A higher-order tensor voting-based approach for road junction detection and delineation from airborne LiDAR data

The location and delineation of road junctions produce valuable inputs for road network understanding and topology reconstruction; the results also serve as basic information for road construction planning, emergency rescue, and large-scale data registration. This study addresses fully automatic road junction detection and delineation from airborne lidar (or LiDAR light detection and ranging) data. For this purpose, a higher-order tensor voting-based approach is developed, which can locate road junctions by identifying multidirectional features in encoded higher-order tensor models. This approach comprises three main steps. First, a roughness enhanced Gabor filter is implemented to process both the lidar-derived intensity image and the normalized digital surface model to extract homogeneous elongated structural keypoints. Second, these keypoints are fed into a higher-order tensor voting algorithm to recognize road junction candidates. We modified the method for encoding higher-order tensor models to enable the algorithm to deal with both antipodal symmetric and non symmetric junctions. After a higher-order tensor decomposition, the number of road branches and their directions are obtained; road junction candidates were then located. Third, by matching the results with a geometric template, road junction delineation is performed to retrieve the junction center position and the road branch directions and widths. We tested our method in two study areas and assessed the quality of the results by comparing them with ground-truth data. The results proved the validity of this approach in both urban and semiurban scenarios. We also compared our method with other junction detection algorithms and analyzed the parameter influence of the approach to essential parameters.