The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part Bl. Beijing 2008 
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used in this paper. In the following sections, we briefly review 
both methods. 
2.3.1 RANSAC Method: 
Dividing the images of both datasets into patches: Due to the 
large size of two datasets which causes difficulties in running 
the program large image data have been divided in smaller 
patches. 
The "RANdom SAmple Consensus" RANSAC was introduced 
by Fischler and Bolles in 1981. If this concept is used in the 
context of image registration the idea is to separate outliers and 
inliers in a set of point pairs which are introduced to a 
coordinate transformation. More generally speaking, the basic 
assumption is that the data consist of inliers, i. e., data points 
which can be explained by some set of model parameters, and 
outliers which are data points that do not fit the model. In 
addition, the data points are subject to noise. An advantage of 
RANSAC is its ability to robustly estimate the model 
parameters. It finds reasonable estimates of the parameters even 
if a high percentage of outliers are present in the data set. A 
small drawback of RANSAC is that a complete search would be 
computationally very expensive. Therefore the number of 
random samples which is selected to estimate the parameters is 
usually limited by an upper number which may lead to a 
suboptimal solution (Fischler and Bolles, 1981). 
2.3.2 Baarda’s Data Snooping Method: 
Baarda’s method is one of the most commonly used blunder 
detection methods. It separates the inliers from the outliers 
using the estimated normalised residuals. With observation 
vector L and design matrix A the least squares estimate of the 
unknowns x is found by 
x = (A T A)~ l A T L (2-7) 
The residual vector v and its covariance matrix C v 
v = Ax- L 
C,=crl{l-A{A T Ay x A T ) (2-8) 
Designing a GUI: A GUI is developed which handles loading 
datasets and specifying SIFT parameters, in particular: Number 
of octaves, number of images in each octaves (levels), standard 
deviation of Gaussian Function (a), threshold of SIFT matching, 
and some others of minor importance. The error search with 
RANSAC and data snooping is visualised to simply control the 
impact of SIFT parameter settings onto the registration result of 
Figure 1 : Flowchart of the registration program 
are used to determine normalized residuals by computing the 
ratio of a residual and the square root of corresponding diagonal 
element of the covariance matrix C v 
the four types of the LiDAR data used in the experiments (i.e. 
LIDAR range, LIDAR intensity, both with first and second 
pulse). 
V 
yj diag(Cç)ji 
(2-9) 
Normalized residuals follow the standard normal distribution 
N(0,1) if no errors are present in the data. If a normalised 
residual is above a critical value, the corresponding observation 
might be erroneous. Data snooping is the process of eliminating 
the observation, which produces the largest normalised residual. 
This process has to be repeated until no further outlier is 
detected anymore. 
Overall registration procedure: Flowchart of the overall 
registration process is shown in the following figure (Figure 1). 
The histogram matching between LiDAR intensity and aerial 
images serves for convenience of visual evaluation. SIFT 
keypoints are extracted from the original image data. The 
transformation parameters are used to rectify the target image to 
the reference image frame. 
3. EXPERIMENTAL INVESTIGATIONS 
3.1 Test data set of Stuttgart city 
The TopScan Company has acquired LiDAR data and aerial 
images in April 2006 with a sampling density of 4.8 points per 
square meter for the LiDAR data. Simultaneously aerial colour 
images with 20 cm ground resolution have been recorded using 
2.4 Procedural development 
The procedural development undertaken in this research can be 
summarized as follow: