International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B4, 2012 
XXII ISPRS Congress, 25 August - 01 September 2012, Melbourne, Australia 
407 
facilitate scale analysis, point cloud is appropriately vacuated. 
So the following data combinations are acquired: (0.056m, 
0.65 point / m 2 ) , (0.056m, 1.306 point / m ) , (0.11m, 
1.306 point tm ) and (0.25m, 1.306 point lm ) . 
5.2 The Registration Result of Single Image and LiDAR 
Points Data 
The registration of one-chip frame aerial image and LiDAR 
point clouds data. Registration model is shown as (2). 16 
checkpoints, absolute accuracy of registration result is shown in 
table 1, and the unit is m. 
Single 
Image Resolution (m) 
0.056m 
0.11m 
0.25m 
0.056m 
Density 
Point cloud density (points/ nr) 
1.306 
1.306 
1.306 
0.65 
Accuracy(m) 
0.2125 
0.3978 
0.4984 
0.2237 
Table 1: registration result of single image and LiDAR points 
data (experiment 1) 
5.3 The registration result of multi-images and LiDAR 
points data 
The registration model of multi- mages and LiDAR points data 
still is as shown in (2). 10 checkpoints are used to check the 
registration result of airborne LiDAR point clouds data with 
point feature and remote sensing image, as well as the 
registration result of airborne LiDAR point clouds data with 
linear feature instead of point feature and remote sensing image. 
The registration results are shown as table 2 and table 3. 
Multi-image 
scale 
analysis 
Image Resolution (m) 
0.056m 
0.11m 
0.25m 
0.056m 
Density 
Point cloud density (points/ nr) 
1.306 
1.306 
1.306 
0.65 
Accuracy(m) 
0.139581 
0.242089 
0.268104 
0.177526 
Table 2 Registration result of multi- images based on point 
feature and LiDAR point clouds data 
Multiple 
DMC 
Image Resolution(m) 
0.056 
0.11 
0.25 
0.056 
Density 
Point cloud density (point/ nr) 
1.306 
1.306 
1.306 
0.65 
Accuracy( 
m) 
0.055636 
0.091570 
0.143567 
0.132470 
Table 3 Rgistration result of multi-image based on linear 
feature instead of point feature 
It can be seen from the tables above that in the registration 
based on linear feature instead of point feature when flying 
height is no more than 2500m and point clouds density is more 
than 1 point/m“, coordinate deviation accuracy is in a pixel with 
accuracy of 0.15m, the result is shown in figure7 a-c. By 
comparing table 2 and 3, the accuracy using linear primitives is 
much better than using point registration primitive. And, the 
registration accuracy of multi-images is better than single 
images. 
(c) 
Fig.7 Registration result of multi- images and LiDAR points 
5.4 Scale analysis of image and LiDAR point clouds data 
registration 
With the registration results of LiDAR points data with different 
density, and multi-images with different flying heights, the 
profile of scale factor S, registration error and scale analysis 
parameters are shown in figure 8: 
0. 056-1. 306 j 
0. 11-1.306 I 
0.25-1.306 ; 
0. 56-0. 65 1 
Figure 8: registration results’ position deviation of check points 
What can be seen from table 4 and table 2 is that the higher 
resolution of DMC aerial image, the higher absolute accuracy of 
registration.When points’ density is fixed, the higher image’s 
resolution, the larger registration scale and the bigger 
registration deviation. When image resolution is fixed, the 
smaller density of points, the larger value of registration scale 
factor and the bigger deviation of registration point position. 
From figure 8, it can be seen that under original point clouds 
density, the deviation of checkpoint is rather steady in the range 
of [0,1.5]. Resolution of aerial image satisfies centimeter 
accuracy level. Assume image resolution is 0.05m, points 
density then should be 1.7 point / m , so it can get the 
registration accuracy in a pixel. 
5.5 The analysis of the patameter ^ 
In this paper, /L is introduced to make the line features in the 
LiDAR points data as registration primitives corresponding to 
the tie point selected in the image data. In order to verify the 
impaction of the initial X value, we compare three groups of 
the X values: “true values”, “the results with good original 
values” and “the results with 0 original values ” . 
true values 
Result with good 
original value 
Result with “0” 
original value 
0.21 
0.209663 
0.209653 
0.84 
0.83865 
0.83895 
1.18 
1.185947 
1.185944 
1.68 
1.678126 
1.678176 
2.435 
2.425356 
2.425356 
3.06 
3.062787 
3.062791 
6.96 
6.96088 
6.96087 
Table 4: comparation of results with differemt orginal values