of scan locations

� Increasing effort for scan registration


0

500

1000

1500

2000

2500

1 2 3 4 5 6 7 8

tim

e

eff

ord

[m

inu

tes]

factor of increasing the area

time effort for themeasurement on site

time_UAV

time_TLS

� Man-made reworks and construction works

� Terrain modelling around to the dike

� Small reworking in parts of the dike

� Natural processes (lower magnitude)

- Subsidence (1cm), Erosion (negligible), Vegetation (some parts disturbing)

Handling the time delay


Full extent of the DSMs after processing

Cutting to the extent of the dike

Comparison over the entire extent of the dike

Selection of characteristic parts

Comparison in parts of the dike

TLSUAS

Time [week]

0 3

Accuracy comparison

� Error budgets of both methods are investigated

� in total extent of the dike

� and in parts of the dike

� manually defined patches (12 planes)

� and cross-sections (4 segments)

� Each UAS surface element was assigned to the closest TLS surface element

� Differences between next adjacent elements were compared (not Z-directions)

� Avoids overstate of local height errors due incorrect locations


- - - Z-difference

- - - Nearest-difference

UAS-DEM

TLS-DEM

Accuracy over entire model

� Largely, magnitude of the surface deviations have a random character

� In some areas smaller local systematic effects (see the legend)

� Empirical standard deviation:


Without differences

exceeding 10 cmAll Differences

#Points 681338 685977

Mean [m] 0.000 0.000

StdDev [m] 0.022 0.040

Max [m] 0.100 1.369

Min [m] -0.100 -1.997

Legend:

UAS - TLS:

Blue Ellipse: Fill

Red Ellipse: Removal

Subsidence (Geo-textiles, Geo-grids )

Green Ellipse: Vegetation

Accuracy in parts

� Segment analysis

� Standard deviation: ca. 0.022 m

� In segment 1: randomly deviations

� In segment 4: small reworks can be recognized


Seg. 1 Seg. 2 Seg. 3 Seg. 4

#Points 42543 45329 53128 37993

Error Mean [m] -0.010 -0.001 0.009 0.001

Error StdDev [m] 0.022 0.023 0.020 0.024

Error Max [m] 0.274 0.212 0.271 0.234

Error Min [m] -0.124 -0.239 -0.185 -0.321

Error Range [m] 0.398 0.363 0.309 0.445 Segment 1 (Polder 1)

Segment 4 (Polder 3)

Accuracy in parts

� For 12 patches best-fitting planes

� Regression analysis with minimizing the square error between actual and calculated Z-value for each method

� For each pair of the 12 planes calculation of differences between the two adjusted planes

� Largely random character

� They differ by an average of 0.007 m

� Range of -6.2 to 4.3 cm


Small objects analysis - 1

� Terrain kink could be detected in the UAS data

� Center buckling protrudes in both models in the range of about 3 cm with respect to the adjusted tangent planes

� Differentiability is even better with the TLS measurements

� But accuracy potential of the UAS model could also be illustrated


Detail view


� Example 1: Detailing of vertical objects (walls):

� TLS better results

� UAS-DSM should be improved by using oblique images

� Example 2: Detailing of trenches or manholes: UAS better results

Small objects analysis


Summary

� Time for on-site measurements significantly lower

� UAS is slightly less accurate and more scattered at objects

� Lower resolution � Increasing the resolution by reducing the altitude

� Installations (jumps in height, sharp edges, vertical surfaces)

� High level of agreement in areas with gradual curvature change

� UAS shows mainly a standard deviation of about 2.2 - 3.5 cm

� Mainly homogeneous accuracy (TLS more depends on scan stations)

� Proposals for improvements of resolution and precision:

� Reducing the flight altitude

� Improve the DSM through oblique images

� Complex flight patterns e.g. for vertical objects, shades areas:

- parallel along trenches or walls on both sides

- Altitude adapted to height differences (oblique-looking camera)



Thank you for your attention!

Dipl.-Ing. (FH) MSc. (GIS) Matthias Naumann

Rostock University

Professorship for Geodesy & Geoinformatics

[email protected]

Accuracy comparison of Digital Surface Models created by UAS ...

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