STATIONARY AIRCRAFT DETECTION FROM SATELLITE IMAGES

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.

Keywords:

Aircraft detection, satellite image analysis support vector machines, Gabor filter,

PDF

___

Jakubowicz J., Lefebvre S., Maire F., Moulines E., "Detecting aircraft with a low resolution infrared sensor," Image Processing, IEEE Transactions on, no.99, pp.1, doi: 10.1109/TIP.2012.2186307.
Hsieh, J.-W.; Chen, J.-M.; Chuang, C.-H.; Fan, K.-C.; , "Aircraft type recognition in satellite images," Vision, Image and Signal Processing, IEE Proceedings, vol.152, no.3, pp. 307315, 3 June 2005.
Das, S.; Bhanu, B.; Xing Wu; Braithwaite, R.N.; , "A system for aircraft recognition in perspective aerial images," Applications of Computer Vision, 1994., Proceedings of the Second IEEE Workshop on , vol., no., pp.168-175, 5-7 Dec 1994
Yingchun Li; Hexin Chen; Ming Zhao; Pengfei Qu; , "Selfadaptive cluster segmentation aircraft objects in aerial images," Intelligent Control and Automation, 2004. WCICA 2004. Fifth World Congress on , vol.6, no., pp. 5415- 5418 Vol.6, 15-19 June 2004.
http://www.google.com/earth/index.html
J. G. Daugman. “Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters.” Journal of the Optical Society of America A, 2(7):1160–1169, July 1985.
Cristianini, Nello; and Shawe-Taylor, John; An Introduction to Support Vector Machines and other kernel-based learning methods, Cambridge University Press, 2000.
B. Scholkopf, C. Burges and V. Vapnik, Proceedings of the First International Conference on Knowledge Discovery & Data Mining, AAAI Press, 1,252(1995).
T. Joachims, Learning to Classify Text Using Support Vector Machines, Kluwer Academic Publishers, 2002.
O. Deniz, M. Castrillon and M. Hernandez, “Who are you? [Face Recognition]”, International Conference on Pattern Recognition - ICPR , vol. 3, pp. 938-941 Vol.3, 2004
P. Shaoning, K. Daijin and Y. B. Sung, Pattern Recognition Lett. Vol. 24, pp. 215-223, 2003.
T. Gunes and E. Polat, Feature Selection for Multi-SVM Classifiers in Facial Expression Classification, 23rd Symposium on Computer and Information Sciences, October 2008, Istanbul, Turkey.
Tadayoshi Shioyama, Hai Yuan Wu, Shigetomo Mitani, "Object Detection with Gabor Filters and Cumulative Histograms," Pattern Recognition, International Conference on, p. 1704, 15th International Conference on Pattern Recognition (ICPR'00) - Volume 1, 2000.
Urolagin, S., Prema, K., & Reddy, S., Et Al. (2010). Rotation invariant object recognition using Gabor filters. Industrial and Information Systems ICIIS 2010 International Conference on (p. 404–407).
E. Polat, M. Yeasin, R. Sharma, “Robust Tracking of Human Body Parts for Collaborative Human Computer Interaction”, Computer Vision and Image Understanding Vol: 89, No: 1, 2003, pp. 44-69.

ISSN: 2619-9831
Başlangıç: 2001
Yayıncı: İstanbul Üniversitesi-Cerrahpaşa

Arşiv