Abstract
In a traditional registration of a single aerial image with airborne light detection and ranging (LiDAR) data using linear features that regard line direction as a control or linear features as constraints in the solution, lacking the constraint of linear position leads to the error propagation of the adjustment model. To solve this problem, this paper presents a line vector-based registration mode (LVR) in which image rays and LiDAR lines are expressed by a line vector that integrates the line direction and the line position. A registration equation of line vector is set up by coplanar imaging rays and corresponding control lines. Three types of datasets consisting of synthetic, theInternational Society for Photogrammetry and Remote Sensing (ISPRS) test project, and real aerial data are used. A group of progressive experiments is undertaken to evaluate the robustness of the LVR. Experimental results demonstrate that the integrated line direction and the line position contributes a great deal to the theoretical and real accuracies of the unknowns, as well as the stability of the adjustment model. This paper provides a new suggestion that, for a single image and LiDAR data, registration in urban areas can be accomplished by accommodating rich line features.
Highlights
IntroductionThe discreteness in light detection and ranging (LiDAR) data makes it difficult to directly obtain semantic information (e.g., texture and structure) of ground features [2]
light detection and ranging (LiDAR) is characterized by high elevation accuracy, strong autonomy, and well-operated automatization [1], which could directly obtain the three-dimensional information of ground features
We extract the point cloud of building roofs in LiDAR data, and project the roof point cloud onto the image by the solved exterior orientation parameters (EOPs), where projected roof points are shown in blue
Summary
The discreteness in LiDAR data makes it difficult to directly obtain semantic information (e.g., texture and structure) of ground features [2]. LiDAR data and aerial images are highly complementary [2], so the combination of these two sources of data could simultaneously obtain three-dimensional, semantic, and texture information from spatial targets. This has important significance for feature extraction [3], three-dimensional reconstruction of buildings [4,5,6], and manufacturing of true orthophotos [7]
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