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International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B1. Istanbul 2004
including distortion parameters, as well as the ground
coordinates of the points defining the object space line. Such a
constraint does not introduce any new parameters and can be
written for all intermediate points along the line in the imagery.
The number of constraints is equal to the number of
intermediate points measured along the image line.
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Figure 2: Perspective transformation between image and object
space straight lines.
As a special case of the above procedure, the treatment of
control linear features (with known object coordinates of its end
points) will be slightly different. The control line will provide
the end points in the object space; hence, these end points need
not be measured in any of the images and no collinearity
equations will be written for any of the control lines.
Subsequently, image space linear features are represented only
by a group of intermediate points measured in all images.
LIDAR straight-line features
The growing acceptance of LIDAR as an efficient data
acquisition system by researchers in the photogrammetric
community led to a number of studies aiming at pre-processing
LIDAR data. The purpose of such studies ranges from simple
primitive detection and extraction to more complicated tasks
such as segmentation and perceptual organization (Maas and
Vosselman, 1999: Csathó et al., 1999; Lee and Schenk, 2001;
Filin, 2002).
In this paper, LIDAR straight line features will be used as a
source of control for photogrammetric models. To extract such
lines, suspected planar patches in a LIDAR dataset are
manually identified with the help of corresponding optical
imagery, Figure 3. The selected patches are then checked using
a least squares adjustment to determine whether they are planar
or not and to remove blunders. Finally, neighbouring planar
patches with different orientation are intersected to determine
the end points along object space discontinuities between the
patches under consideration.
In another approach to simplify the extraction process, intensity
and range data recorded by the LIDAR system are utilized for
direct measurement of linear features. Raw range and intensity
data are first interpolated to a uniform grid using identical
interpolation method and parameters. Linear features previously
extracted from photogrammetry are then identified on the
intensity image from which planimetric coordinates of line ends
are measured while observing height readings from the range
image, Figure 4. It is worth mentioning that the interpolation
method and applied parameters have a visible effect on the
accuracy of this approach.
(a) (b)
Figure 3: Manually identified planar patches within the LIDAR
data (a) guided by the corresponding optical image (b).
Sa
FEPEIRE
(a) à
Figure 4: Manually measuring planimetric coordinates from
intensity image (a) and height from range image (b).
2.2 Approach 2: Using LIDAR lines in the absolute
orientation of photogrammetric model
This approach starts with generating a photogrammetric model
through a photogrammetric triangulation using an arbitrary
datum without knowledge of any. control information. The
datum is achieved through fixing 7 coordinates of three well-
distributed points in the bundle adjustment procedure. The next
step is determining the elements of the absolute orientation
parameters to align this photogrammetric model to the LIDAR
reference frame using conjugate straight line segments. Both
photogrammetric and LIDAR line segments are represented by
their end points. These end points are not required to be
conjugate. In this paper, a 3D similarity transformation is used,
Equation 2.
X. Xr X
Y deze +S Ame LL: 0
Z4 Zr Zz
Where S is the scale factor, (X4 Y4 Z4). is the translation vector
between the origins of the photogrammetric and LIDAR
coordinate systems, R is the 3D orthogonal rotation matrix, (X,
Y, Z,). are the point coordinates in one dataset, while (X4 YA
Z4). are the point coordinates in the other.
Referring to Figure 5, the two points describing the line
segment from the photogrammetric model undergo a 3-D
similarity transformation onto the line segment AB from the
LIDAR dataset. The objective here is to introduce the necessary
constraints to describe the fact that the model segment (12)
