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PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING March 2014 205
Table 2. Error quantum to be introduced to nominal values of parameters (Note: The values are only place holders)
ΔX (m) ΔY (m) ΔZ (m)
Δω
(seconds)
Δϕ
(seconds)
Δκ
(seconds)
Mirror angle
scale (unit less)
Range errors
(meters)
–0.13 0.7 .17 17″ –18″ 72″ 0.13 0.7
–0.15 –0.14 0.05 11″ 106″ 5″ 0.15 –0.14
–0.04 0.07 0.08 71″ 129″ 66″ 0.04 0.07
errors from many sources, including boresigh parameters.
The test plan involves selecting different sets of values for
the boresight parameters and generating a test dataset. Vol-
unteers (data providers) distributed a table (similar to Table
2), with three sets of errors to be introduced to the nominal
parameter values, generating three data sets per volunteer.
The amount of errors introduced to the boresight parame-
ters will vary depending on discussions with data vendors.
The volunteer will store the data sets in separate folders,
while maintaining a record of errors, and process the data
sets with the DQM software tool. The output generated will
be analyzed to obtain summary estimates of errors in differ-
ent data sets. This analysis will be followed with discussions
by the ASPRS Cal/Val Working Group members on providing
summary statistics of errors in the data. The publication
of results of the analysis will help the customers of data to
correctly specify the quality of data for procurement and sci-
entific applications. In the next few months, processes will
also be developed to determine DQMs over planar features
and DQMs over linear features. Simultaneously, the USGS
will also lead the Cal/Val Working Group in identifying and
testing targets for absolute accuracy assessment of lidar data.
The geospatial community is invited to contact Aparajithan
Sampath(asampath@usgs.gov) or Greg Stensaas (stensaas@
usgs.gov) if they would like to participate in the process.
The ASPRS guidelines on Geometric Accuracy and Quality
of Lidar Data will incorporate the results of the analysis.
The development of DQMs can have additional applications.
Currently, there exists no accepted method of geometrical-
ly comparing two lidar or other 3D datasets, collected by
different vendors at different times. The DQMs will allow
the lidar user community to identify baseline dataset and
use DQMs to directly compare any lidar data set against
the baseline data and determine its relative accuracy. This
test may allow scientists to perform studies such as change
detection, time series analysis etc. with the confidence that
they are working with geometrically consistent datasets.
It is expected that this USGS- led ASPRS research will
result in an across-the-board improvement in the quality
of lidar data processing. The new DQMs will provide the
geospatial community with the capability to procure and
acquire lidar data of high and quantifiable accuracy.
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