Auto-registration of aerial imagery and airborne LiDAR data based on structure feature

Xu Jingzhong; Kou Yuan; Yuan Fang; Zhang Wei

Current algorithms of registration of aerial imagery with airborne LiDAR data has the major issue of strong dependency upon the matching feature, so these methods are impressionable to the texture feature of image and the density of LiDAR point cloud. A new method of auto-registration of aerial imagery with airborne LiDAR data based on structure feature was proposed. The first step was the automated extraction of structure feature from LiDAR range image and aerial imagery. After that the LiDAR structure features were projected onto aerial imagery and corresponding features were determined using geometry constraints. The second step was the wrong matches eliminating by two points geometric constraint after calculating the DLT parameters as the initial value, and iteration strategy was adopted to obtain optimal results. The last step was the pose parameters calculated by the optimal matching results using quaternion-based solution of space resection. Experimental studies have demonstrated that this algorithm is effective in auto-registration of aerial imagery with airborne LiDAR data and little influenced by noise.

1. School of Remote Sensing and Information Engineering,Wuhan University,Wuhan 430079,China;

2. School of Geodesy and Geomatics,Wuhan University,Wuhan 430079,China

Received Date: 2013-04-10

Rev Recd Date: 2013-05-25

Publish Date: 2013-12-25

Key words:

Abstract: Current algorithms of registration of aerial imagery with airborne LiDAR data has the major issue of strong dependency upon the matching feature, so these methods are impressionable to the texture feature of image and the density of LiDAR point cloud. A new method of auto-registration of aerial imagery with airborne LiDAR data based on structure feature was proposed. The first step was the automated extraction of structure feature from LiDAR range image and aerial imagery. After that the LiDAR structure features were projected onto aerial imagery and corresponding features were determined using geometry constraints. The second step was the wrong matches eliminating by two points geometric constraint after calculating the DLT parameters as the initial value, and iteration strategy was adopted to obtain optimal results. The last step was the pose parameters calculated by the optimal matching results using quaternion-based solution of space resection. Experimental studies have demonstrated that this algorithm is effective in auto-registration of aerial imagery with airborne LiDAR data and little influenced by noise.

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Reference (23)

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Auto-registration of aerial imagery and airborne LiDAR data based on structure feature

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