International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B7. Istanbul 2004
assembled into strings, whereby some elements (e.g. date and
parts of coordinates) are unchanged for the whole video and
therefore fixed. The following table gives and overview of the
initial values and a typical readout (Table 2).
Initial values Example output string
latitude "0006 ..,...." Latitude 0006 54.900
longitude "52 ..,..." longitude 52 13.000
angleH -14.23
angleV -33.97
time 17h18m23s
date 2000:05:13
angleH ;........
angleV ........
time "l.h. m.s"
date "2000:05:1."
Table 2. Example result of frame information decoding
rt
2 mn
Ze d
Figure 4. (a) Pre-disaster Ikonos image overlaid with post-event
aerial photographs, and with helicopter flightpath of 13 May
plotted (yellow dots) based on information extracted from the
video data. The immediate disaster area is indicated in yellow.
(b) Camera viewing directions calculated from the GPS
auxiliary data. These data can also be used to plot the footprints
of individual frames, as illustrated in (c). The solid red box
shows the approximate location of the frame shown in (d). The
box in hatched red is the result of the automatic calculation,
based on absolute azimuth, camera inclination, and helicopter
location. Focal length and flying height were simulated. The
apparent positional difference between the two footprints results
from uncertainty in the absolute camera azimuth, as the
helicopter orientation is not necessarily identical to the flight
direction, further illustrating the need for IMU information.
A problem for the correlation was the low video quality, in
particular the horizontal instability between lines, a result of
interlacing of two frames carrying half the information each
(odd vs even lines). Therefore we first deinterlaced the frames
(combining the two half-frames into one), and sharpened the
result. The subsequent correlation and string processing then
took approximately 4 sec per frame.
The resulting table was then further processed to convert the
geographic coordinates into UTM to make them fit our
reference imagery, as well as to calculate the absolute flight
vector between frames and absolute camera azimuth. We
consider the inclination angle to be absolute, although it is
dependent on the roll, yaw and pitch of the helicopter. The
effect on the IFOV of the camera can be substantial, and should
be corrected for with Inertial Measurement Unit (IMU)
information.
3.4 Video mosaicing .
The erratic nature of video imagery detailed above complicates
its use. Unlike with vertical aerial photographs and satellite
imagery a simple geocoding is not possible. However, given the
value for overview and orientation purposes of such a mosaic,
we used RavenView (www.observera.com) to assemble a
mosaic of the disaster site based on the police video data
(Figure 5). The software also allows a geocoding, although for
that a sensor model of the camera used is required.
Figure 5. Mosaic of the Enschede disaster site, comprising 227
video frames (red line approximates outline in Figure 1 b)
4. CONCLUSIONS AND DISCUSSIONS
In this project we investigated the utility of oblique airborne
video data for urban post-disaster damage assessment. The main
objectives were to enhance the overall quality (lower
signal/noise ratio) of the imagery, and detect damage based on
hue, intensity and saturation (HIS), as well as edge and variance
characteristics in a specially created processing environment.
We furthermore explored possibilities to register video data
spatially based on encoded GPS information.
The data quality enhancement carried out in AstroStack, based
on aligning and stacking, led to a measurable improvement. The
SA approach described by Gorny and Latypov (2002) was also
verified in a theoretical experiment, but the method did not
improve the quality of the video data. This was likely a result of
several data conversion steps that led to severe image
degradation and colour bleeding, but also of low contrast that IS
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