186
Dark car
Figure 9. Different reflectance
(3) Background confusing
Some cars’ background is very confusing. Sometimes the
background is detected as vehicle.
Figure 10. Confusing background
4. DISCUSSION
The accuracy of moving vehicle detection is based on the
techniques of satellite sensor model refinement, image
resolution, accuracy of vehicle image coordinates, accuracy of
satellite time interval, and DEM accuracy. The accuracy of
satellite time interval and image resolution is related to satellite
equipment. They can be considered as constants.
Because the time interval of panchromatic image and multi-
spectral image is very small, which means the intersection
angle of the PAN and MS images is also very small, so we can
not calculate vehicle ground coordinates just based on its image
coordinates. DEM is necessary and its affection to the ground
coordinates calculation should be limited in a reasonable range.
DEM accuracy is a very complicated difficulty. Many research
scientists are focusing on this topic. But in this research it is not
focus.
So the last aspect that can be improved is the accuracy of
vehicle image coordinates. In our experiment, we used the
mean gray value as threshold to select vehicle pixels, so as to
calculate vehicle’s central position. But because the vehicles
can have different color, they can have different reflectance.
Some vehicles have more than one color, such as half bright,
half dark, or top bright, the others dark. Therefore, for these
vehicles, taking the mean gray value as threshold to select
vehicle pixels is not very suitable. On the other hand, the
vehicle is so small on the satellite imagery, so the color
information is very limited. This is a problem we should pay
attention to in the future.
From the experiment, we noticed that the vehicle’s color,
background, and their relative position all can affect the
detection and later the accuracy.
5. CONCLUSION
We have presented the whole procedure of extracting vehicle
information from Quickbird imagery based on an automatic
method. It includes several steps: image classification, vehicle
detection, image matching, calculation of image position on the
PAN image and the MS image, and calculation of velocity. The
experiment result shows that this technique can detect moving
vehicle and extract vehicle’s position, moving speed and
moving direction. But we recognized there is still very potential
for further improvement in the vehicle image coordinates
calculation, so that to improve the accuracy of the vehicle
information. As the satellite time interval is very small and
vehicle’s moving distance during this short time is also very
limited, so even a very small improvement in the vehicle image
coordinates measurement, say 0.1 pixels, will give a very big
contribution to the accuracy of moving vehicle detection. This
also is our next focus.
We noticed that classification and image segmentation may not
be the best techniques to detect vehicle. The accuracy is limited
by the poor spectral feature of QuickBird satellite images.
Therefore, a new method to accurately detect the vehicle is
urgent and necessary.
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