PE&RS March 2018 Public - page 117

PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING March 2018 117
Summary
This document provides guidelines on quantifying the rela-
tive horizontal and vertical errors observed between conju-
gate features in the overlapping regions of lidar data. The
quantification of these errors is important because their pres-
ence estimates 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 that data acquisi-
tion processes are not introducing any unforeseen distortion
in the data. Good geometric quality also leads to better geolo-
cation accuracy of the data when the data acquisition process
includes coupling the sensor with GNSS.
Current specifications are not adequately able to quantify
these geometric errors. This is mostly because the methods to
quantify systematic and non-systematic errors have not been
investigated well. Accuracy measurement and reporting prac-
tices followed in the industry and as recommended by data
specification documents (Heidemann 2014) 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 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:
ASPRS Guidelines on
Geometric Inter-Swath
Accuracy and Q uality of
Lidar Data
a) Median Discrepancy Angle
b) Mean and RMSD of Horizontal Errors using DQM mea-
sured on sloping surfaces
c) RMSD for sampled locations from flat areas (defined as
areas with less than 10 degrees of slope)
A user defined number of test points (in testing 2000-5000
points were used per swath pair) are uniformly sampled in
the overlapping regions of the point cloud, to measure the dis-
crepancy between swaths. Care is taken to sample only areas
of single return points. Point-to-Plane data quality measures
are determined for each sample point and are used to deter-
mine the above mentioned quality metrics. This document
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 er-
rors obtained using measurements extracted from sloping re-
gions (slope greater than 10 degrees).
The inter-swath assessment of data is one of the highest prior-
ity lidar QA components identified by the US Geological Sur-
vey (USGS)/ASPRS Lidar Data Quality Working Group, (also
referred to as the “ASPRS Lidar Cal/Val Working Group”) as
documented in “Summary of Research and Development Ef-
forts Necessary for Assuring Geometric Quality of Lidar Data”.
Photogrammetric Engineering & Remote Sensing
Vol. 84, No. 3, March 2018, pp. 117–128.
0099-1112/17/117–128
© 2018 American Society for Photogrammetry
and Remote Sensing
doi: 10.14358/PERS.84.3.117