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The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part Bl. Beijing 2008 
conjugate patch are compared to determine the degree of 
similarity between these patches. 
g) Once the patch with the best matching parameters is 
found, both patches are visualized to ensure the correctness 
of the chosen patch, and to determine the number of points 
constituting these patches; patches containing more points 
are preferred. 
Then the angle derived from patch matching can be calculated 
average, standard deviations, RMS to evaluate the quality of 
Airborne LiDAR data. 
In addition, through matching patch, also generating a lot of 
corresponding points for adjustment. 
3.5 Strips adjustment 
A three-parameter mathematical model of adjusting is put 
forward to improve the accuracy of LiDAR data. 
If only considering error in height, the error parameters are 
generalized to three parameters. As follows: 
a : elevation constant 
b : linear variable along strip 
C : linear variable across strip 
C 
Where b and C are relative to the direction of strip, so firstly 
transforming coordinates in overlapping strip to one set of 
coordinates. As follows: 
Y 
Figure 4. A local coordinate system 
In elevation, AH can be shown as following formula: 
AH(U,V) = a + bU + cV (7) 
Tie points were extracted from the overlap area between 
LiDAR strips. Since a point to point correspondence is not 
available between LiDAR strips, the points had to be 
interpolated in order to achieve a match. 
Through large amounts of redundancy data, a, b , C can be 
resolved based on a least squares adjustment procedure. 
3.6 Calculating offset in horizon with checkpoints 
Elevations of LiDAR derived points were compared with RTK- 
derived reference points. A circle with a radius of 2 m using a 
reference point as a centre point of the circle was created for 
every reference point. Statistics of the LiDAR points were 
calculated inside the circles if there were more than 5 laser 
points included. Mean value, median, minimum, maximum and 
standard deviation, nearest laser point to the reference point and 
an interpolated height value from the laser points were 
calculated. A 10 cm by 10 cm grid and a cubic method was 
used in the height interpolation calculations. The above 
mentioned statistical values were calculated to find out if there 
was a difference between mean value, nearest laser point to the 
reference point and an interpolated height value in the 
comparison process. 
Figure 5. Checkpoints in the wild (left) and point clouds (right) 
3.7 Density of point cloud based on TIN 
In addition, building digital elevation model uses ground data 
after classification. Therefore, the precision of digital terrain 
model acquisition from Airborne LiDAR data mainly by the 
following factors: 
• Accuracy of per point 
• Density of points 
When the raw point clouds have high density, we can better 
represent terrain and surface features in survey area. So, the 
density of point clouds from LiDAR is also an important quality 
indicator. 
Then the corresponding points’ coordinates in overlapping area 
of different strips are supposed to be coincidence in theory. As 
following equation: 
h[7+(u, v) = Hj™+ah u (u, v) <8) 
Where: k, j is strip numbers 
i is the number of corresponding points 
Then in fact the equation of difference in elevation is: 
The method of calculate density of point clouds is: 
• Generating a Triangulated Irregular Network (TIN) 
using point clouds. 
• Calculating area of each triangle in TIN. Then sorting 
by area and generating histogram. 
• From this histogram, we can be aware of data-density 
distribution of point clouds. 
H T - ”T = + w, + eft, - (a k + b k U kJ + c k V kJ ) (9)