ASPRS 2012 Annual Conference

Sacramento, California, March 19-23, 2012

OBJECT-BASED MOVING VEHICLE EXTRACTION FROM WORLDVIEW2

IMAGERY

Bahram Salehia, PhD Candidate

bsalehi@unb.ca

Yun Zhanga, Professor and

Canada Research Chair in Advanced Geomatics Image Processing

yunzhang@unb.ca

Ming Zhongb, Associates Professor

ming@unb.ca a Department of Geodesy and Geomatics Engineering,

b Department of Civil Engineering,

University of New Brunswick

15 Dineen Drive, Fredericton, New Brunswick, Canada, E3B 5A3

ABSTRACT

Moving vehicle detection is very important for transportation management and traffic monitoring. Due to the sub-

meter spatial resolution of very high resolution (VHR) imagery, vehicles can be identified from this type of imagery.

Furthermore, because of the slight time difference between image acquisition of onboard sensors, (i.e. Pan and MS

sensors) in VHR satellite such as Quickbird and GeoEye-1, a moving vehicle is observed, by the satellite, at two

different locations. Consequently, moving vehicles can be distinguished from the stationary ones by applying a

proper change detection algorithm. WorldView2 possess three sensors, i.e. a Pan and two MS sensors (MS1 and

MS2). Therefore, a moving vehicle is observed at three different locations. This feature together with the new

spectral bands of WV2 adds opportunity to improve moving vehicle detection and extraction. This paper, utilizing

an object-based framework, compares the automatic moving vehicle extraction by using the three pairs of WV2

sensors (i.e. Pan-MS1, Pan-Ms2 and MS1-MS2). The results show that of three image pairs, the MS1-MS2 is the

best choice for moving vehicle extraction because of the larger time lag between MS1 and MS2 than between the

Pan and MS1 or MS2.

KEYWORDS: moving vehicle extraction, WorldView2, object-based image analysis

INTRODUTION

Vehicle detection has many applications in transportation management and traffic monitoring. With the recent

advancement in sensor technology, the vehicle monitoring using satellite data has gain researchers’ attention in the

remote sensing community. The sub-meter spatial resolution of VHR imagery provides the opportunity for

automatic detection of small objects such as vehicles in the scene. Furthermore, because of the small time difference

between the panchromatic (Pan) and multispectral (MS) sensors, a moving target is observed at two different

positions by the satellite. This is the basis for extracting moving vehicle information such as speed and direction.

Over the last few years, several studies have been carried out for vehicle detections using very high resolution (VHR)

satellite imagery. Examples are McCord et al., 2005; Sharma et al., 2006; Zheng et al., 2006; Jin and Davis, 2007;

Zheng and Li, 2007, Xiong and Zhang, 2008; Pesaresi et al., 2008; Leitloff and Hinz, 2010; and Liu et al., 2010.

The newly launched WV-2 satellite carries three sensors, a pan and two MS sensors (MS1 and MS2). Each MS

sensor collects imagery in four bands. The sequence of images collected by WV-2 is MS1, Pan and MS2. There is a

fraction of second time lag in image acquisition between each pair of sensors (i.e. MS1-Pan, MS2-Pan, and MS1-

MS2). As a result, for a moving target, the Pan, MS1 and MS2 will record three different positions at different times.

Therefore, by applying change detection procedure to the image the moving objects can be detected. The time lag

ASPRS 2012 Annual Conference

Sacramento, California, March 19-23, 2012

between each MS image (i.e. MS1 and MS2) and the pan image is 0.13 seconds and the time lag between the MS1

and MS2 is 0.26 seconds (Smiley, 2011).

Moving targets including vehicles can be detected using each of the three pairs. This paper aims to extract the

moving vehicle using the images of all three pairs of WV2 sensors and to compare the results achieved by each pair

of images. The method, for each pair of images, includes three major steps: a) object-based road extraction, PCA-

based change detection, and c) object-based moving vehicle extraction.

STUDY AREA AND IMAGE DATA

The study area is a part of the City of Moncton in the Province of New Brunswick, Canada. A subset of

geometrically corrected WorldView-2 imagery was used in this study (figure 1). The image was acquired on

October 5, 2010. The WV-2 imagery includes a panchromatic band with the spatial resolution of around 0.5 m and

two sets of multispectral bands (MS1 and MS2); each contains four bands with the spatial resolution of around 2.0

m. The MS1 contains the conventional multispectral bands (i.e. blue, green, red, and near infrared), and the MS2

possesses four newly added bands which are coastal, yellow, red edge and near infrared-2 (DigitalGlobe, 2009). The

sequence of images collected by WV-2 is: MS1, Pan and MS2 (Smiley, 2011).The image contains different traffic

areas including a highway, a major road, some minor streets, and parking lots.

Figure 1. True colour pan-sharpened WV2 image of the study area.

METHODOLOGY

The methodologies used for all three pairs of the three images are similar. First, the roads are extracted using the

pan-sharpened image and an object-based image analysis framework. Next, a principal component analysis (PCA) is

ASPRS 2012 Annual Conference

Sacramento, California, March 19-23, 2012

applied to each pair of images to detect the change areas between two images. Finally, moving vehicles are extracted

using an object- based framework. The flowcharts for the methods based on the Pan-MS1 or Pan-MS2 (figure 2-

right) and MS1-MS2 (figure 2- left) are displayed in Figure 2.

Figure 2. Flowchart of the object-based moving vehicle extraction for different pairs of images. MS1-MS1

(left) and MS1-Pan and MS2-Pan (right).

Road Extraction

The search area for moving vehicle extraction can be restricted to roads. Therefore, road extraction is the first

step in our method. The object-based road extraction framework used in this study is the one we described in Salehi

et al. (2012). First, the eight MS bands were fused with the Pan band using the UNB-Pansharp algorithm (Zhang,

2004). Then, an object based-framework was developed (Salehi et al 2012) using the Cognition Network Language

available in eCognition® software package.

Change Detection

Having extracted the roads, the next step is to detect the change areas within the boundary of roads. As

mentioned the change can be detected using three different pairs of the image (i.e. Pan-MS1, Pan-MS2, and MS1-

MS2). For the Pan-MS1 and Pan-MS2, the MS image is first re-sampled in order to enhance its resolution to that of

the Pan image (i.e. 0.5m), and then the Pan image and the re-sampled MS image (MS1 or MS2) are stacked together

for creating a five-band image. Indeed, the first band of these five bands (i.e. the Pan band) were collected about

0.13 seconds after (for the MS1) or before (for MS2) the MS image. Hence, if a change detection method is applied

to these five bands, the detected changes represent the moving vehicles in two positions. A standardized PCA was

applied to the five-band image and the best PC (PC4 in this case) representing the moving vehicles along roads were

chosen. The last step is to extract the detected changes within the boundary of roads. Another object-based frame

work described in Salehi et al (2012) was utilized for extracting the change areas (i.e. moving vehicles). As shown

in figure 2, the methodology for extracting moving vehicles using the MS1-MS2 pair of images is very similar to the

ones used for the Pan-MS1 and Pan-MS2. For the MS1-MS2 pair, both MS1 and MS are re-sampled and stacked

together creating an eight-band re-sampled image. Next, the PCA change detection is applied to the stacked set of

the eight bands. Having determined the best principal component, in terms of enhancing the change areas within the

boundary of roads, an object-based framework is utilized to extract the enhanced changes (moving vehicles).

Pan-Sharp image

Object-based

road extraction

Roads

Pan image MS1 or MS2

Re-sampling

Change detection

Object-based

change extraction

Moving vehicles

MS1 image MS2 image

Re-sampling Re-sampling

Change detection

Object-based

change extraction

Moving vehicles

ASPRS 2012 Annual Conference

Sacramento, California, March 19-23, 2012

RESULTS AND DISCUSSION

Figure 2 shows the vector layer of extracted roads. All major roads and most of the minor roads were extracted

correctly. Some parking lots were also mis-extracted as roads; this mis-extraction can be reduced by a more complex

rule set for the classification of roads (e.g. incorporating more spatial features of objects). In this study, the “length”

of object was used as the shape feature for differentiating parking lots from roads.

Figure 3. The vector layer of extracted roads

The results of the PCA change detection, applied to each set of the three pairs of images, are depicted in Figure 4.

As it can be seen from the figure, the vehicles moving along the highway are detected as the two neighbouring dark

and bright objects. Each of these two neighbouring objects represents the vehicle location in each image. As seen,

the change in the location of the vehicles is more “obvious” for the MS1-MS2 pair than for Pan-MS1 and Pan-MS2.

This will lead to a better moving vehicle extraction for MS1-MS2 than for any of two other pairs. The time

difference between the MS1 and MS2 is twice than that between Pan and each MS image. This results in a larger

change in the vehicle location and consequently a more enhanced change detection result.

ASPRS 2012 Annual Conference

Sacramento, California, March 19-23, 2012

Figure 4. Pan-Sharp image (top-right) and PC4 of Pan-MS1 (top-left), PC5 of MS1-MS2 (bottom- left), and PC4 of

Pan-MS2 (bottom-right).

The Final results of the moving vehicle extraction for the three pairs of the image are depicted in Figure 5. Each

pairs of neigbouring red objects represents the moving vehicle. As expected the two locations of a moving vehicle

were correctly extracted for most of the vehicles in MS1-MS2 pair (figure 5-bottom), while the results for the two

other pairs are unsatisfactory. For the Pan-MS1 and Pan-MS2 in most cases only one location of the moving vehicle

was extracted. Regarding the performance of the PCA change detection, these results for the Pan-MS1 and Pan-MS2

was expecting as the detected changes (moving vehicle) in the PC4 are blur such that the segmentation algorithm is

not able to find the meaningful boundary for such blurry dark and bright areas in the PC4 image. For the Pan-MS1

and Pan-MS2, the pixel-based methods most likely outperform the object-based in extracting the change areas. On

the other hand, due to the relative large time difference between MS1 and MS2 images, the change detection results

in more distinct objects for each pair of the vehicles in the MS1 and MS2 image.

ASPRS 2012 Annual Conference

Sacramento, California, March 19-23, 2012

Figure 5. Final moving vehicle extraction results for a small part of the image for the three pairs of images. Pan-

MS1 (top-left), Pan-MS2 (top-right) and MS1-MS2 (bottom). Each pair of neighbouring red objects represents a

moving vehicle.

CONCLUSION

The results achieved in this paper suggest that the MS1-MS2 pairs of a WV2 image is the best choice, out of the

three pairs, for extracting moving vehicles using the proposed PAC-based change detection method. This is because

of the larger time lag between the MS1 and MS2 images than between the Pan and each of the MS images. In fact,

for the Pan-MS1 and Pan-MS2, the difference between the vehicles positions in two images is very small such that

the PCA-based change detection is not able to clearly detect the change areas. For this reason, pixel-based methods

ASPRS 2012 Annual Conference

Sacramento, California, March 19-23, 2012

may outperform the object-based ones in extraction the changes (moving vehicles) for the Pan and MS images of the

VHR satellite.

ACKNOWLEDGEMENT

The WorldView-2 image was provided by DigitalGlobe® Inc. to the first author through “The DigitalGlobe 8-

Band Research Challenge” contest. The authors are grateful to DigitalGlobe for making such a high quality dataset

available. This research is supported by the NSERC (Natural Science and Engineering Research Council of Canada)

Strategic Project Grants and NSERC Discovery Grants Programs.

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