The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part Bl. Beijing 2008 
173 
Aerial 
———Last intensity 
* - • First intensity 
Figure 4: Dependency of matched features on the 
number of octaves 
3.6 The Impact of the Number of Sub-levels per Octave on 
Keypoint Extraction 
On each image scale represented by a certain octave a fixed 
number of sublevels (differently blurred images) is used in the 
SIFT concept to extract the keypoints as explained in Section 
2.1. With only two sub-levels a fairly small number of 
keypoints is extracted, this increases to a maximum of features 
if 4 sub-levels are employed for LiDAR intensity images and 5 
sub-levels for the aerial image (Figure 5). The curve for the 
aerial images starts from a lower number at sub-level 2 and 
intersects the curves for the LiDAR intensities near sub-level 4. 
Ie\el=2 level=3 le\el=4 le\el=5 le\«l=6 
Number of image per octave 
Figure 5: Impact of sub-levels on keypoint extraction 
3.7 The Impact of the Matching Threshold on the Number 
of Matched Keypoints 
To study the dependency of the number of matched features 
from matching parameters again image regions of 500 by 500 
pixels are used for the experiments. Matching is controlled by 
the threshold t (cf. Section 2.2). The lower the threshold t the 
more prominent should be the extracted keypoints. With values 
of t close to 1 a higher number of error is expected in the 
matched keypoints. Figure 6 shows a high sensitivity of the 
number of matched keypoints on the threshold t. Around a 
threshold of 0.85 a strong growth in the number of matched 
keypoints is observed. An increased base level smoothing (the 
red curve) produces less matched points. 
2000 
Threshold of Matching 
Figure 6: The number of matched features as a function 
of the matching threshold 
3.8 Positive and negative LiDAR intensity images 
Samples of negative LiDAR intensity, positive LiDAR intensity 
and aerial images are depicted in Figures 7A, 7B and 1C. For 
the sample depicted in Figure 7A the aerial image is more 
similar to the positive LiDAR intensity images, whereas in 
Figures 7B and 7C a higher similarity of the aerial image to the 
negative LiDAR intensities can be observed. The reason for that 
is the Laserscanner operates with a frequency in the Near 
Infrared. Whether positive or negative LiDAR intensity images 
are used for keypoint extraction has almost no influence on the 
number of extracted keypoints. But the extracted keypoints are 
different which leads to a higher number of outliers if matched 
keypoints related to the positive LiDAR intensity images are 
used. 
3.9 RANSAC and Data Snooping used for Removing 
Outliers 
Firstly it should be emphasized that both procedures, RANSAC 
and Baarda’s data snooping, are appropriate for removing the 
outliers which are among the matched keypoints. If there is a 
sufficient number of corresponding keypoints, which match 
very well both procedures eliminate the outliers efficiently. If 
there is a high percentage of outliers and a weak geometric 
distribution of the extracted points RANSAC may stuck to a 
local maximum of the search space. But the iterative outlier 
elimination also failed by eliminating some good point pairs. A 
simulation would probably give a more detailed insight into the 
pros and cons of both methods that our experiments with real 
image data can do. 
A) Aerial image , Negative LiDAR Intensity, Positive LiDAR