The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part Bl. Beijing 2008 
174 
B) Aerial image , Negative LiDAR Intensity, Positive LiDAR 
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C) Aerial image , Negative LiDAR Intensity, Positive LiDAR 
Figure7: Effect of the texture 
3.10 Visualisation of the Rectification Result 
After estimating the parameters of the transformation between 
the images, one image can be transformed into the coordinate 
system of the other one. The standard method of image 
rectification is used for this purpose. Figure 8 shows the 
rectified LiDAR image, which is superimposed to the aerial 
image. 
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Figure 8 :Superimposed LiDAR and aerial images 
4. CONCLUSIONS 
Registration of aerial images and LiDAR data can be carried 
out successfully by using SIFT technique if the LiDAR 
intensity images are used for this purpose. In general, a large 
number of SIFT keypoints can be extracted and matched 
between this two data sets. 
Experimental findings with our data are: 
1. The number of keypoints extracted from LiDAR intensity 
images is 7-8 times higher than the number of extracted 
keypoints from LiDAR range data. 
2. The grey value distribution (texturing) of a LiDAR intensity 
image and an aerial image might be locally very different. 
Sometimes the negative (inverse) LiDAR intensities fit much 
better to the aerial image. Those differences have to be 
expected as the Laser scanner operates in the near infrared 
while the aerial is recoding radiation of the visible 
electromagnetic spectrum. 
3. The following parameters for extracting SIFT features are 
found to be most suitable: A sigma level of 1 for the base level 
smoothing of LIDAR and aerial images and 5 octaves and 4 
sub-levels (number of images per octave) for creating the scale 
space representation. 
4. The ratio between the distances to the first and second 
nearest descriptors has to be lower than a given threshold. A 
threshold of 0.8 produced a low outlier quota but at the expense 
of a small number of matched keypoints. 
5. Regarding the outlier removal with RANSAC and Baarda’s 
data snooping no clear preference could be identified. 
Limitations that have been observed are: 
The invariance of SIFT features to small changes in 
illumination does not result in an extraction of suitable features 
from shadow areas in an aerial image. 
The polynomial transformation of second order which we used 
for image registration is only an approximation for a more 
general transformation model, e.g. a transformation based on 
rational functions. 
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