1. A Preliminary Mapping Accuracy Assessment of UAV Photogrammetry
A Presentation at
International Symposium on Remote Sensing (ISRS 2017)
Nagoya, Japan
17th – 19th May 2017
A Preliminary Mapping Accuracy Assessment of UAV Photogrammetry
Shofiyatul Qoyimah1 and Yi-Hsing Tseng2
1Master Student and 2Professor
Department of Geomatics
National Cheng Kung University, Taiwan (R.O.C)

2. Outline Motivation Methodology Result Conclusion 1
Background & Problem
Methodology
Study Area, Equipment, Data and Workflow
Result
IOP Evaluation and Aerial Triangulation
Conclusion
Australis, Pix4Dmapper, S.O.D.A 1
International Symposium on Remote Sensing 2017, Japan, 17th – 19th May 2017

3. = / ≠ Unmanned Aerial Vehicle (UAV) ???? DRONE
Potential Growth in Commercial Use of UAS
Entertaining / Recording
Toys
Hobbyists
Aerial Photography
Protecting / Inspecting
Military
Public Safety
Wildlife
Oil Rigs
Agriculture
Mining
Bridges
Evaluating / Managing
Situational Awareness
Operations Management
Asset Tracking
Modelling/mapping
Environmental Monitoring
Delivering / Transporting
Online Retail
Local Stores
Restaurants
Legal Papers
Medical
2019 …
… 2014
Unmanned Aerial Vehicle (UAV) ????
= / ≠
DRONE
Source : Unmanned Aircraft Systems (UAS): Commercial Outlook for a New Industry, 2015
Motivation Methodology Result Conclusion
Rescuer Drone
Drone Sprayer
Drone Delivery Express
“Dronie”
Drone Racing
Source : 2
International Symposium on Remote Sensing 2017, Japan, 17th – 19th May 2017

4. Motivation Methodology Result Conclusion
Source : Close Range Photogrammetry – Principles, Methods and Applications, 2006
Accuracy of Measurement Methods
Motivation Methodology Result Conclusion
Mapping
Photogrammetric measurement platform using UAV that equipped with a photogrammetric measurement system (camera)
(Eisenbeiss, 2008)
UAV Photogrammetry
Image Source : 3
International Symposium on Remote Sensing 2017, Japan, 17th – 19th May 2017
How good is UAV Photogrammetry applied in mapping ???

5. Motivation Methodology Result Conclusion
Taiwan (R.O.C)
-Nantou County-
Motivation Methodology Result Conclusion
Study Area (NanGang Industrial Area)
Taiwan (R.O.C)
-Nantou County-
Image Source : Google Earth 4
International Symposium on Remote Sensing 2017, Japan, 17th – 19th May 2017

6. Australis Photometrix Image Station Automatic Triangulation (ISAT)
Motivation Methodology Result Conclusion
Equipment
SenseFly Sensor Optimized for Drone Application (S.O.D.A) Camera
RGB sensor
1-inch sensor size
2.4 micron pixel pitch
20 MP resolution
eBee Plus SenseFly UAV
Wingspan UAV model
1.1 kg weight
3 km radio link range
59’ maximum flight time
Source :
Software
Pix4Dmapper
For Aerial Triangulation
Australis Photometrix
For IOP Evaluation
Image Station Automatic Triangulation (ISAT)
For Aerial Triangulation 5
International Symposium on Remote Sensing 2017, Japan, 17th – 19th May 2017

7. Motivation Methodology Result Conclusion
Data
Ground Control Point
29 GCPs in TWD97 coordinate system
Measured and maintained by National Land Surveying and Mapping Center R.O.C (NLSC R.O.C)
Accuracy: horizontal = 5cm, vertical = 10cm
Geotagged Aerial Images
330 aerial images
1 km2 coverage area
80%, 70% overlap and sidelap
200 m flight height
4.610 cm GSD
Image Source : Google Earth 6
International Symposium on Remote Sensing 2017, Japan, 17th – 19th May 2017

8. Absolute Orientation Process Information
Motivation Methodology Result Conclusion
GCP Position
Geotagged Aerial Images
Workflow 1
Aerial Triangulation
(Pix4Dmapper)
Original Tie Point 3 2
Corrected Tie Point
Absolute Orientation Process Information
Aerial Triangulation
(ISAT)
IOP Evaluation (Australis) 7
International Symposium on Remote Sensing 2017, Japan, 17th – 19th May 2017

9. Motivation Methodology Result Conclusion
Ground Control Point (GCP) Position
Tie Point Coordinate
Exterior Orientation Parameter (EOP)
Interior Orientation Parameter (IOP)
Quality Report
Motivation Methodology Result Conclusion 1
Aerial Triangulation (Pix4Dmapper) R1 R2 R3
Initial
0.124
-0.411
0.377
Optimized
0.123
-0.427
0.399
Geotagged Aerial Images
GCP Position
Aerial Triangulation
(Pix4DMapper)
EOP
IOP
GCP position
Original Tie point (Case 1)
Quality Report
Problem:
Different definition of radial distortion parameter in Pix4D
IOP Evaluation 8
International Symposium on Remote Sensing 2017, Japan, 17th – 19th May 2017

10. Lens Distortion Correction
Motivation Methodology Result Conclusion
2.1
IOP Evaluation (original tie point lens distortion correction from Pix4D)
Image Coordinate x (mm)
Image Coordinate y (mm)
Radial Distortion from Pix4Dmapper
Original Tie Point (Case 1)
Radial Distortion Profile
Radial Distance (micron)
Distortion Value (micron)
17.9
16.6
IOP (Pix4D mapper)
IOP from Pix4Dmapper
IOP
Value
F (mm)
px (mm)
py (mm) R1
1.23E-01 R2
-4.27E-01 R3
3.99E-01 T1
-1.10E-03 T2
-2.13E-03
Lens Distortion Correction
Pix4D Corrected Tie Point (Case 2)
Hypothesis:
There is some error in S.O.D.A internal camera parameter 9
International Symposium on Remote Sensing 2017, Japan, 17th – 19th May 2017

11. Calibration & Distortion Correction
Radial Distortion Profile
Radial Distance (micron)
Distortion Value (micron)
-54.8
-35.8
Motivation Methodology Result Conclusion
2.2
IOP Evaluation using Australis
IOP from Australis
Image Coordinate x (mm)
Image Coordinate y (mm)
Radial Distortion (K1,K2,K3,K4,K5)
Radial Distortion (K1,K2,K3)
Tie point
IOP
Value
Standard Error
C (mm)
2.15E-20
XP (mm)
-0.091
YP (mm)
0.0266 K1
-1.08E-03 K2
3.31E-05
2.15E-24 K3
-2.74E-07
2.15E-30 P1
2.12E-04 P2
-1.10E-04 B1
-5.36E-33 B2
-3.99E-33 K4
2.17E-10
7.14E-12 K5
-3.98E-12
1.27E-13
Original Tie Point
(Case 1)
Calibration & Distortion Correction
with and without K4&K5
Corrected Tie Point with K4&K5 parameter (Case 3)
Corrected Tie Point without K4&K5 parameter (Case 4)
Hypothesis:
Something wrong with calibration process in Pix4Dmapper software
There is some error in S.O.D.A internal camera parameter 10
International Symposium on Remote Sensing 2017, Japan, 17th – 19th May 2017

12. Relative Orientation of Case 1
Relative Orientation Result (Case 1)
Before gave threshold
T = 10 pixel (24micron)
T = 5pixel
(12micron)
T = 3 pixel
(7.2 micron)
T = 2 pixel
(4.8 micron)
T =1pixel
(2.4 micron)
Sigma (micron)
6.4
6.3
4.7
2.5 1
0.4
RMS image x (micron)
6.2 5 3
1.7
0.9
RMS image y (micron)
5.4
5.3
4.1
2.8
Degree of Freedom (DOF)
129451
129233
112583
71057
20646
9830
Gross image blunder 2
Gross control blunder
Image blunders
328
162
Image point used
76161
76052
67727
46964
21757
16346
Motivation Methodology Result Conclusion 3
Aerial Triangulation (ISAT)
Aerial Triangulation – Case 1
Sigma naught <= 1 pixel
Give threshold to remove image blunder No
Yes
Case 1
Relative Orientation
Absolute Orientation
10 control point
14 check point
Tie point
Case 1, Case 2
Case 3, Case 4
Relative Orientation (T = 2 pixel)
Absolute Orientation
Images EOP
GCP position
Process Information
Tie Point
Tie point
Case 1, Case 2
Case 3, Case 4
Relative Orientation
Absolute Orientation
Images EOP
GCP position
Process Information
Tie Point
Relative Orientation of Case 1
Value
Sigma (micron)
6.4
RMS image x (micron)
6.3
RMS image y (micron)
5.4
Degree of Freedom (DOF)
129451
Gross image blunder 2
Gross control blunder
Image blunders
328
Image point used
76161
1 pixel = 2.4 micron 11
International Symposium on Remote Sensing 2017, Japan, 17th – 19th May 2017

13. Gross control blunders
Motivation Methodology Result Conclusion
Relative Orientation Result
Relative Orientation Result
Parameter
Case 1
Case 2
Case 3
Case 4
Sigma (micron) 1
1.8
1.3
1.5
RMS x (micron)
1.7 2
1.2
1.4
RMS y (micron)
DOF
20646
94831
121891
121465
Gross image blunders
Gross control blunders
Image blunders
Image Point used
21757
58851
72366
72162
Case 1 = Original tie point data from Pix4Dmapper
Case 2 = Tie point corrected by Pix4Dmapper lens distortion parameter
Case 3 = Corrected tie point by Australis (K1,K2,K3,K4,K5)
Case 4 = Corrected tie point by Australis (K1,K2,K3) 12
International Symposium on Remote Sensing 2017, Japan, 17th – 19th May 2017

14. Motivation Methodology Result Conclusion
Relative Orientation Result
Relative Orientation Result
Case 3 has the lowest value of RMS image x and y
Case 3 has the similar value of sigma naught with the case 1 (that has the lowest value)
Case 3 has the highest number of image point used 13
International Symposium on Remote Sensing 2017, Japan, 17th – 19th May 2017

15. Motivation Methodology Result Conclusion
Absolute Orientation Result
RMS Control and Check Point (Absolute Orientation)
RMS control point (m)
RMS check point (m) X Y Z
Case 1
0.048
0.035
0.084
0.119
0.205
1.073
Case 2
0.188
0.181
0.312
0.285
0.368
0.531
Case 3
0.046
0.075
0.115
0.051
0.128
Case 4
0.033
0.049
0.114
0.041
0.171
Compare the case 3 and case 4:
Case 4 has the smallest different RMS value between control point and check point in X and Y direction
Case 3 has the smallest different RMS value between control point and check point in Z direction 14
International Symposium on Remote Sensing 2017, Japan, 17th – 19th May 2017

16. Motivation Methodology Result Conclusion
Absolute Orientation Result
Sigma naught, RMS x and RMS y (Absolute Orientation)
Sigma naught (micron)
RMS image x (micron)
RMS image y (micron)
Case 1
0.9
1.9
1.8
Case 2 2
1.7
Case 3
1.3
1.2
Case 4
1.4
Case 3 has the smallest value of RMS image x and RMS image y
While it is the second lowest value of sigma naught 15
International Symposium on Remote Sensing 2017, Japan, 17th – 19th May 2017

17. Motivation Methodology Result Conclusion
Australis
Calibrating the original tie point can increase the measured point number and makes aerial triangulation process more accurate
Pix4Dmapper
Has different definition in radial distortion parameter that the calibrated IOP need to be evaluate using another software
S.O.D.A SenseFly camera
Still has some error in its camera system that affect the aerial triangulation result 16
International Symposium on Remote Sensing 2017, Japan, 17th – 19th May 2017

18. Reference
Canis, B. (2015). UNmanned Aircraft Systems (UAS): COmmercial Outlook for a new Industry. Washington,D.C: Congressional Research Service.
Eisenbeiss, H. (2008). UAV Photogrammetry in Plant Sciences and Geology. Povo (Torento), Italy: In : 6th ARIDA Workshop on "Innovations in 3D Measurement, Modelling and Visualization.
Luhmann, T. R. (2006). Close Range Photogrammetry - Principles, Methods and Applications. Caithness: Whittles Publishing.
SenseFly. (2017, January 20). eBee Plus Aerial efficiency, photogrammetric accuracy. Retrieved from sebseFly: 19
International Symposium on Remote Sensing 2017, Japan, 17th – 19th May 2017

19. Thank you
“Knowledge is that which benefits, not that which is memorized”
- Imam al-Shafi’i (ra) -

20. Absolute Orientation Information
Absolute Orientation Result of Case 1 and Case 2
Absolute Orientation Information
Case 1
Case 2
X/Omega/x
Y/Phi/y
Z/Kappa
RMS control point (m)
0.048
0.035
0.084
0.188
0.181
0.312
RMS check point(m)
0.119
0.205
1.073
0.285
0.368
0.531
max ground residual (m)
0.057
0.139
0.352
0.306
0.534
Mean STD dev photo position (m)
0.232
0.239
0.215
0.065
0.069
0.063
Mean STD dev photo attitude (degree)
0.135
0.12
0.144
0.036
0.032
0.026
mean image residual (micron)
RMS image residual (micron)
1.9
1.8 2
1.7
control point used 7 8
check point used 13 14
DOF
16555
95770
sigma naught
measured point
18716
58322
gross blunder
2873
497
image blunder
169 32
International Symposium on Remote Sensing 2017, Japan, 17th – 19th May 2017

21. Absolute Orientation Information
Absolute Orientation Result of Case 3 and Case 4
Absolute Orientation Information
Case 3
Case 4
X/Omega/x
Y/Phi/y
Z/Kappa
RMS control (m)
0.046
0.075
0.115
0.033
0.049
0.114
RMS check (m)
0.035
0.051
0.128
0.041
0.171
max ground residual (m)
0.076
0.182
0.19
0.057
0.113
0.21
Mean STD dev photo position (m)
0.023
0.022
0.026
0.025
Mean STD dev photo attitude (degree)
0.006
0.005
0.002
0.007
mean image residual (micron)
RMS image residual (micron)
1.2
1.4
control point used 10
check point used 14
DOF
123914
123465
sigma naught
measured point
72379
72162
gross blunder
image blunder
International Symposium on Remote Sensing 2017, Japan, 17th – 19th May 2017