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PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING
Figure 1. Framework for draft Guidelines on Geometric Accuracy and Quality of Lidar Data.
Figure 2. Surface uncertainties in hypothetical adjacent swaths. Profile of actual surface is shown as solid line while the
surface defined by swath # 1 and swath # 2 are shown as dotted lines.
Internal Accuracy of Lidar Data Through DQMs
The importance of correct calibration of a lidar sys-
tem to the data acquisition process and to the geometric
quality of data cannot be overstated. A good calibration
involves precise measurements between the various
subsystems of a lidar system, including the lidar instru-
ment, GPS receiver and IMU (Habib et. al., 2010).
Figure 2 shows a profile of a surface that falls in the
overlapping region of two adjacent swaths. The surface as
defined by the swaths is shown in dotted lines while the
solid profile represents the actual surface. A poorly cali-
brated system leads to at least two kinds of errors in lidar
data. The first error is that the same surface is defined in
two (slightly) different ways (relative or internal error)
by different swaths, and the second error is the deviation
from the actual surface (absolute error). For most users of
lidar data, the calibration procedures are of less concern
than the data themselves. However, users would like to
have a process to test the quality of calibration of the in-
strument, because a well-calibrated instrument is a neces-
sary condition for high quality data. While data providers
make every effort to reduce the kind of errors shown in
Figure 2, there are no standard methodologies in current
QC processes to measure the internal goodness of fit be-
tween adjacent swaths (i.e. internal or relative accuracy).
Current specifications documents (e.g. Heidemann 2012)
do not provide guidance on measuring the inter-swath (in-
ternal accuracy) goodness of fit of lidar data. The ASPRS
Cal/Val Working Group is investigating three quanti-
ties (Table 1) that measure the inter-swath goodness of
fit. These measures describe the discrepancy between
two overlapping point clouds and are often used to ob-
tain optimal values of the transformation parameters.
