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(1) Maximum likelihood classification 
The spectral feature is used for classification. Six classes are 
selected: high way, car, vegetation, concrete ground, park area, 
water. Figure 1 shows the discrete point graph of these six 
classes. Figure 2 shows the spectral feature and figure 3 shows 
a classification sample. 
Figure 1. Discrete point graph in 4 dimensions 
Figure 3, Image classification 
(2) Vehicle detection 
After image classification, the vehicle detection is performed on 
the classified image based on following criteria. 
■ class=2 
■ Surrounded by 1 
■ Not longer than 5 pixels on multispectral image 
■ Mean DN number greater than a threshold 
■ Mean DN number greater than its background by a threshold 
Mean_DN_value=(Min+Max)/2 (1) 
Grey value 
The vehicle candidate’s position on the MS image is calculated 
with equation 2 and 3. 
II 
M 
(2) 
n 
II 
(3) 
Where gray(7, J) > meanDNvalue. 
(3) Image matching 
Because some vehicles are very close and similar (Fig. 5), small 
area based matching can not differentiate these vehicles. A two 
step image matching method is used to search corresponding 
point on the PAN image. The first step, a large area-based 
image matching is used to find a coarse position on the PAN 
image. The second step takes this position as initial position, 
use small area-based image matching to search the final 
position. 
(4) Calculation of vehicle position on the PAN image 
The two-step image matching only can find the vehicle on the 
PAN image. The vehicle’s central position should be detected 
before calculation of vehicle position and velocity. The method 
in step (2) is used to detect the vehicle’s central position on the 
PAN image. 
(5) Calculation of vehicle’s moving speed and direction 
After the vehicle’s position on the PAN image is detected, the 
vehicle’s position, moving speed, and moving direction are 
calculated by a direct location algorithm (Xiong Z. and Zhang 
Y„ 2007). 
3. EXPERIMENT 
A pair of level 1A Quickbird images which contains one 0.61 
meter resolution panchromatic image and one 2.44 meter 
resolution multi-spectral image (figure 3) is used to test this 
technique. These images are acquired on July 26, 2002 in 
Gagetown, New Brunswick, Canada. We just used a part of 
whole scene image for our experiment. Below is the detailed 
data clipping information.