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Integration of Photogrammetric and LiDAR Data for True Orthophoto Generation

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Ayman F. Habib Photogrammetric & LiDAR Mapping 
Integration of Photogrammetric and LiDAR 
Data for True Orthophoto Generation 
Chapter 5 
Ayman F. Habib Photogrammetric & LiDAR Mapping 
2 
Overview 
• Photogrammetric and LiDAR Data: Why? 
• Photogrammetric and LiDAR Data: How? 
– Registration (co-alignment) paradigm 
– Experimental results from real data 
• True Orthophoto Generation & 3D Visualization 
– Z-Buffer method 
– Angle-Based method 
Ayman F. Habib Photogrammetric & LiDAR Mapping 
3 
Laser 
Unit 
2
tc
Camera 

A
A
a 'a
Ranging Unit 
Scanning Mechanism 
Photogrammetric System LiDAR System 
GPS /INS 
Y 
Z 
X 
Y 
Z 
X 
Y 
Z 
X 
GPS /INS 
Control Points 
Indirect Geo-referencing Direct Sensor Orientation 
Photogrammetric & LiDAR Data 
Ayman F. Habib Photogrammetric & LiDAR Mapping 
4 
Optical Imagery LiDAR Range Data 
Photogrammetric & LiDAR Data 
Ayman F. Habib Photogrammetric & LiDAR Mapping 
5 
LiDAR Range Data 
Optical Imagery 
Photogrammetric & LiDAR Data 
Ayman F. Habib Photogrammetric & LiDAR Mapping 
6 
Optical Imagery 
LiDAR Intensity Image 
Photogrammetric & LiDAR Data 
Ayman F. Habib Photogrammetric & LiDAR Mapping 
7 
• Photogrammetric data: 
 High redundancy 
 Rich with semantic information 
 Dense positional information along object space break-
lines 
 Straightforward procedures for quality assurance and 
quality control 
– Almost no positional information along homogeneous 
surfaces 
– Complicated and sometimes unreliable automated 
matching procedures (especially when dealing with 
large scale imagery over urban areas) 
Photogrammetric & LiDAR Data 
Ayman F. Habib Photogrammetric & LiDAR Mapping 
8 
• LiDAR data: 
 Direct and simple acquisition of positional information 
 Dense positional information along homogeneous 
surfaces 
– Almost no positional information along object space 
break-lines 
– No redundancy 
– Mainly positional information (no semantic 
information) 
– No common standards for quality assurance and quality 
control 
Photogrammetric & LiDAR Data 
Ayman F. Habib Photogrammetric & LiDAR Mapping 
9 
LiDAR Range Data 
Optical Imagery 
Photogrammetry + LiDAR: Why? 
Ayman F. Habib Photogrammetric & LiDAR Mapping 
10 
Photogrammetry + LiDAR: Why? 
• The two technologies deliver complementary 
information. 
– Photogrammetric processing of imagery will produce 
accurate information regarding break-lines 
(discontinuities) within the photographed area. 
– On the other hand, LiDAR will provide accurate 
information related to homogeneous surfaces. 
• The synergism between these sensors results in a 
more complete description of the objects. 
– Complete description of objects is useful for change 
detection applications. 
Ayman F. Habib Photogrammetric & LiDAR Mapping 
11 
Complete Surface Description 
(X, Y, Z): 1122.23 m, 3251.53 m, 72.03m 
(R, G, B): 23, 136, 69 
Ayman F. Habib Photogrammetric & LiDAR Mapping 
12 
• Sensor calibration 
• Understand the data collection/processing scheme 
of a particular sensor/algorithm 
– Factors affecting the shape of the received waveform in 
a LiDAR system 
– Analysis of an automatically generated DSM from 
stereo-photogrammetry 
• Successful integration of these sensors is 
contingent on having the data from both systems 
relative to the same reference frame. 
Photogrammetry + LiDAR: Why? 
Ayman F. Habib Photogrammetric & LiDAR Mapping 
13 
LiDAR (Pros) Photogrammetry (Cons) 
Dense information along 
homogeneous surfaces 
 
Almost no positional 
information along 
homogeneous surfaces 
Day or night data collection Day time data collection 
Direct acquisition of 3D 
coordinates 
Complicated and sometimes 
unreliable matching procedures 
Vertical accuracy is better than 
the planimetric accuracy 
Vertical accuracy is worse than 
the planimetric accuracy 
Photogrammetric & LiDAR Data 
Ayman F. Habib Photogrammetric & LiDAR Mapping 
14 
Photogrammetry (Pros) LiDAR (Cons) 
High redundancy No inherent redundancy 
Rich with semantic 
information 
Positional; difficult to derive 
semantic information 
Dense positional information 
along object space break-lines 
Almost no information along 
break-lines 
Planimetric accuracy is better 
than the vertical accuracy 
Planimetric accuracy is worse 
than the vertical accuracy 
Transparent Model Non-Transparent Model 
Photogrammetric & LiDAR Data 
Ayman F. Habib Photogrammetric & LiDAR Mapping 
15 
Photogrammetry & LiDAR: How? 
• The attainment of the synergistic characteristics of 
photogrammetric and LiDAR data is contingent on 
the co-registration (alignment) of both datasets 
relative to the same reference frame. 
– Alignment of photogrammetric and LiDAR data 
relative to the same reference frame 
• In the next few slides, the key issues of the 
registration procedure will be explained. 
Ayman F. Habib Photogrammetric & LiDAR Mapping 
16 
Data Registration 
• In general, the registration aims at combining data 
and/or information from multiple sensors in order 
to achieve improved accuracy and better inference 
about the environment than could be attained 
through the use of a single sensor. 
– “The whole is more than the sum of its parts.” 
• The enormous increase in the volume of spatial 
data that is being acquired by an ever growing 
number of sensors mandates the development of 
accurate and robust registration procedures that 
can handle spatial data with varying formats. 
Ayman F. Habib Photogrammetric & LiDAR Mapping 
17 17 
Input perspective imagery 
Data Registration 
Ayman F. Habib Photogrammetric & LiDAR Mapping 
18 18 
• Impact of miss-registration 
– Produced orthophoto from 
optical imagery and LiDAR 
data using an independent 
source of control for 
photogrammetric geo-
referencing. 
Data Registration 
Ayman F. Habib Photogrammetric & LiDAR Mapping 
19 19 
• Proper registration: 
– Produced orthophoto from 
optical imagery and LiDAR 
data using LiDAR as the 
source of control for 
photogrammetric geo-
referencing. 
 
Data Registration 
Ayman F. Habib Photogrammetric & LiDAR Mapping 
20 
Registration: Key Issues 
• Registration primitives: 
– Distinct points, linear features, or areal features 
• Registration transformation parameters: 
– Relationship between the reference frames of the 
involved datasets 
• Similarity measure: 
– Ensure the correspondence of conjugate primitives 
• Matching strategy: 
– Controlling framework that utilizes the primitives, 
transformation parameters, and the similarity measure 
to solve the registration problem 
Ayman F. Habib Photogrammetric & LiDAR Mapping 
21 
Registration Primitives 
type, representation, and 
extraction 
Registration 
Transformation Parameters 
Similarity Measure - to ensure the coincidence of 
conjugate primitives after applying the 
transformation parameters 
Registration: Key Issues 
Ayman F. Habib Photogrammetric & LiDAR Mapping 
22 
Registration Primitives 
Ayman F. Habib Photogrammetric & LiDAR Mapping 
23 
Registration Primitives 
• Conjugate features that could be identified in both 
datasets 
• These features will be used for the alignment of 
the LiDAR and photogrammetric datasets relative 
to the same reference frame. 
• Alternatives: 
– Points, 
– Linear features, or 
– Areal features 
Ayman F. Habib Photogrammetric & LiDAR Mapping 
24 
Points Primitives 
The most commonly used primitive 
in photogrammetry 
Photogrammetry 
 
 
It is almost impossible to link a 
laser footprint with a distinct point 
in the image 
LiDAR 
 
Ayman F. Habib Photogrammetric & LiDAR Mapping 
25 
Linear Features 
LiDAR Photogrammetry 
LiDAR cloud Image patch 
Image patch LiDAR intensity image 
   
Ayman F. Habib Photogrammetric & LiDAR Mapping 
26 
Areal Features 
LiDAR Photogrammetry 
 
 
 
LiDAR cloud Image patch 
Ayman F. Habib Photogrammetric& LiDAR Mapping 
27 
• Point primitives: 
– It is almost impossible to correlate a laser beam 
footprint with the corresponding point in optical 
imagery. 
• Linear features: 
– Can be reliably extracted from LiDAR and 
photogrammetric data 
• Areal features: 
– Easy to extract from LiDAR data 
– Object boundaries can be used to extract areal features 
from photogrammetric data. 
Registration Primitives 
Ayman F. Habib Photogrammetric & LiDAR Mapping 
28 
• LiDAR linear features can be extracted through 
either: 
– Segmentation, plane fitting, and plane intersection. 
• Utilizes the original point cloud 
• Very accurate 
– Utilization of range and intensity data 
• Requires prior interpolation of the LiDAR point cloud into a 
uniform grid 
• Planimetric coordinates are derived from the intensity image 
while the corresponding height information is derived from the 
range data. 
• Less accurate 
Registration Primitives: LiDAR Data 
Ayman F. Habib Photogrammetric & LiDAR Mapping 
29 
Linear Features from LiDAR (1) 
• First: Planes are fitted through homogeneous 
patches. 
– Surface roughness can be used to reject points that do 
not belong to the patch in question. 
• Linear features are obtained by intersecting 
neighboring planar patches. 
Ayman F. Habib Photogrammetric & LiDAR Mapping 
30 
LiDAR: 
 
Method 1: Through patch intersection 
manual identification of LiDAR 
patches with the aid of imagery 
Linear Features from LiDAR (1) 
Ayman F. Habib Photogrammetric & LiDAR Mapping 
31 
LiDAR: 
Method 2: From range and intensity images 
Raw point cloud 
Range image 
2-point 
representation 
Intensity 
image 
Z reading 
XY coordinates 
Interpolated 
grid 
Interpolation 
Technique and 
Spacing 
Linear Features from LiDAR (2) 
Ayman F. Habib Photogrammetric & LiDAR Mapping 
32 
LiDAR: 
Method 2: From range and intensity images (example) 
Linear Features from LiDAR (2) 
Ayman F. Habib Photogrammetric & LiDAR Mapping 
33 
LiDAR: 
Z
Y
X
Ground Coordinate System
Object Space 
Line
(X2,Y2,Z2)
(X1,Y1,Z1)
 
LiDAR line = Object space line: 
• Two end points 
Linear Features Representation 
Ayman F. Habib Photogrammetric & LiDAR Mapping 
34 
Photogrammetry: 
 
Direct measurement of intermediate points on images 
G
33
C
`
G
31
C
G
33
C
G
33
C
G
33
C
G
31
C
G
31
C
G
31
C
e.g.: measuring line intermediate points 
Linear Features Representation 
Ayman F. Habib Photogrammetric & LiDAR Mapping 
35 
G
33
C
G
31
C
G
33
C
G
33
C
G
33
C
G
31
C
G
31
C
G
31
C
LiDAR Photogrammetry 
Linear Features Representation 
Ayman F. Habib Photogrammetric & LiDAR Mapping 
36 
Transformation Parameters & Similarity 
Measure 
Ayman F. Habib Photogrammetric & LiDAR Mapping 
37 
• Transformation Parameters: Parameters relating 
the reference frames of photogrammetric and 
LiDAR datasets 
• Similarity Measure: Mathematical constraint that 
describes the coincidence of photogrammetric and 
LiDAR primitives after the application of the 
registration transformation parameters 
Similarity Measure and Transformation Parameters 
Ayman F. Habib Photogrammetric & LiDAR Mapping 
38 
Image 
line 
LiDAR 
control line 
Projected 
LiDAR line 
1 
2 
 
 
 
 
 
 
 
 
 
 
- 
- - 
- - 
K   F   W    
 
c 
y s distortion y y 
x s distortion x x 
R V p i 
p i 
) , , ( 3 
 
 
 
 
 
 
 
 
 
 
  - 
  - 
  - 
 
 
O 
O 
O 
Z Z 
Y Y 
X X 
V 
2 
2 
2 
2 
 
 
 
 
 
 
 
 
 
 
  - 
  - 
  - 
 
 
O 
O 
O 
Z Z 
Y Y 
X X 
V 
1 
1 
1 
1 
) , , ( O O O Z Y X       ) , , ( O O O Z Y X    
) , , ( K  F  W  
i ' 
i 
) , , ( K   F   W   
  0321  VVV

Similarity Measure and Transformation Parameters 
Ayman F. Habib Photogrammetric & LiDAR Mapping 
39 
v1 
v2 
vA 
vB 
Coplanar 
0)(
0)(
2
1


VVV
VVV
BA
BA


• The LiDAR linear features will be used as the 
source of control (i.e., establish the datum) for the 
photogrammetric model. 
Similarity Measure and Transformation Parameters 
Ayman F. Habib Photogrammetric & LiDAR Mapping 
40 
• The triple product in the previous equations will 
ensure the coincidence of: 
– The image line, and 
– The object line after being projected into the image space. 
• The triple product has an implicit assumption that the 
transformation parameters between the LiDAR and 
photogrammetric reference frames represents a 3-D 
similarity transformation. 
– Assumes the absence of any biases between the two data 
systems, which cannot be represented by such a 
transformation. 
• The quality of fit of the final registration could be used to verify 
such an assumption. 
Similarity Measure and Transformation Parameters 
Ayman F. Habib Photogrammetric & LiDAR Mapping 
41 
Areal Features 
LiDAR Photogrammetry 
 
 
 
LiDAR cloud Image patch 
Ayman F. Habib Photogrammetric & LiDAR Mapping 
42 
u1A. 
u1P. 
u1B. 
Areal Features Representation 
LiDAR Photogrammetry 
Ayman F. Habib Photogrammetric & LiDAR Mapping 
43 
0
1
1
1
1

---
---
---

ACACAC
ABABAB
AiAiAi
CCC
BBB
AAA
iii
ZZYYXX
ZZYYXX
ZZYYXX
ZYX
ZYX
ZYX
ZYX
Volume of the pyramid: 
 i, A, B, C should = 0 
A 
B 
C 
a’ 
b’ 
c’ 
LiDAR point, i 
a” 
b” 
c” 
Similarity Measure and Transformation Parameters 
Ayman F. Habib Photogrammetric & LiDAR Mapping 
44 
Experimental Results 
Ayman F. Habib Photogrammetric & LiDAR Mapping 
45 
Datasets: Imaging Systems 
Camera model Kodak 14N RC10 
Focal length (mm) ~51.5 153.167 
Frame size (W x H) 4500 x 3000 pxl 9” x 9” 
# of captured images 9 7 
Avg. flying height (m) 1315 1375 
Avg. base (m) 250 700 
Pixel size (mm) 0.008 0.024 
Expected image measurement 
accuracy (mm) 
± 0.008 ± 0.024 
Expected accuracy (assuming one pixel measurement error) 
planimetric (m) 0.25 0.21 
vertical (m) 1.19 0.60 
Ayman F. Habib Photogrammetric & LiDAR Mapping 
46 
Analog Digital 
Expected accuracy: 
planimetric: 0.21m / vertical: 0.60m 
spatial: 0.64m 
Expected accuracy: 
planimetric: 0.25m / vertical: 1.19m 
spatial: 1.22m 
Datasets: Imaging Systems 
Ayman F. Habib Photogrammetric & LiDAR Mapping 
47 
• Scanner: OPTECH ALTM 2050 
laser scanner (1,064 nm laser) 
• Average flying height: 975m 
• Mean point density: 2.24 
points/m2 (~0.7m point spacing) 
• The first and last responses of 
the range and intensity data were 
recorded 
Dataset: LiDAR System 
Ayman F. Habib Photogrammetric & LiDAR Mapping 
48 
Control points: 11 Control lines: 27 Control lines: 109 
24 check points 24 check points 24 check points 
RMSE, m RMSE, m RMSE, m 
X 0.28 0.31 0.29 
Y 0.18 0.21 0.20 
Z 0.25 0.40 0.35 
Check points analysis for point- and line-controlled bundle adjustment 
LiDAR - RC10 Analog Camera 
 
 
 
Experimental Results 
Ayman F. Habib Photogrammetric & LiDAR Mapping 
49 
Check points analysis for point- and line-controlled bundle adjustment 
LiDAR – KODAK 14n Digital Camera 
Control points: 8 Control line: 24 Control line: 103 
48 check points 48 check points 48 check points 
RMSE, m RMSE, m RMSE, m 
X 0.33 0.31 0.26 
Y 0.27 0.29 0.34 
Z 0.79 0.83 0.68 
Experimental Results 
Ayman F. Habib Photogrammetric & LiDAR Mapping 
50 
 Straight-line features proved to be suitable in establishing a 
common reference frame for the photogrammetric and LiDAR 
surfaces. 
 LiDAR-based and GCP-based photogrammetric results are 
quite comparable. 
Conclusions 
Experimental Results 
Ayman F. Habib Photogrammetric & LiDAR Mapping 
51 
Photogrammetric Data 
LiDAR Data 
Integrated Product 
Photogrammetric & LiDAR Data 
Ayman F. Habib Photogrammetric & LiDAR Mapping 
52 
Photogrammetric & LiDAR Data 
3D Realistic Model 
AKAM_MOSAIC_Korea.avi
Ayman F. Habib Photogrammetric & LiDAR Mapping 
53 
Photogrammetric & LiDAR Data 
3D Realistic ModelAyman F. Habib Photogrammetric & LiDAR Mapping 
54 
Digital Orthophoto 
Ayman F. Habib Photogrammetric & LiDAR Mapping 
55 
Orthophoto 
• Orthophoto: 
– Relief displacement free image 
– Image which has the same characteristics of a map 
• Orthogonal (parallel) projection 
• Uniform scale 
• No relief displacement 
Ayman F. Habib Photogrammetric & LiDAR Mapping 
56 
• The objective of rigorous rectification is the 
assignment of grey values from the image (aerial 
or satellite imagery) to each cell within the 
orthophoto while removing the sensor tilt and 
terrain relief effects. 
• After the rectification, both the elevation and the 
grey/color values are stored at the same location 
along the datum. 
Rigorous Rectification 
Ayman F. Habib Photogrammetric & LiDAR Mapping 
57 
Rigorous Rectification 
• Input: 
– Digital Image 
– EOP of that image 
– IOP of the used camera 
– Digital Elevation/Surface Model 
• Output: 
– Digital image which has the same characteristics of a 
map (Orthophoto) 
Ayman F. Habib Photogrammetric & LiDAR Mapping 
58 
Digital Image 
PC 
(x, y) 
Backward Projection 
Datum 
Terrain 
g 
(Resampling) 
G(X, Y) = g (x, y) 
Z(X, Y) 
Interpolation 
(X, Y) 
Orthophoto Generation: Indirect Trans. 
Differential Rectification 
Ayman F. Habib Photogrammetric & LiDAR Mapping 
59 
x 
y 
pc 
Backward Projection: Frame Camera 
Straightforward Procedure 
Ayman F. Habib Photogrammetric & LiDAR Mapping 
60 
• Procedure: 
– Define a uniform grid over the orthophoto plane 
(datum) 
– For each grid element (X, Y) in the orthophoto plane 
interpolate for the corresponding elevation → Z(X, Y) 
– Using the EOP and IOP together with the collinearity 
equations find the corresponding image point (x, y) 
– Find g(x, y) using one of the resampling techniques 
– G(X, Y) = g(x, y) 
– Repeat the above procedure for all the pixels in the 
orthophoto plane 
Differential Orthophoto Generation 
Ayman F. Habib Photogrammetric & LiDAR Mapping 
61 
Original Image 
Ayman F. Habib Photogrammetric & LiDAR Mapping 
62 
Orthophoto Generation 
Ayman F. Habib Photogrammetric & LiDAR Mapping 
63 
Digital Image 
PC 
Orthophoto & Relief Displacement 
Ghost Images (Double Mapping Problem) 
Ayman F. Habib Photogrammetric & LiDAR Mapping 
64 
Orthophoto Generation 
Ayman F. Habib Photogrammetric & LiDAR Mapping 
65 
True Orthophoto Generation 
Ayman F. Habib Photogrammetric & LiDAR Mapping 
66 
Generated Orthophoto Original Imagery 
Invisible (occluded) 
areas 
True Orthophoto Generation 
Ayman F. Habib Photogrammetric & LiDAR Mapping 
67 
perspective center 
imagery 
0 0 1 1 1 2 0 
A 
B C 
D 
a b c 
D 
C 
P.C 
longer 
Invisible point 
Digital Surface Model 
Orthophoto 
True Orthophoto Generation 
Z-Buffer Method 
Ayman F. Habib Photogrammetric & LiDAR Mapping 
68 
Generated True Orthophoto Original Imagery 
True Orthophoto Generation 
Z-Buffer Method 
Ayman F. Habib Photogrammetric & LiDAR Mapping 
69 
 
True Orthophoto Generation 
Z-Buffer Method 
• Sensitive to the relative relationship between the DSM 
cell size and the GSD of the imaging sensor 
Ayman F. Habib Photogrammetric & LiDAR Mapping 
70 
perspective center 
A 
B Invisible point 
D E 
C 
5 ° visible 
12° visible 
15° invisible 
12 ° 
15° 
14° 
A 
B 
C 
D 
E 20° 15° visible 
max 
angle 
visible 
/ hidden 
point angle comparison 
0° visible 5° > 
> 
> 
< 
> 
Nadir point 
Digital Surface Model 
Ortho-photo 
Angle-Based True Orthophoto Generation 
Frame Cameras: Single Nadir Point 
Ayman F. Habib Photogrammetric & LiDAR Mapping 
71 
Differential Orthophoto Generation 
Ghost Images 
Double Mapping Problem 
Ayman F. Habib Photogrammetric & LiDAR Mapping 
72 
Angle-Based True Orthophoto Generation 
Ayman F. Habib Photogrammetric & LiDAR Mapping 
73 
True Orthophoto After Occlusion Filling 
Ayman F. Habib Photogrammetric & LiDAR Mapping 
74 
perspective center 
A 
B Invisible point 
D E 
C 
5 ° visible 
12° visible 
15° invisible 
12 ° 
15° 
14° 
15° 
A 
B 
C 
D 
E 20° visible 
max 
angle 
visible 
/ hidden 
point angle comparison 
0° visible 5° > 
> 
> 
<= 
> 
Nadir point 
Digital Surface Model 
Orthophoto 
Occlusion Extension 
16° 
Angle-Based True Orthophoto Generation 
Ayman F. Habib Photogrammetric & LiDAR Mapping 
75 
True Orthophoto After Occlusion Filling 
Ayman F. Habib Photogrammetric & LiDAR Mapping 
76 
True Orthophoto After Occlusion Extension 
Ayman F. Habib Photogrammetric & LiDAR Mapping 
77 
Integrated Product 
(X, Y, Z): 1122.23 m, 3251.53 m, 72.03 m (±10 – ±30cm) 
(R, G, B): 23, 136, 69 
Accurate Enough for Engineering Applications 
Ayman F. Habib Photogrammetric & LiDAR Mapping 
78