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flying height. This would obviously cancel any gains in
resolution. However, it would produce images with larger
ground footprints which reduce the final size of the image
database. As the final target ground resolution increases,
motion blur will become increasingly important. One
option would be to use a helicopter capable of stationary
flying. Another significant cause of motion blur is low
light associated with cloudy weather. The experience of
the authors quite clearly indicates that in the case of
professional grade SLR digital cameras with pixel counts
above 12-15 megapixels, image blur will be significant
unless weather conditions are perfect and there is abun-
dant light. Furthermore, other authors (Dugdale, personal
communication ) have found that the type and altitude of
the cloud cover can also have a significant impact on
image quality with high cloud allowing for better quality
imagery than low cloud.
Smaller format cameras such as those produced for
the mass consumer market are also susceptible to motion
blur. One key difference between small format and SLR
cameras is the much reduced diameter and quality of the
lens system. Furthermore these cameras generally employ
smaller imaging sensors. As a result, these cameras need
brighter light conditions and are quite sensitive to motion
blur and not suited for use on board full sized aircraft.
However, these cameras have found a niche in UAV
and ULAVs applications. The payload of UAVs currently
available is often restricted to a few hundred grams which
is insufficient to carry full SLR cameras which generally
weigh in excess of 1 kg. Fortunately, small UAVs can fly
at very low speeds (often below 10 m/s). This therefore
allows for a reduction in motion blur which makes these
cameras a viable option.
Advanced imaging systems often use what is termed
'forward motion compensation' in order to reduce
motion blur. The basic principle is simply to move the
camera in the opposite direction to flight during the
split second of image exposure. This therefore results in
a reduction in the effective ground speed of the camera
and therefore leads to a reduction of motion blur (Pacey
and Fricker, 2005). Such systems have been widely used
in larger format aerial cameras since the 1980s (e.g. in
the classic Leica WildRC20 camera). Generally speaking,
forward motion compensation requires additional
weight. This is not an issue for general aviation aircraft
but remains problematic for small UAS platforms.
small UAVs have similar logistic constraints to that of
traditional fieldwork albeit with a requirement for good
weather which may be difficult to fulfil in certain climates
and in the immediate aftermath of floods. However, once
purchased their operational costs are generally quite low
since they can be operated by any staff with sufficient
training. In the case of traditional aircraft platforms
which must be contracted out to external companies, the
logistic challenge is often the rapid deployment of field
crews which are required to collect ground data in order
to calibrate and validate the subsequent data analyses
procedures (e.g. see Chapter 9). The associated costs
are generally determined by the length (or area) of the
survey. Figure 8.6 shows some sample costs for outsourced
airborne data acquisition. Some common satellite data
has been added for the purpose of comparison. However,
it should be noted that the costs in Figure 8.6 do not take
into account the mobilisation or deployment costs. These
costs therefore assume that the aircraft is based near the
study site. If this is not the case, the cost of moving the
aircraft in position closer to the study site will always be
directly passed on to the client.
8.3 Issues, potential problems
and plausible solutions
Several technical problems are associated with hyperspa-
tial image datasets. Many of the relatively new platforms
discussed above are still experimental and as a result,
acquisition is not yet perfectly controlled. Indeed, air-
craft orientation and elevation are not perfectly constant
during the acquisitions thus resulting in variations in the
ground footprint of the image and the spatial resolution of
the image. This complicates the already challenging issue
of georeferencing the imagery. Moreover, since we are
often using consumer cameras not specifically designed
for aerial use, image quality can often be sub-optimal.
Typically, the authors' experience has shown that image
brightness can vary significantly even when ambient light
conditions have not varied. Another common issue is
shading caused by bankside vegetation. This causes sig-
nificant problems in subsequent analysis methods such as
depth mapping and even basic classification. In fact the
process of classification is significantly more difficult with
hyperspatial imagery. Finally, even if all these hurdles
are progressively overcome with better equipment and
more advanced post-processing solutions, hyperspatial
image datasets are usually very large and often number
in the thousands of images. Image management and data
8.2.4 Logisticsandcosts
The diverse range of platforms discussed above implies a
range of logistic complications. Tethered platforms and
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