Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B1, 93-99, 2016
02 Jun 2016
A. Sampath1, H. K. Heidemann2, and G. L. Stensaas2 1SGT, Contractor to US Geological Survey’s EROS Data Center
2US Geological Survey EROS Data Center
Keywords: Lidar, Quality, Geometry, Relative, Accuracy Abstract. This paper provides guidelines on quantifying the relative horizontal and vertical errors observed between conjugate features in the overlapping regions of lidar data. The quantification of these errors is important because their presence quantifies the geometric quality of the data. A data set can be said to have good geometric quality if measurements of identical features, regardless of their position or orientation, yield identical results. Good geometric quality indicates that the data are produced using sensor models that are working as they are mathematically designed, and data acquisition processes are not introducing any unforeseen distortion in the data. High geometric quality also leads to high geolocation accuracy of the data when the data acquisition process includes coupling the sensor with geopositioning systems. Current specifications (e.g. Heidemann 2014) do not provide adequate means to quantitatively measure these errors, even though they are required to be reported. Current accuracy measurement and reporting practices followed in the industry and as recommended by data specification documents also potentially underestimate the inter-swath errors, including the presence of systematic errors in lidar data. Hence they pose a risk to the user in terms of data acceptance (i.e. a higher potential for Type II error indicating risk of accepting potentially unsuitable data). For example, if the overlap area is too small or if the sampled locations are close to the center of overlap, or if the errors are sampled in flat regions when there are residual pitch errors in the data, the resultant Root Mean Square Differences (RMSD) can still be small. To avoid this, the following are suggested to be used as criteria for defining the inter-swath quality of data:

a) Median Discrepancy Angle

b) Mean and RMSD of Horizontal Errors using DQM measured on sloping surfaces

c) RMSD for sampled locations from flat areas (defined as areas with less than 5 degrees of slope)

It is suggested that 4000-5000 points are uniformly sampled in the overlapping regions of the point cloud, and depending on the surface roughness, to measure the discrepancy between swaths. Care must be taken to sample only areas of single return points only. Point-to-Plane distance based data quality measures are determined for each sample point. These measurements are used to determine the above mentioned parameters. This paper details the measurements and analysis of measurements required to determine these metrics, i.e. Discrepancy Angle, Mean and RMSD of errors in flat regions and horizontal errors obtained using measurements extracted from sloping regions (slope greater than 10 degrees). The research is a result of an ad-hoc joint working group of the US Geological Survey and the American Society for Photogrammetry and Remote Sensing (ASPRS) Airborne Lidar Committee.

Conference paper (PDF, 1465 KB)

Citation: Sampath, A., Heidemann, H. K., and Stensaas, G. L.: GEOMETRIC QUALITY ASSESSMENT OF LIDAR DATA BASED ON SWATH OVERLAP, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B1, 93-99, doi:10.5194/isprs-archives-XLI-B1-93-2016, 2016.

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