Full text: Mapping without the sun

183 
AUTOMATED VEHICLE INFORMATION EXTRACTION FROM ONE PASS OF 
QUICKBIRD IMAGERY 
Zhen Xiong, Yun Zhang 
Department of Geodesy & Geomatics Engineering, University of New Brunswick, 
15 Dineen Drive, PO Box 4400, Fredericton, NB, Canada E3B 5A3 
v009v@unb.ca, yunzhang@unb.ca 
Commission VI, WG VI/4 
KEY WORDS: Vehicle Information Extraction, One Pass, Quickbird Imagery 
ABSTRACT: Vehicle information is useful for transportation management, security surveillance and military applications. The 
vehicle information includes vehicle’s ground position, moving speed, and moving direction. Vehicle detection and vehicle 
information extraction is usually based on Radar, SAR, or video. The platforms are almost ground based or airborne based; but 
seldom is space-borne based. Basically these techniques are usually used for security surveillance or military applications. Some of 
them just provide the service of catching vehicle, without any information on the vehicle’s position and velocity. An automated 
method of vehicle information extraction from Quickbird imagery is presented in this paper. Because there is a time interval between 
Quickbird’s panchromatic and the multi-spectral imagery, once we get a vehicle’s image positions on both the MS and the Pan 
imagery, we can calculate vehicle’s ground position and distance the vehicle moved during the time interval, so as to calculate 
vehicle’s moving speed and moving direction. In this research, an image classification based vehicle detection method, a region 
growing method to refine the image position, a multi-step image matching method to search the corresponding vehicle position, and 
an algorithm to calculate vehicle’s ground position from its image position based on DEM are developed. From vehicle’s image 
position to ground position, image position, sensor model, DEM, image matching, and region growing are involved in the process. In 
this paper, the methodology is introduced first. Later an experiment and result are given. Finally the discussion and conclusion are 
delivered. 
1. INTRODUCTION 
Moving target detection is a fast growing research field. Most 
of techniques for moving target detection are based on Radar 
[Liu Guoqing, et al., 2001; Nag, S., et al., 2003; Liu, C.-M. Jen, 
C.-W., 1992], SAR [Dias, J.M.B., et al., 2003; Hong-Bo Sun, et 
al., 2002; Pettersson, M.I., 2004; Soumekh, M.I., 2002], or 
video [Munno, C.J., et al., 1993]. The platforms are almost 
ground based [Castellano, G., et al., 1999; Nag, S., et al., 2003; 
Munno, C.J., et al., 1993; Pettersson, M.I., 2004] or airborne 
based [Liu Guoqing, et al., 2001; Hong-Bo Sun, et al., 2002; 
Soumekh, M.I., 2002]. But seldom is space-borne based. 
Basically these techniques are usually used for security 
surveillance or military applications. Some of them just deliver 
the service of catching moving target, without any information 
on the target’s position and velocity. Based on different 
equipment, different techniques were adopted for catching 
moving targets, such as using generalized likelihood ratio as a 
threshold to decide which target is moving [Liu Guoqing, et al., 
2001; Dias, J.M.B., et al., 2003; Pettersson, M.I., 2004]; using a 
filter for digital moving target detection [Nag, S., et al., 2003]; 
applying a fractional Fourier transformation [Hong-Bo Sun, et 
al., 2002]; or utilizing victo’s frequency domain spatiao- 
temporal filtering and spatio-temperal constraint error of image 
frame pairs to detect and track moving targets (e.g., personnel) 
in natural scenes in spite of low image contrast, changes in the 
target's infra-red image pattern, sensor noise, or background 
clutter [Munno, C.J., et al., 1993]. 
In this paper we introduce a new technique to vehicle’s 
positions and moving speed based on a single set of high spatial 
resolution satellite imagery. Such single set of satellite imagery 
consists of one panchromatic image and the corresponding 
multi-spectral imagery, which are acquired by some current 
high resolution satellites, such as SPOT, IKONOS, and 
Quickbird. Although the time interval between panchromatic 
image and multi-spectral image is very small, if the vehicle is 
moving, the panchromatic image and multi-spectral image 
should record this position change during satellite time interval. 
So theoretically if the vehicle is moving, we can find the 
vehicle is moving and even moving speed of the vehicle. We 
developed an algorithm to calculate the position and moving 
speed of moving vehicle which is acquired by both 
panchromatic and multispectral images at a very close time. 
2. METHODOLOGY 
To date, many satellites can acquire both the panchromatic 
(PAN) and the multi-spectral (MS) images at the same time. 
But because of technique arrangement, these satellites usually 
catch the panchromatic and multi-spectral images not really at 
same time. Usually there is a very small time interval between 
the PAN and the MS imagery. Therefore, if a ground vehicle is 
moving, theoretically this moving vehicle should be record in 
different ground position. That is to say, if we can calculate 
vehicle’s ground position from image coordinates, we should 
obtain two different coordinates from the PAN and the MS 
images respectively for the same moving vehicle. Then from 
these two ground coordinates, we can calculate the moving 
speed and moving direction of the vehicle. This is the elements 
of our moving vehicle detection technique. 
However, because the time interval between the PAN and the 
MS imagery is very small, less than 1 second, the position of 
moving vehicle changes within this small time interval is also 
very small. Therefore if the error of position calculation is 
greater than the value of its position change, we can never 
detect moving vehicle correctly. Therefore, some methods must 
be used to minimize the errors of vehicle position calculation. 
Following is the detailed steps of moving vehicle information 
extraction.
	        
Waiting...

Note to user

Dear user,

In response to current developments in the web technology used by the Goobi viewer, the software no longer supports your browser.

Please use one of the following browsers to display this page correctly.

Thank you.