PE&RS October 2015 - page 787

Applying ASPRS Accuracy Standards to Surveys
from Small Unmanned Aircraft Systems (UAS)
Ken Whitehead and Chris H. Hugenholtz
Abstract
We present a first assessment of UAS-derived orthoimagery and
digital elevation data in the context of newly-released accuracy
standards for digital geospatial data developed by the American
Society for Photogrammetry and Remote Sensing. We outline
results from two case studies using a commercially-available
UAS, photogrammetry software, and an array of ground control
and check points. Radial horizontal and vertical root-mean-
square-errors (RMSE) were calculated as 0.05 m and 0.06 m, re-
spectively, for one site, and 0.08 m and 0.03 m, respectively, for
the other. Under the 1990 ASPRS standards, both surveys meet
the requirements for Class 1 accuracy at the 1:500 map scale
and at the 0.50 m contour interval. Under the newly-developed
ASPRS standards, the reported errors fulfill the requirements for
both horizontal and vertical mapping at the 10 cm RMSE level.
Overall, these results provide initial direction for practitioners
considering UAS surveying in the context of accuracy standards.
Introduction
In recent years, small unmanned aircraft systems (UASs) have
become an affordable and flexible option for remote sensing
and surveying. In Canada, the twin drivers of low cost and
a supportive regulatory environment have fueled tremen-
dous growth in research and commercial services involving
lightweight (i.e., < 5kg) UASs (e.g., Whitehead and Hugenholtz,
2014). As the advantages of small UASs have become ever more
apparent, the adoption of this technology for aerial surveying
has increased dramatically in Canada (Whitehead et al., 2014).
One of the main uses of small UASs is to carry out photo-
grammetric surveys over small (<3 km2) areas (e.g., Hugen-
holtz et al., 2013; d’Oleire-Oltmanns et al., 2012; Eisenbeiss,
2009). Traditionally, photogrammetry has made use of pho-
tography obtained using large-format metric cameras flown
at comparatively high altitudes. However, UAS surveys are
normally flown using poorly-calibrated compact cameras at
low altitudes (Hardin and Jensen, 2011). As such, the prop-
erties of the imagery obtained are often quite different from
those associated with conventional aerial photography. Imag-
ery acquired from small UASs typically has variable imaging
geometry and the amount of overlap between images can vary
significantly (Hardin and Jensen, 2011).
Initially, photogrammetric processing for UAS surveys was
carried out using software packages designed for conventional
photogrammetric surveys, such as LPS (formerly known as
Leica Photogrammetry System) (e.g., d’Oleire-Oltmanns et al.,
2012; Mozas-Calvache et al., 2012), and Inpho (e.g., Hugen-
holtz et al., 2013; Whitehead et al., 2013). However the tradi-
tional photogrammetric approach imposes rigorous constraints
on image geometry and requires well-calibrated cameras in or-
der to give optimal results. In recent years the development of
structure from motion (SfM) packages such as Bundler, Pix4d,
and Photoscan (e.g., Westoby et al, 2012; Turner et al., 2012;
Fonstad et al, 2013) has made it easier to produce high-quality
digital elevation models (DEMs) and orthoimage mosaics from
imagery acquired with variable orientations and overlap, and
from cameras that have not been rigorously calibrated.
Although imagery acquired from small UASs typically has
sub-decimeter ground resolution, this high spatial resolution
does not necessarily imply correspondingly-high spatial ac-
curacies. Many UAS-based studies have provided planimetric
and vertical accuracy estimates (e.g., Whitehead et al., 2013;
Hugenholtz et al., 2013; Nex and Remondino, 2013; d’Oleire-
Oltmanns et al., 2012). These estimates are typically derived by
comparing the surveyed and measured positions of a number
of checkpoints on the final DEM and orthoimage. While such
estimates can be useful for assessing the quality of the survey,
accuracy checking is usually carried out on an ad-hoc basis,
making it difficult to compare the accuracies of different sur-
veys carried out by small UASs. Such an inconsistent approach
can also cause problems when comparing results with those
obtained from surveys carried out by other methods, and may
make it difficult to identify optimal processing methodologies.
This paper presents a first assessment of UAS accuracy in
the context of mapping standards newly established by the
American Society for Photogrammetry and Remote Sensing
(ASPRS). ASPRS is one of several organizations who rigorously
define standards for positional accuracy on maps and geospa-
tial data. ASPRS released accuracy standards for large-scale
mapping in 1990 (ASPRS, 1990) and has recently released a
new set of standards appropriate to mapping from digital
imagery (ASPRS, 2015). To the best of our knowledge, UAS data
has not yet been evaluated in the context of these standards,
which motivated the study presented herein. We outline two
case studies in non-vegetated terrain, using commercially-
available SfM software and a large array of ground control
points (GCP s) and checkpoints. The broader goal of this
research is to provide initial direction on achievable accuracy
for practitioners who are considering the application of UASs.
The Photogrammetric Process
Photogrammetric reconstruction involves recreating the exact
geometric conditions under which a series of aerial photos
have been acquired. A series of overlapping images covering
the area of interest are typically combined into a block. Each
image in the block has six exterior orientation parameters
(EOP), comprising shifts in the x-, y-, and z -directions, and the
corresponding rotations around each axis, which are denoted
by the Greek letters ω , φ, and κ, respectively. In traditional
aerial photogrammetry, a point on the ground, the center of
the camera lens, and the representation of the point on the
camera’s image plane are assumed to be collinear. If a number
Department of Geography, University of Calgary, 2500
University Drive NW, Calgary, AB T2N 1N4, Canada
(kwhitehe@ucalgary.ca).
Photogrammetric Engineering & Remote Sensing
Vol. 81, No. 10, October 2015, pp. 787–793.
0099-1112/15/787–793
© 2015 American Society for Photogrammetry
and Remote Sensing
doi: 10.14358/PERS.81.10.787
PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING October 2015 787