The table above shows that: the original À value does not affect 
the registration result. 
6. CONCLUSION 
This paper utilizes a kind of registration model using linear 
feature instead of point feature, achieving high-accuracy 
registration of LiDAR points and image, and conducts a semi- 
quantitative registration scale analysis. This registration model 
is fit not only for the registration of multi-image and LiDAR 
point clouds data, but also for the registration of single image 
and LiDAR point cloud. In the experiment, the registration of 
DMC images with different resolutions and LiDAR points data 
with different density are used. In single image condition, when 
flying height is less than 2500m, the absolute accuracy of 
registration is less than 0.5m; In multi-images condition, the 
registration accuracy is better than that of single image. When 
flying height is less than 2500m, absolute accuracy is less than 
0.15m. The experimental results show that using linear feature 
is better than that of point feature. 
As for the images of different resolutions which are registrated 
with the points data of the same density, the higher resolution, 
the higher accuracy of registration result. In order to enhance 
the universality of algorithm, the registration method using 
various feature on this basis is needed to be further researched 
and breakthrough, especially when the linear features is absent. 
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