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STATIONARY AIRCRAFT DETECTION FROM SATELLITE IMAGES

Journal Name:

  • Istanbul University Journal of Electrical & Electronics Engineering

Publication Year:

  • 2012

Key Words:

Author NameUniversity of Author
Abstract (2. Language): 
Satellite image analysis is an important research area in the field of image processing. Detection and recognition of regions and objects from satellite images find many useful civil applications such as detection of buildings, roads, bridges and other man-made objects as well as land plant classification. On the other hand, the detection of stationary aircrafts in airports can be strategically important in military applications. In this study, a learning-based system that detects stationary aircrafts in satellite images obtained from Google Earth is developed. The features that emphasize the geometric structure of an aircraft are determined using 2D Gabor filter. The aircraft detection is performed using Support Vector Machines (SVM) classification method. The SVM is a supervised learning method that analyzes data and recognizes patterns for classification The SVM takes a set of input data (a vector consists of Gabor filter output of images) and predicts the one of two classes (aircraft or non-aircraft). The performance of the system is demonstrated using satellite images collected from airports in Europe and United States.
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FULL TEXT (PDF): 
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  • 2
1523-1528
  • English

REFERENCES

References: 

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Ediz Polat received B.S. degree in Electrical-Electronics Engineering Department at Gazi University, Turkey in 1992, and M.S. and PhD. Degrees in Electrical Engineering Department at Pennsylvania State University, USA, in 1996 and 2002. His research interests include image processing, computer vision and pattern recognition. He is currently working in Electrical-Electronics Engineering Department at Kirikkale University, Turkey.

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