174
April 2018
PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING
I
ntroduction
Calibration is often called for with a new instrument. Howev-
er, instrument error can occur due to a variety of factors such
as environment, electrical supply, addition of components, pro-
cess changes, etc. Therefore, calibration should be performed,
at a minimum, as directed by the instrument manufacturer
either periodically, or based on usage. Calibration should also
be performed when an instrument has had a shock or vibra-
tion (which potentially may put it out of calibration), any time
it is reinstalled on the host platform (e.g., after aircraft main-
tenance), or whenever observations appear questionable. It is
common practice calibration of a partial set of sensor system
parameters can be performed in situ (field tests or self-calibra-
tion), and it is a standard practice among lidar data providers
to perform such a calibration of boresight parameters.
The users of lidar point cloud data require quantifiable means to
be assured of the calibration of the lidar data acquisition system.
This quality of calibration of lidar systems can be quantified
without requiring externally referenced data. These means are
Intra Swath analysis of data and Inter Swath analysis of data.
Intra Swath Analysis
The Intra Swath analysis of data quantifies the level of noise
presentinthelidarsensor(fore.g.duetotherangemeasurements
due to jitter). In the current specifications (Heidemann 2014),
the intra swath quality of data is in general defined as the
repeatability of measurements over a smooth surface, and in
particular, repeatability of elevation values over a flat surface.
Inter Swath Analysis
Lidar data for large projects are collected by flying swaths, with
overlap between portions of the swaths to prevent data gaps
or to ensure the required level of point density. The quality
of calibration of a lidar instrument and data acquisition most
readily manifests itself in these overlapping areas. The USGS-
ASPRS WG has defined several measures to quantify the
Inter Swath accuracy or relative accuracy of lidar data. These
measures are termed Data Quality Measures (DQMs).
The WG have also defined the process of measuring these
DQMs and defined how these could be summarized to robust-
ly quantify the lidar point cloud’s geometric quality. More de-
tails can be found in the “ASPRS Guidelines of Measurement
of Inter Swath Geometric Quality of Lidar data.
The sampled locations where DQMs are measured be catego-
rized as functions of slope of terrain: Flat terrain and slopes
greater than 10 degrees. For higher slopes, the DQM errors
are further categorized as belonging to slopes along track and
across track. The WG recommends that the Inter Swath qual-
ity may be reported in three parts for flat areas:
a) The distance of each sampled location from the mean
centerline of overlap is measured.
b) The discrepancy angle at each sampled location is mea-
sured.
c) The summary statistics for the discrepancy angles is
recorded. For high quality data, the discrepancy angles
must be close to zero, and the standard deviation must
also be low.
The Discrepancy Angle (DA) is a measure of systematic er-
rors present in the lidar point cloud, and is a direct indicator
of the geometric quality of the data.
Recommendations for Further Research
To quantify the intra swath noise level in the data, the WG
recommends research and development efforts towards an-
swering the following questions:
•
How should the intra swath “noise” be mathematically
defined?
•
How should measurements be made to quantify intra
swath noise?
•
How should these measurements be summarized (in
terms of mean, median, standard deviation, and spatial
distribution, etc.)?
On Inter Swath analysis, the WG further recommends that
government agencies, academia and the industry work to-
gether towards determining acceptable solutions to the fol-
lowing questions:
•
Are current thresholds for relative accuracy valid?
•
Are the thresholds measurable?
•
Should the thresholds be different for different terrain?
•
What should be the thresholds for the discrepancy angles?
It is also recommended that a comprehensive error library
consisting of systematic errors found in lidar data and their
source be maintained by the ASPRS.
Table 1: Data Quality Measures (DQMs) or inter-swath goodness of fit measures.
Nature of surface
Examples
Data Quality Measures (DQMs)/Goodness of fit measures
Units
Natural surfaces
Ground surface, i.e.
not trees, chimneys,
electric lines etc.
Point to natural surface (tangential plane to surface) distance
Meters
Point to surface vertical distance
Meters
Man-made
surfaces
Roof planes
Perpendicular distance from the centroid of one plane to the conjugate plane Meters
Roof edges
Perpendicular distance of the centroid of one line segment to the conjugate
line segment
Meters
