  Co-Registration of aerial imagery and Light Detection and Ranging (LiDAR)
data is quilt challenging because the different imaging mechanism causes
significant geometric and radiometric distortions between such data. To tackle
the problem, this paper proposes an automatic registration method based on
structural features and three-dimension (3D) phase correlation. In the proposed
method, the LiDAR point cloud data is first transformed into the intensity map,
which is used as the reference image. Then, we employ the Fast operator to
extract uniformly distributed interest points in the aerial image by a
partition strategy and perform a local geometric correction by using the
collinearity equation to eliminate scale and rotation difference between
images. Subsequently, a robust structural feature descriptor is build based on
dense gradient features, and the 3D phase correlation is used to detect control
points (CPs) between aerial images and LiDAR data in the frequency domain,
where the image matching is accelerated by the 3D Fast Fourier Transform (FFT).
Finally, the obtained CPs are employed to correct the exterior orientation
elements, which is used to achieve co-registration of aerial images and LiDAR
data. Experiments with two datasets of aerial images and LiDAR data show that
the proposed method is much faster and more robust than state of the art
methods
