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Progress In Electromagnetics Research
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A NOVEL SAR TARGET DETECTION ALGORITHM BASED ON CONTEXTUAL KNOWLEDGE

By F. Gao, A. Ru, J. Sun, and A. Hussain

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Abstract:
This paper proposes a Synthetic Aperture Radar (SAR) vehicle target detection algorithm based on contextual knowledge. The proposed algorithm firstly obtains the general classification of SAR image with a Markov Random Field (MRF)-based segmentation algorithm; then modifies the prior target presence probability utilizing terrain types, distances to boundary and target aggregation degree; finally gains the detection results using improved Cell Averaging-Constant False Alarm Rate (CA-CFAR). Detections with real SAR image data show that this algorithm can effectively improve target detection rate and reduce false alarms compared with conventional CA-CFAR.

Citation:
F. Gao, A. Ru, J. Sun, and A. Hussain, "A Novel SAR Target Detection Algorithm Based on Contextual Knowledge," Progress In Electromagnetics Research, Vol. 142, 123-140, 2013.
doi:10.2528/PIER13062403
http://www.jpier.org/PIER/pier.php?paper=13062403

References:
1. Bhanu, B., "Automatic target recognition: State of the art survey," IEEE Transactions on Aerospace and Electronic Systems, Vol. 22, No. 4, 364-379, 1984.

2. Tian, B., D.-Y. Zhu, and Z.-D. Zhu, "A novel moving target detection approach for dual-channel SAR system," Progress In Electromagnetics Research, Vol. 115, 191-206, 2011.

3. Chiang, C.-Y., Y.-L. Chang, and K.-S. Chen, "SAR image simulation with application to target recognition," Progress In Electromagnetics Research, Vol. 119, 35-57, 2011.
doi:10.2528/PIER11061507

4. Szottka, I. and M. Butenuth, "Tracking multiple vehicles in airborne image sequences of complex urban environments," 2011 Joint Urban Remote Sensing Event, 13-16, Munich, 2011.
doi:10.1109/JURSE.2011.5764707

5. Gerhardinger, A., D. Ehrlich, and M. Pesaresi, "Vehicles detection from very high resolution satellite imagery," International Archives of Photogrammetry and Remote Sensing, Vol. 36, Part 3/W24, 83-88, 2005.

6. Di Bisceglie, M. and C. Galdi, "CFAR detection of extended objects in high-resolution SAR images," IEEE Transactions on Geoscience and Remote Sensing, Vol. 43, No. 4, 833-843, 2005.
doi:10.1109/TGRS.2004.843190

7. Gao, G., "A Parzen-window-kernel-based CFAR algorithm for ship detection in SAR images," IEEE Transactions on Geoscience and Remote Sensing Letters, Vol. 8, No. 3, 557-561, 2011.
doi:10.1109/LGRS.2010.2090492

8. Habib, M. A., M. Barkat, B. Aissa, and T. A. Denidni, "CA-CFAR detection performance of radar targets embedded in `non-centered Chi-2 Gamma' clutter," Progress In Electromagnetics Research, Vol. 88, 135-148, 2008.
doi:10.2528/PIER08092203

9. Cui, Y., G. Zhou, J. Yang, and Y. Yamaguchi, "On the iterative censoring for target detection in SAR images," IEEE Transactions on Geoscience and Remote Sensing Letters, Vol. 8, No. 4, 641-645, 2011.
doi:10.1109/LGRS.2010.2098434

10. Oliver, C. J. and S. Quegan, Understanding Synthetic Aperture Radar Images, Artech House, Norwood, MA, 1998.

11. Bar, M. and S. Ullman, "Spatial context in recognition," Perception, Vol. 25, No. 3, 343-352, 1993.
doi:10.1068/p250343

12. Biederman, I., R. J. Mezzanotte, and J. C. Rabinowitz, "Scene perception: Detecting and judging objects undergoing relational violations," Cognitive Psychology, Vol. 14, 143-177, 1982.
doi:10.1016/0010-0285(82)90007-X

13. Carbonetto, P., N. de Freitas, and K. Barnard, "A statistical model for general contextual object recognition," Proc. European Conf. Computer Vision, Vol. 3021, 350-362, 2004.

14. Divvala, S. K., D. Hoiem, J. H. Hays, A. A. Efros, and M. Hebert, "An empirical study of context in object detection," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2009.

15. Galleguillos, C. and S. Belongie, "Context based object categorization: A critical survey," Computer Vision and Image Understanding, Vol. 114, No. 6, 712-722, 2010.
doi:10.1016/j.cviu.2010.02.004

16. Galleguillos, C., A. Rabinovich, and S. Belongie, "Object categorization using co-occurrence, location and appearance," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 1-8, 2008.

17. Tian, B., D.-Y. Zhu, and Z.-D. Zhu, "A novel moving target detection approach for dual-channel SAR system," Progress In Electromagnetics Research, Vol. 115, 191-206, 2011.

18. Kumar, S. and M. Hebert, "A hierarchical field framework for unified context-based classification," IEEE 10th International Conference on Computer Vision, 1284-1291, 2005.

19. Chang, Y.-L., C.-Y. Chiang, and K.-S. Chen, "SAR image simulation with application to target recognition," Progress In Electromagnetics Research, Vol. 119, 35-57, 2011.
doi:10.2528/PIER11061507

20. Rabinovich, A., A. Vedaldi, C. Galleguillos, E. Wiewiora, and S. Belongie, "Objects in context," IEEE 11th International Conference on Computer Vision, 1-8, 2007.

21. Diao, W.-H., X. Mao, H.-C. Zheng, Y.-L. Xue, and V. Gui, "Image sequence measures for automatic target tracking," Progress In Electromagnetics Research, Vol. 130, 447-472, 2012.

22. Zhang, X., X., J. Qin, and G. Li, "SAR target classification using bayesian compressive sensing with scattering centers features," Progress In Electromagnetics Research, Vol. 136, 385-407, 2013.

23. Blacknell, D., S. Arini Nicholas, and I. McConnell, "SAR image understanding using contextual information," Proc. SPIE, SAR Image Analysis, Modeling, and Techniques IV, Vol. 4543, 73284, 2001.

24. Blacknell, D., "Contextual information in SAR target detection," IEEE Proceedings: Radar, Sonar and Navigation, Vol. 148, No. 1, 41-47, 2001.
doi:10.1049/ip-rsn:20010113

25. Noda, H., M. N. Shirazi, and E. Kawaguchi, "MRF-based texture segmentation using wavelet decomposed images," Pattern Recognition, Vol. 35, No. 4, 771-782, 2002.
doi:10.1016/S0031-3203(01)00077-2


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