The Photogrammetric Journal of Finland, Vol. 22, No. 1, 2010
Received 11.10.2009, Accepted 26.04.2010
INDIRECT ORIENTATION OF IMAGES USING CONTROL POINTS
EXTRACTED BY THE MEANS OF MONOPLOTTING MODEL
Daniel R. dos Santos1, Aluir P. Dal Poz2, and Quintino Dalmolin1
1
2
Federal University of Paraná, Department of Geomatic, Curitiba, Brazil
State University of São Paulo, Department of Cartographic Engineering,
Presidente Prudente, Brazil
[email protected]; [email protected]; [email protected]
ABSTRACT
This article presents a method for extracting control points from photogrammetry and light
detection and ranging (LiDAR) data integration for the indirect orientation of images. Its most
important characteristic is control point extraction from the proposed monoplotting model, which
allows the projection of image space onto the object space by use of the integration of data
derived from different sensors. Data integration has shown its potential in the optimization of
photogrammetric tasks, including the indirect orientation of images. Here, the image points are
extracted from scanned analogue images, captured using a conventional metric camera and
covering the same region as a LiDAR data-set, and based on the intersection between the
projection ray from the perspective centre through an image point and its object space
correspondence and a digital surface model (DSM derived from LiDAR data). The control points
are determined and used in the adjustment for the indirect orientation of digital images taken by
a small-format digital camera. Implementation and testing of the adopted method have employed
data from different sensors. The quality of the control points determined by the suggested method
was determined statistically using the discrepancies between 3D coordinates of pre-signalised
points determined by means of a global positioning system (GPS) survey. The generated control
points were used in the least squares adjustment for indirect orientation of images acquired from
a small-format digital camera. The results obtained showed that the proposed method is
promising and has potential for the indirect orientation of digital images.
1. INTRODUCTION
The indirect orientation of images is demanded in different mapping tasks such as: map revision
and production; orthophoto and digital terrain model (DTM) generation; 3D extraction and object
reconstruction; geographic information system (GIS) updating; change detection; etc. The aim of
indirect orientation is to establish a relationship between the image and ground reference systems.
To realise the indirect orientation of images, the identification of image points that represent
precise image and object space coordinate correspondences is required. Any other sensor model
can apply this relationship such as direct linear transformation, affine transformation, projective
or rational function transformation and more interesting ideas have been suggested by Okamoto
et al. (1992), El-Manadili and Novak (1996), Fritsch et al. (1998), Hattori et al. (2000), Zhang
and Zhang (2002), Grodecki and Dial (2003), Ono and Hattori (2003), Fraser and Hanley (2004),
Fraser and Yamakawa (2004), Habib and Alruzouq (2004), Fraser et al. (2006), etc. The point-
60
based rigorous sensor model has also been used for indirect orientation of images. The most
important methods were proposed by Ebner and Strunz (1988), Gugan (1987), Kratky (1989),
Zeng and Wang (1992), Ackermann and Tsingas (1994), Toth and Krupnik (1994), Orun and
Natarajan (1994), Gulch (1995), and Schenk (2004).
Usually, the ground reference system, required to establish a relationship between images and
object space, depends on sensor geometry and available data. There are two methods of collecting
control points, that is, by conventional geodetic surveying methods and remote methods.
According James et al. (2007), control point data collection using a global positioning system
(GPS) and topographic instruments is slow by comparison with photogrammetry and remote
sensing, because traditional ground surveying is direct, time-consuming and has expensive
operational costs. Fast, autonomous and reduced cost alternatives for control point data collection
are photogrammetry and light detection and ranging (LiDAR) systems.
Two methods exist to measure photogrammetric coordinates: the stereoscopic and monoscopic
(monoplotting) methods. The stereoscopic method uses two or more images for the precise
determination of coordinates. For a rigorous solution, the monoplotting method uses a single
image and a high quality digital surface model (DSM) or DTM. Various procedures for
monoplotting have been published in the literature, with alternative solutions proposed by
Makarovic (1973), Masry and Mclaren (1979), Jauregui et al. (2002), Ressl et al. (2006), etc.
LiDAR comprises a GPS receiver, an inertial navigation system (INS) and a light amplification
by stimulated emission of radiation – a LASER. The LiDAR has the capability to rapidly provide
3D information about points located on a surface. The system emits pulses that are reflected from
objects on the surface and returned to be received by the system. The distance between the sensor
and the target is computed based on the time delay between pulse emission and its return (Wehr
and Lohr, 1999). The data used in this paper were provided by a airbone system, specifically an
Optech ALTM 2050 laser scanner. Aircraft positions were determined by differential GPS
(DGPS) and the attitude of the sensor was provided by an INS. Points in object space were
scanned nearly perpendicular to the flight path. This system automatically provides 3D
information, with high accuracy and speed. Another advantage of the system is a decrease in the
geodetic surveying required.
The information registered by DGPS and INS is used for the generation of a DSM or DTM and
the spectral information acquired by LiDAR is employed to produce an intensity image. The
intensity value is a measure of the return signal strength. It measures the peak amplitude of return
pulses as they are reflected back from the target to the detector of the LiDAR. Intensity values are
relative rather than absolute and vary with altitude, atmospheric conditions, directional
reflectance properties and the reflectivity of the target. The complexity of the system influences
the quality of position determination. According Maas (2003), the major error sources influencing
the accuracy of point data are the LiDAR instrument itself, the position and orientation
determination system, the alignment between these two subsystems and possibly human errors in
data handling and transformation.
According Wehr and Lohr (1999), a LiDAR data set can be characterised as a cloud of irregularly
distributed 3D points. The sampling density depends on the flying speed, the pulse rate, the scan
angle and the flying height. However, pointing the LASER beam to specific objects or features is
not possible using a LiDAR system. Ackermann (1999) dealt with this problem, explaining that
the resulting coordinates are derived from the footprints of the laser scan, whose points are
distributed in a random fashion. Due to the aleatory effect of the scanning system and the
61
frequency used, it is not possible to register the information about every ground point influencing
the horizontal errors because of the point spacing. Many users prefer to work on DTM or DSM in
a regular grid and apply an interpolation filtering procedure.
According Habib et al. (2007), the integration of data derived from different acquisition systems
had been showing potential, principally because LiDAR has the capability to rapidly provide 3D
information about points located on a surface with high accuracy and provides rich geometrical
surface information, which could complement information from photogrammetric data. Several
scientific papers have been presented as an alternative solution for extracting control points
derived from photogrammetry and LiDAR data integration, the most important being by Habib
and Schenk (1999), Schenk et al. (2001), Schenk and Csatho (2002), Habib et al. (2004a, b, c),
Furkuo and King (2004), Chen et al. (2004), Barbarella et al. (2004), Habib et al. (2005a, b),
Habib et al. (2006), Habib et al. (2007), Abdelhafiz et al. (2007), Mitishita et al. (2008), and
Delara et al. (2008).
Consequently, this study aims at extracting control points from photogrammetry and LiDAR data
integration for the indirect orientation of images. Implementation and testing of the adopted
method involves data from different sensors. The quality of control points extracted from data
integration images was statistically available through comparison with 3D coordinates
determined by means of a GPS survey. The control points extracted from the suggested
monoplotting model were applied for indirect orientation of images over test sites in Curitiba,
Brazil and the results showed that the proposed method is promising and has potential for the
indirect orientation of images.
This paper is organised as follows: the method is presented in the next section, followed by the
experimental results, a discussion, some conclusions and recommendations for future work.
2. METHOD
2.1
Overview
The proposed method is based on the extraction of control points from LiDAR and
photogrammetry data integration for the indirect orientation of images. The process sequence of
method is as follows: (1) Taking into account one scanned analogue image (with 1:16000 image
scale) taken using a conventional metric camera with known interior orientation parameters (IOP)
and exterior orientation parameters (EOP), the process starts by collecting 11 pre-signalised
image points and 19 other image points in the scanned analogue image for extracting control
points from data integration. Here, the 19 image points not signalised belong to the same region
covered by the digital image taken by a small-format digital camera (with 1:1800 image scale, see
Fig. 1a). For each image point manually collected, a corresponding LiDAR data-set region is
projected from object space onto image space using the well-known collinearity equations and
the IOP and EOP of the scanned analogue image. As a result, several image triangles are
generated using the cloud of irregularly 3D distributed points from the LiDAR data-set projected
from object space onto image space and the implemented algorithm automatically verifies the
intersection between collected image points and image triangles; (2) When a collected image
point positively intersects an image triangle, the control point is determined using the suggested
monoplotting model. The algorithm repeats stages (1) and (2) for all the image points collected in
the scanned analogue image; (3) Finally, 19 other image points are collected in the digital image
taken by the small-format digital camera and their control points’ correspondences extracted from
62
the suggested model are used for indirect orientation of the digital image using the collinearity
equations. It is interesting to note here that after the EOP determination of the digital image, it
could also be used for a change detection photogrammetric task. The results obtained are
statistically verified and present the efficiency and potential of the proposed method for indirect
orientation of images.
In the following, the methodology and mathematical models used in the method are described in
detail.
2.2
Process to verify the intersection between irregularly distributed 3D points
from a LiDAR data-set projected to image space and collected image points
Consider one scanned analogue image (with 1:16000 image scale), taken with a conventional
metric camera with known IOP and EOP. Eleven pre-signalised image points and 19 other image
points that cover the same region as the digital image taken by a small-format digital camera were
collected. Figure. 1a shows the digital image covering one part of the scanned analogue image
and Figure. 1b shows an example of the pre-signalised image points.
(a)
(b)
Figure 1. (a) Digital image recovering one part of the scanned analogue image. (b) Example of
pre-signalised image points collected from the scanned analogue image.
Here, the collection of image points from the scanned analogue image is manually realised by an
operator and the systematic errors are eliminated by applying the monoplotting model suggested
in this paper. For each image point manually collected, a corresponding LiDAR data-set region
(cut-off DSM search window, Fig. 2) of object space is projected onto image space using the
collinearity equations (Mikhail et al., 2001) and known IO and EO parameters.
63
Figure 2. Process to verify if the image point is considered to be inside the image triangle.
In Figure 2, the red points are the cloud of irregularly distributed 3D points from the LiDAR dataset; the black rectangle is the cut-off DSM search window that will be projected using the
collinearity equations. Here, all the points inside the black rectangle will be projected from object
space onto image space, with points OPQ being the likely 3D coordinates that will be used to
verify if the collected image point ( g i ) is considered to be inside the image triangle (blue image
triangle opq). The image point is regarded as inside the image triangle if the intersection of the
collected image point and the image triangle occurs. This procedure is performed for all collected
image points in the scanned analogue image; the intersection process is automatically verified.
When an image point is regarded as inside the image triangle, the suggested monoplotting model
must be applied for the extraction of control points.
2.3
Extraction of control points using the monoplotting model
The proposed mathematical model allows the projection of image space points onto the object
space using photogrammetry and LiDAR data integration. As described above, the proposed
model is based on the intersection between the projection ray from the perspective centre, an
image point and its object space correspondence (collinearity condition) and a DSM or DTM
derived from the LiDAR data-set. Figure 3 shows the geometry of the proposed model.
r
In the Figure 3, the vector ni = PCg i' is formed by perspective centre (PC, with
coordinates X C , Y C , Z C ) and an image point ( g i ), as follows:
r
ni = ( xi '− x0 , yi '− y0 ,− f )T
(1)
Where f is calibrated focal length, xi ' , yi ' are the image coordinates. We can normalize the vector
considering the following expression:
r ( x '− x ) r ( y '− y ) r (− f ) r
(2)
ni = i 0 i + i 0 j +
k
di
di
di
64
Figure 3. Geometry of proposed model.
Hence x0 , y0 are the principal point coordinates (pp) and
di = ( xi '− x0 ) 2 + ( yi '− y0 ) 2 + (− f ) 2
.
r
The normal vector N i to the object space is obtained by multiplying the vector nri by the inverse
of rotation matrix M T , defined by the sequence M = M κ M ϕ M ω the well-known 3D rotation Matrix, as
follows:
⎡ N xi ⎤
r
r
⎢ ⎥
N i = M T ni = ⎢ N yi ⎥
⎢ N zi ⎥
⎣ ⎦
(3)
The basic mathematical model is the parametric representation of the 3D feature, defined as
follows:
r
(4)
Gi = Niti + PC
Hence, it is important to note that ti is a real variable denoted as the line parameter.
In the Figure 1, assuming no collinearity between the vectors PO , QP and G i O , we satisfied
the equation of a plane, which can be obtained through the cross product of these three vectors, as
follows:
(5)
Gi O • ( PO ∧ QP) = 0
Developing equation (6) and carrying out some mathematical manipulations we have the general
equation of a plane:
(6)
AX + BY + CZ + D = 0
Since the parametric representation of the 3D feature passes through the perspective centre
and Gi = [ X iYi Z i ]T , we can intersect it with the object triangle (DSM points OPQ). Substituting
equation (4) in (6) we have the following mathematical expression:
(7)
A( N xi ti + X C ) + B( N yi ti + Y C ) + C ( N zi ti + Z C ) + D = 0
65
Now, using equation (7) for extracted the ti parameter, we consider:
ti = −
(8)
AX C + BY C + CZ C + D
AN xi + BN yi + CN zi
Finally, using the ti parameter in equation (4) the suggested monoplotting mode can be written
as:
AX + BY + CZ + D
(9)
X =−
N +X
C
i
Yi = −
C
C
AN + BN + CN zi
i
x
i
y
i
x
C
AX C + BY C + CZ C + D i
Ny +Y C
AN xi + BN yi + CN zi
⎛ AX C + BY C + CZ C + D i
⎞
Zi = ⎜ −
Nz + Z C ⎟ − ZN
⎜
⎟
AN xi + BN yi + CN zi
⎝
⎠
Hence, X iYi Z i are the 3D coordinates (control points) extracted from the monoplotting model and
Z N is the geoidal undulation determined using a geoidal map of region.
Figure 4. Cut off image point search window processed by step 1.
As described above, the control points are extracted using equation (9) for each image point
manually collected by the operator. In this case, the proposed model is applied for all collected
image points (Fig. 4) and control points are determined.
2.4
Sensor model for indirect orientation of images
The ground reference system (GRS) assumed here is the Conventional Terrestrial Reference
System (CTRS). The CTRS has its origin at the centre of gravity of the Earth. The axis Z points
towards the North Pole, axis X is directed towards the Greenwich meridian, the axis Y is in the
equatorial plane, and the three axes constitute a right-handed system (Fig. 5.a). Now the XiYiZi
(Eq. 9 and Fig. 4.b) are determined based on the intersection between the projection ray from the
perspective centre, an image point and its object space correspondence (collinearity condition)
and the cloud of irregularly 3D distributed points from LiDAR data-set (determined by GPS/INS
data integration). The extracted control points are transformed to the South American datum 1969
(SAD69) knowing the characteristics of the reference ellipsoid geodetic reference system 1980
(GRS80) and are then shifted to the centre of the area covered by the images ( X sYs [ Z s − Z N ]) .
The image coordinate system defined in a digital image is two-dimensional. It has its origin at the
left upper corner of the image, the C-axis is in the direction of increasing rows and the L-axis is
orthogonal to it (see Fig. 5b). The pixel coordinates (C , L) can be converted to metric image
coordinates ( x' , y ' ) by the following expression:
66
NC
)Tp
2
N
y ' = −( L − L )Tp
2
x' = (C −
(11)
Where Tp is the pixel resolution, N C , N L total number of rows (C) and columns (L), respectively.
(a)
(b)
Figure 5. (a) Definition of the collinearity sensor geometry. (b) Definition of the image
coordinate system.
The small-format digital camera is an imaging sensor frame (see Fig. 5a). Its detector subsystem
employs the charge coupled device (CCD) technique, where each frame has its own perspective
central relationship with the ground. Generally, the transformation between this system and the
ground is described by the collinearity equation. For a known control point in object space, the
standard collinearity model can be deduced such that the projection ray from the perspective
centre through an image point intersects the control point. Figure 5a illustrates this scenario.
In order to apply the collinearity equations to the image coordinates of a point ( x' , y ' ) , they must
first be transformed to the photogrammetric coordinate system by reducing the values of
systematic effects such as the principal point coordinates ( xo , yo ) and length distortions ( Δ r , Δ d )
before the adjustment. So, the collinearity equations can be written as:
r00 ( X i − X C ) + r01 (Yi − Y C ) + r02 ( Z i − Z C )
r20 ( X i − X C ) + r21 (Yi − Y C ) + r22 (Z i − Z C )
r ( X − X C ) + r11 (Yi − Y C ) + r12 (Z i − Z C )
y '− yo − Δ ry − Δ dy = − f 10 i
r20 ( X i − X C ) + r21 (Yi − Y C ) + r22 (Z i − Z C )
x'− xo − Δ rx − Δ dx = − f
(12)
Where X i , Yi , Z i are control points extracted from the suggested monoplotting model, and
are the elements of rotation matrix M.
r00 ,..., r22
The mathematic model presented in equation (12) is non-linear with respect to the parameters.
The set of error equations provided by linearization can be written in vector form as V = AX + L ,
where V is the residual error vector, A is the design matrix, X is the correction vector and
T
Lb contains the values of the observations. In the least squares adjustment, residual V PV must be
minimised and the product of minimisation is the vector form X = −( AT PA) −1 ( AT PL ) , where P is
a matrix containing the inverse of the variances of the observations.
67
3. EXPERIMENTS AND RESULTS
Two typical photogrammetric experiments were conducted to verify the viability of the method.
The experiments addressed: 1) discrepancy analysis between control points extracted from the
suggested monoplotting model (equation 9) and 3D coordinates determined by means of a GPS
survey; and 2) indirect orientation of a digital image using the control points extracted from the
proposed method and the collinearity equations presented above. Here we used two data-sets to
carry out the proposed experiments; the first data-set is based on control points extracted from the
suggested monoplotting model, while the second data-set is based on control points determined
by means of GPS survey. The steps involved in the methodology were implemented in C++ using
the Borland Builder 5.0 for Windows.
3.1
Data sets and models tested
The data-sets used for experiments were: one scanned analogue image (with 1:16000 image scale
acquired in 1999) taken with a conventional metric camera with known IO and EO parameters;
one digital image (with 1:8000 image scale acquired in 2003) taken with a small-format digital
camera DSC F717, which needed to be orientated. Here, it is important to remember that the
scanned analogue image covered part of Curitiba - more specifically the Campus of the Federal
University of Paraná/Brazil - and the digital image covered one part of the scanned analogue
image; a data-set of 3D coordinates of the 30 control points determined by means of a GPS
survey, including 11 pre-signalised and 19 other control points not pre-signalised; a LiDAR data
set captured with ∼0.7 m point spacing, whose LiDAR data were only available from the
LACTEC already interpolated to a 0.8 m grid using inverse-distance-weighting; 11 pre-signalised
and 19 control points not pre-signalised determined from the suggested monoplotting method.
Hence, the 19 control points not pre-signalised determined using the GPS survey and the
monoplotting model were to be used for indirect orientation of the digital image and the other 11
pre-signalised points were to be used for analysing the control point accuracy determined using
the suggested monoplotting model. Table 1 shows the specifications for the photogrammetric
data-set and Table 2 shows the specifications for the LiDAR data-set used in the experiments.
Table 1. Specifications for photogrammetric data-set.
Camera model
WILD RC-10
Calibrated focal length
Scale
Number of images
Avg. flight height (m)
Pixel size (mm)
Expected image measurement accuracy (mm)
Camera model
153.167
1:16000
1
∼1375
0.024
±0.024
SONY DSC F717
Calibrated focal length
Scale
Number of images
Avg. flight height (m)
Pixel size (mm)
Expected image measurement accuracy (mm)
68
10.078
1:8000
1
∼730
0.004
±0.004
According to Wehr and Lohr (1999), the LiDAR system provides horizontal accuracy of better
than 0.5 m and vertical accuracy of better than 0.15 m. According to the US national map
accuracy standards (NMAS), horizontal control point errors should not exceed 33% of target
DSM or DTM horizontal error and vertical control point errors should not exceed 40% of the
target DSM or DTM vertical error (Wolf and Dewitt, 2000). Thus, with a horizontal error of ±0.5
m and a vertical error of up to ±0.15 m, the expected horizontal and vertical control point
accuracies (assuming 1 pixel measurement error) extracted from the suggested monoplotting
model using the scanned analogue image are ±1.51 m and ±0.38 m, respectively. This does not
take into account any error introduced through point projection from object space onto image
space and point measurement in the scanned analogue image (Step 1).
Table 2. Specifications for LiDAR data-set.
Model system
Optech ALTM 2050
Avg. flying height (m)
Mean point density (points/m2)
Expected horizontal accuracy (m)
Expected vertical accuracy (m)
975
2.24
0.5
0.15
In this first experiment, we collected 11 pre-signalised image points in the scanned analogue
image and applied the suggested monoplotting model. The analysis of control point accuracy was
realised using the calculated discrepancies between control points determined from the suggested
model and the 3D coordinates determined by means of the GPS survey.
3.2
Analysis of control points accuracy
The main goal of the first experiment was to determine the control point accuracy. Thus, we
computed the discrepancies between the 3D coordinates of points determined by means of a GPS
survey and the control points determined by the proposed monoplotting model.
Firstly, monoscopic image measurements of 11 pre-signalised points were taken manually in the
scanned analogue image (e.g. see Fig. 1) and the remaining points were used in the monoplotting
model to determine the 3D coordinates in the object space (control points). It is interesting to note
that Step 1 described above was applied to calculate the 3D coordinates for each collected image
point. We also used 11 pre-signalised 3D coordinates determined by means of GPS survey to
compare with the control points determined from the suggested model.
The discrepancies between the 3D coordinates of pre-signalised points determined by means of a
GPS survey and the suggested method are presented in Table 3.
Table 3. Results of root mean square error (RMS error) discrepancy.
Discrepancies between control points determined from the monoplotting model
and 3D coordinates determined from GPS survey
Number of pre-signalised control points
Mean discrepancy (m)
RMS error discrepancy (m)
69
11
μX =-0.99, μY=-0.099, μZ=-0.01
σX =0.63, σY=0.43, σZ=0.19
3.3
Indirect orientation of a digital image taken by a small-format digital camera
The second experiment concerned the indirect orientation of a digital image taken by a smallformat digital camera using the collinearity equation and other control points determined using
the suggested monoplotting model to verify the applicability of the method. Hence, another 19
not pre-signalised image points were collected in the scanned analogue image and the control
points were determined from the suggested monoplotting model and used in the adjustment for
indirect orientation of the digital image. We also used these 19 control points determined by
means of GPS survey in the adjustment for indirect orientation of the digital image described
above. The results obtained in both methods were used to compute the discrepancies between
them and are listed in Table 4.
Table 4. Estimated EOP using the control points determined from monoplotting model and
determined by means GPS survey.
Estimated EOP by using the control points determined using the suggested model
Rotations (rad)
Standard deviation of rotations (rad)
Translations (m)
Standard deviation of Translations (m)
kappa=1.95, phi=-0.0005, omega=-0.016
σkappa=0.00031, σphi=0.0015, σomega=0.0013
XC=677399.57, YC=7183614.10, ZC =1652.15
σXC=0.54, σYC=1.65, σZC=0.23
Estimated EOP by using the control points determined by GPS survey
Rotations (rad)
Standard deviation of rotations (rad)
Translations (m)
Standard deviation of Translations (m)
kappa=1.951, phi=-0.005, omega=-0.014
σkappa=0.0004, σphi=0.0024, σomega=0.0023
XC=677398.54, YC=7183613.74, ZC =1651.84
σXC=1.93, σYC=1.84, σZC=0.55
Discrepancies between the methods
Rotations (rad)
Translations (m)
Δkappa=0.001, Δphi=0.0055, Δomega=0.002
ΔX=1.03, ΔY=0.36, ΔZ =0.31
As described above, a space resection of the digital image (with 1:8000 image scale) was
executed with control points determined from the monoplotting model and control points
determined by means of GPS survey and the discrepancies were computed. The initial
approximations of the EOP were: kappa=1.9, phi=omega=0.0 radians and XC=677600.00,
YC=7183800.0, ZC =1850.00 metres.
3.4
Discussions
The values presented in Table 3 reveal that the RMS errors in horizontal and vertical ground
coordinates are better than the expected precision value; that is, they are less than 1.51 m and
0.38 m. Hence, using the LiDAR and photogrammetry data integration (proposed method), the
control point horizontal and vertical accuracy can be improved. It is important to remember that
the image scale has an influence on the results here; that is, the larger the image scale, the higher
precision in the determination of control points. Figure 6 shows the systematic errors for the
horizontal and vertical components.
70
Figure 6. Horizontal and vertical discrepancies.
Figure 6 shows that all the X and Y discrepancies resulting from the analysis performed with the
proposed method were less than 0.64 m and the Z discrepancies were less than 0.2 m. These
values represent the differences between the horizontal and vertical ground coordinates of the
control points determined using the suggested method and the GPS survey. However, the
distribution of the X discrepancy reveals a systematic trend that was not modelled. Slightly
inaccurate values of the IO and EO parameters are probably the source of this systematic error;
hence no conclusions can be drawn. Fig. 6 shows that one control point had its horizontal
discrepancy greater than 1.65 m, probably due to the pre-signalised image point being close to the
building. In this case, one or two points that delineate the image triangle could have been the
edge points of the building.
These results demonstrate the accuracy of the control points determined using the suggested
monoplotting model and its potential to extract control points.
With respect to indirect orientation of images the discrepancy values show that both methods are
close, with the rotation parameter phi found in the method presenting the largest discrepancy
(0.0055 radians ~ 31’51”) and, in the same way, the discrepancy value of the translation
parameter XC is equal to 1.03 m. This problem was expected due to the un-modelled systematic
error in X LiDAR coordinates (see Fig. 6). Thus, both methods are compatible; that is, the
proposed method can be applied for indirect orientation of images.
The standard deviations of parameters determined from the proposed method reveal values less
than determined from the conventional method. Therefore, we do not regard the result as
expected because the 3D coordinate accuracy determined by means of the GPS survey is better
than the control point accuracy determined from the proposed method. Thus, we need more
investigations into this outcome. The estimated orientation parameters of kappa and ZC present
the lower standard deviations. The estimated rotation parameter phi presents a most sensitive
convergence, whose stability was found after the fourth iteration, while the other parameters
present convergence after the third iteration. With respect to the translation parameters, all
estimated parameters show stability after the third iteration.
The proposed method presented in this work to extract control points derived from data
integration has worked very well in all experiments because good results for EOP and control
point accuracy were obtained.
An important advantage of proposed method is that the conventional limits on the number of
control points that can be collected no longer be relevant. Here, a large number of control points
can be determined and there is no need for pre-signalised control points for indirect orientation of
71
images such as through conventional method, including image points derived from building roofs
(see Fig. 4a). Other advantage ones is that the traditional limits on the Z interpolation with respect
to the proposed monoplotting model by Makarovic (1973) there is no need, because the suggested
monoplotting model here computed the 3D coordinates directly, that is, there is no need Z
interpolations. Therefore, there is need to verification if the collected image point is considered to
be inside the projected image triangle. Other one is, how described above, the implemented
algorithm remain file the cut off image points search window (Fig. 4a) and it become possible the
automation of indirect orientation of images using area-based matching.
The disadvantage of the method is that it requires one set of LiDAR data and the analogue image
scale has an influence on the results of control point horizontal and vertical accuracy to digital
image already be accurately oriented. This might not be the case in all practical situations.
4. CONCLUSIONS AND RECOMMENDATIONS FOR FUTURE WORK
This paper presented a method for extracting control points from LiDAR and photogrammetry
data integration for the indirect orientation of images. The most important contribution of the
suggested method is the means of determining control points using a monoplotting model based
on the intersection between the projection ray from the perspective centre through an image point
and its object space correspondence and a DSM derived from LiDAR data.
With a purpose of evaluating method’s potential in extracting control points and indirect
orientation of images, two experiments were conducted: the discrepancies analysis of control
points extracted from suggested monoplotting model; and analysis of results obtained from
indirect orientation of digital image by using the well-known collinearity equation.
In all cases, the results obtained can be considered satisfactory and in accordance with the ones
theoretically expected. The discrepancy analyses produced horizontal coordinate RMS error
values of less than 0.64 m and vertical coordinate RMS error values of less than 0.2 m. The
analysis of results obtained from indirect orientation of a digital image using the collinearity
equation revealed that both methods are compatible and the proposed method can be applied for
indirect orientation of images. The standard deviations of parameters determined from the
method revealed values less than determined from the traditional method and we need to better
investigate the cause of this effect.
The manual establishment of feature point correspondences between image and object space
helped the success of the indirect orientation of images. The control points extracted using the
proposed method also showed its potential for indirect orientation of images, producing results
close to those obtained from the traditional method.
Using the integration of data derived from LiDAR and photogrammetry, we can obtain accurate
3D coordinates, whose integration is necessary for a complete description of 3D features.
However, the greatest factors influencing accuracy are the digital image scale and DSM or DTM
resolution. The automatic area-based correspondence problem will be the focus of our future
work, as well as the automatic indirect orientation of images and the automatic extraction of
control points.
72
5. ACKNOWLEDGEMENTS
The authors would like to thank the LACTEC for providing the LiDAR data-set used in this work
and to CNPq (Conselho Nacional de Desenvolvimento Científico e Tecnológico) for general
support of this project no. 570316/2008-1.
6. REFERENCES
Abdelhafiz, A., Riedel, B., and Niemeier, W., 2007. “3D image” as a result from the combination
between laser scanner point cloud and digital photogrammetry. In: Optical 3D Measurement
Techniques VII, Vol. I, pp. 204-213.
Ackermann, F. and Tsingas, V., 1994. Automatic digital aerial triangulation. Proceedings of
ASPRS0ACSM Annual Convention and Exposition, pp. 1-12.
Ackermann, F., 1999. Airborne laser scanning-present status and future expectations. ISPRS
Journal of Photogrammetry and Remote Sensing, 54(2/3), pp. 64-67.
Barbarella, M., Lenzi, V., and Zanni, M., 2004. Integration of airborne laser data and high
resolution satellite images over landslides risk areas. International Archives of the
Photogrammetry, Remote Sensing and Spatial Information Sciences, 35(B4), pp. 945-950.
Chen, L. T., Teo, Y., Shao, Y., Lai, J., and Rau, J., 2004. Fusion of LiDAR data optical imagery
for building modeling. International Archives of XXth ISPRS Congress, Istanbul, Turkey,
35(B4), pp. 732-737.
dal Poz, A. P., 1997. Uma solução para o problema fotogramétrico inverso. Cartografia e
Cadastro (Lisboa), Lisboa, Portugal, 7, pp. 43-47.
Delara, R. S. Jr., Mitishita, E. A., Vogtle, T., and Bahr, H. P., 2008. Automatic digital image
resection controlled by LiDAR data. 3D Geo-Information Sciences, 11(2), pp. 213-234.
Ebner, H. and Strunz, G., 1988. Combined point determination using digital terrain models as
control information. International Archives of Photogrammetry and Remote Sensing, 27(B11/3),
pp. 578-587.
El-manadili, Y. S. and Novak, K., 1996. Precision rectification of SPOT imagery using the direct
linear transformation model. Photogrammetric Engineering & Remote Sensing, vol. 62, no. 1, pp.
67-72.
Fraser, C. S. and Yamakawa, T., 2004. Insights into the affine model for satellite sensor
orientation. ISPRS Journal of Photogrammetry and Remote Sensing, 58(5/6), pp. 275-288.
Fraser, C. S., Dial, G., and Grodecki, J., 2006. Sensor orientation via RPCs. ISPRS Journal of
Photogrammetry and Remote Sensing, 60(3), pp. 182-194.
Fraser, C. S. and Hanley, H. B., 2004. Bias compensated RPCs for sensor orientation of highresolution satellite imagery. Proceedings of ASPRS Annual Conference, 9 pages (on CD-ROM).
73
Fritsch, D., Englich, M., and Sester, M., 1998. Modelling of the IRS-1C satellite pan stereoimagery using the DLT approach. ISPRS Commission IV Symposium on GIS – Between Vision
and Applications, 32(Part 4), pp. 511-514.
Forhuo, E. and King, B., 2004. Automatica fusion of photogrammetric imagery and laser scanner
point clouds. International Archives of Photogrammetry and Remote Sensing, 35, pp. 921-926.
Grodecki, J. and Dial, G., 2003. Block adjustment of high-resolution satellite images described
by rational functions. Photogrammetric Engineering and Remote Sensing, 69(1), pp. 59-68.
Gugan, D. J., 1987. Practical aspects of topographic mapping from SOPT imagarey.
Photogrammetric Record, 12(69), pp. 349-355.
Gulch, E., 1995. From control points to control structures for absolute orientation and aerial
triangulation in digital photogrammetry. ZPF, 3/1995, pp. 130-136.
Habib, A. F. and Schenk, T., 1999. A new approach for matching surfaces from laser scanners
and optical sensors. International Archives of Photogrammetry and Remote Sensing, 32(3W14),
pp. 55-61.
Habib, A. F. and Alruzouq, R. I., 2004. Line-based modified iterated Hough transformation for
automatic registration of multi-source imagery. Photogrammetric Record, 19(105), pp. 5-21.
Habib, A. F., Ghama, M. S., Morgan, M. F., and Mitishita, E. A., 2004a. Integration of laser and
photogrammetric data for calibration purposes. International Archives of XXth ISPRS Congress,
Istanbul, Turkey, Commission I, 6p. (CD-ROM).
Habib, A. F., Ghama, M. S., Kin, C. J., and Mitishita, E. A., 2004b. Alternative approaches for
utilizing LiDAR data as a source of control information for photogrammetric models.
International Archives of XXth ISPRS Congress, Istanbul, Turkey, Commission I, 6p. (CDROM).
Habib, A. F., Ghama, M. S., and Tait, M., 2004c. Integration of LiDAR and Photogrammetry for
close range applications. International Archives of XXth ISPRS Congress, Istanbul, Turkey,
Commission I, 6p. (CD-ROM).
Habib, A. F., Ghanma, M. S., and Mitishita, E. A., 2005a. Photogrammetric georeferencing using
LiDAR linear and areal features. Korean Journal of Geomatics, 5(1), pp. 1-13.
Habib, A. F., Ghanma, M. S., Morgan, M. F., and Al-Ruzouq, R., 2005b. Photogrammetric
registration using linear features. Photogrammetric Engineering and Remote Sensing, 71(6), pp.
699-707.
Habib, A. F., Cheng, R. W., Kim, E., Mitishita, E. A., Frayne, R., and Ronsky, J., 2006.
Automatic surface matching for the registration of LiDAR data and MR imagery. ETRI Journal,
28(2), pp. 162-174.
Habib, A. F., Bang, K. I., Aldelgawy, M., Shin, S. W., and Kim, K. O., 2007. Integration of
photogrammetric and LiDAR in a multi-primitive triangulation procedure. ASPRS Annual
Conference, pp. 7-11.
74
Hattori, S., Ono, T. Fraser, C. S., and Hasegawa, H., 2000. Orientation of high-resolution satellite
images based on affine projection. International Archives of Photogrammetry and Remote
Sensing, 33(3), pp. 359-366.
James, T. D., Carbonneau, P. E., and Lane, S. N., 2007. Investigating the effects of DEM error in
scaling analysis. Photogrammetry Engineering and Remote Sensing, 73(1), pp. 67-78.
Jauregui, M., Vílchez, J., and Chacón, L., 2002. A procedure for map updating using digital
monoplotting. Computer & Geosciences, 28(4), pp. 513-523.
Kratky, V., 1989. Rigorous photogrammetric processing of SPOT images at CCM Canada.
ISPRS Journal of Photogrammetry and Remote Sensing, 44(1), pp. 53-371.
Maas, H. G., 2003. Planimetric and height accuracy of airbone laserscanner data – user
requerements and system performance. Proceedings of 49o. Photogrammetry Week 2003 (Ed. D.
Fritsch), Wichmann Verlag, pp. 117-125.
Makarovic, B., 1973. Digital mono-plotters. ITC Journal, 4, pp. 583-599.
Masry, S. E. and Mclaren, R. A., 1979. Digital map revision. Photogrammetry Engineering and
Remote Sensing, 45(2), pp. 193-200.
Mikhail, E. M., Bethel, J. S., and Mcglone, J. C., 2001. Introduction to modern Photogrammetry.
Inc. New York: John Wiley & Sons, 479 p.
Mitishita, E. A., Habib, A. F., Centeno, J. A. S., and Machado, A. M. L., 2008. Photogrammetric
and LiDAR data integration using the centroid of a rectangular building roof as a control point.
Photogrammetric Record, 23(6), pp. 19-35.
Okamoto, A., Akamatsu, S., and Hasegawa, H., 1992. Orientation theory for satellite CCD linescanner imageries of mountainous terrain. International Archives of Photogrammetry and Remote
Sensing, 29(2), pp. 205-209.
Newby, P. R., 2007. Technical terminology for the photogrammetric community.
Photogrammetric Record, 22(118), pp. 164-179.
Ono, T. and Hattori, S., 2003. Fundamental principle of image orientation using orthogonal
projection model. International Archives of Photogrammetry and Remote Sensing, 34(3), 6 pages
(on CD-ROM).
Orun, A. B. and Natarajan, K., 1994. A modified bundle adjustment software for SPOT Imagery
and Photography: Tradeoff. Photogrammetry Engineering and Remote Sensing, 60(12), pp. 14311437.
Ressl, C., Haring, A. Briese, C., and Rottensteiner, F., 2006. A Concept for adaptive
monoplotting using images and Laserscanner. International Archives for Photogrammetry and
Remote Sensing, 36(3), pp. 98-104.
75
Schenk, T., Seo, S., and Csatho, B., 2001. Accuracy study of airborne laser scanning data with
Photogrammetry. International Archives of Photogrammetry, Remote Sensing and Spatial
Information Sciences, 34(3/4), pp. 113-118.
Schenk, T. and Csatho, B., 2002. Fusion of LiDAR data and aerial imagery for a more complete
surface description. International Archives of Photogrammetry and Remote Sensing, 34(3/4), pp.
310-317.
Schenk, T., 2004. From point-based to feature-based aerial triangulation. Photogrammetric
Engineering and Remote Sensing, 58(2004), pp. 315-329.
Toth, C. and Krupnik, A., 1994. Concept, implementation and results of an automated aerial
triangulation system. ASPRS/ACSM Annual Convention, pp. 644-651.
Zhang, J. and Zhang, X., 2002. Strict geometric model based on affine transformation for remote
sensing image with high resolution. International Archives of Photogrammetry and Remote
Sensing, 34(3), pp. 309-312.
Zeng, Z. and Wang, X., 1992. A general solution of a closed-form space resection.
Photogrammetry Engineering and Remote Sensing, 58(3), pp. 327-338.
Wehr, A. and Lohr, U., 1999. Airborne laser scanning-an introduction and overview. ISPRS
Journal of Photogrammetry and Remote Sensing, 54(2/3), pp. 68-82.
Wolf, P. R. and Dewitt, B. A., 2000. Elements of Photogrammetry, with Applications in GIS.
Third edition. McGraw-Hill, New York, 608 p.
76