Abstract
The automatic registration of LiDAR data and optical images, which are heterogeneous data sources, has been a major research challenge in recent years. In this paper, a novel hierarchical method is proposed in which the least amount of interaction of a skilled operator is required. Thereby, two shadow extraction schemes, one from LiDAR and the other from high-resolution satellite images, were used, and the obtained 2D shadow maps were then considered as prospective matching entities. Taken as the base, the reconstructed LiDAR shadows were transformed to image shadows using a four-step hierarchical method starting from a coarse 2D registration model and leading to a fine 3D registration model. In the first step, a general matching was performed in the frequency domain that yielded a rough 2D similarity model that related the LiDAR and image shadow masks. This model was further improved by modeling and compensating for the local geometric distortions that existed between the two heterogeneous data sources. In the third step, shadow masks, which were organized as segmented matched patches, were the subjects of a coinciding procedure that resulted in a coarse 3D registration model. In the last hierarchical step, that model was ultimately reinforced via a precise matching between the LiDAR and image edges. The evaluation results, which were conducted on six datasets and from different relative and absolute aspects, demonstrated the efficiency of the proposed method, which had a very promising accuracy on the order of one pixel.
Highlights
At this time, with the advent of various remote sensing sensors, multisource data exploitations are considered to be the most effective techniques
This paper aims to design an automatic registration method between LiDAR data and High
It can be categorized into feature-based registration techniques
Summary
With the advent of various remote sensing sensors (e.g., optical, thermal, RADAR, and LiDAR), multisource data exploitations are considered to be the most effective techniques. Thereby, data registration, which is a prerequisite for all multisource techniques, arises as a major research topic, especially when one looks for a reliable and accurate automatic method. In its common structure for most remote sensing applications, data registration consists of successive steps that include feature extraction, feature correspondence, transformation model determination and resampling [1]. Among those steps, the first and second ones, which lead to control features for the solution of a transformation model, are the most challenging, when dealing with the registration of heterogeneous data sources (e.g., LiDAR-optic, RADAR-optic, and LiDAR-RADAR). LiDAR data and HRSI, as examples of heterogeneous data sources, are unalike in different aspects: 1—the nature of the data acquisition
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