Paper
9 June 2014 Estimating sampling completeness of lidar datasets using voxel-based geometry
Author Affiliations +
Abstract
Advances in LIDAR technology have made sub-meter resolutions from airborne instruments possible, enabling quick capture of fine 3D details over large areas. During collection, occluding objects may prevent a laser pulse from reaching regions where overlapping geometry is present, such as under tree canopies. This is particularly true given the near-nadir angles typically used by airborne LIDAR, since the limited number of unique angles does not ensure that all surfaces can be sensed. These missed surface detections decrease the overall quality of a dataset, but are not normally quantified due to a lack of ground-truth. Using information that is normally discarded about the LIDAR instrument position, we show how these unsampled regions can be identified by tracing the path of each laser pulse. A voxel representation provides the framework for computing the necessary statistics, and also allows for correct representations of overlapping geometry in complex environments. Based on this novel unsampled information we show how the fraction of total surfaces sensed and not sensed by the LIDAR can be estimated, giving a measurement of how completely all surfaces are sampled. Results are demonstrated for a real-world dataset, including the effects of voxel resolution and data density on the sampling completeness metric.
© (2014) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Shea Hagstrom, David Messinger, and Katie N. Salvaggio "Estimating sampling completeness of lidar datasets using voxel-based geometry", Proc. SPIE 9080, Laser Radar Technology and Applications XIX; and Atmospheric Propagation XI, 90800P (9 June 2014); https://doi.org/10.1117/12.2050552
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Cited by 2 scholarly publications and 1 patent.
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KEYWORDS
LIDAR

Interfaces

Data modeling

3D modeling

Clouds

Pulsed laser operation

Tin

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