Abstract
Abstract. 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.
Highlights
At present, the airborne Light Detection and Ranging (LiDAR) and aerial photogrammetry systems are the main sources for obtaining a large amount of earth observation data
Combining the 3D information contained in LiDAR data with the rich semantic information in aerial imagery plays an important role in many applications such as building extraction (Awrangjeb et al, 2013), Change detection (Qin and Gruen, 2014), 3D reconstruction (Wu et al, 2018), etc
In order to address the issues, this paper proposes a registration method of aerial images and LiDAR data based on structural features and 3D phase correlation, which belongs to the 2D-2D registration method
Summary
The airborne Light Detection and Ranging (LiDAR) and aerial photogrammetry systems are the main sources for obtaining a large amount of earth observation data. Most 3D-2D registration methods usually require to detect reliable features by manual (Rönnholm and Haggrén, 2012) This type of method cannot effectively be applied for automatic registration of LiDAR data and aerial images. The third category is the 2D-2D registration method, which interpolates a 3D LiDAR point clouds into a DSM, an intensity image, or a distance image, which can transform the 3D-2D registration into the 2D-2D registration These methods can make use of existing algorithms from digital image registration (Zitova and Flusser, 2003), which are currently more mature and automatic than 3D-2D registration methods. The double-blind peer-review was conducted on the basis of the full paper
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