Search search
submit Submit
Publication Date
to
Vol. 142
Latest Volume
All Volumes
PIER 180 [2024] PIER 179 [2024] PIER 178 [2023] PIER 177 [2023] PIER 176 [2023] PIER 175 [2022] PIER 174 [2022] PIER 173 [2022] PIER 172 [2021] PIER 171 [2021] PIER 170 [2021] PIER 169 [2020] PIER 168 [2020] PIER 167 [2020] PIER 166 [2019] PIER 165 [2019] PIER 164 [2019] PIER 163 [2018] PIER 162 [2018] PIER 161 [2018] PIER 160 [2017] PIER 159 [2017] PIER 158 [2017] PIER 157 [2016] PIER 156 [2016] PIER 155 [2016] PIER 154 [2015] PIER 153 [2015] PIER 152 [2015] PIER 151 [2015] PIER 150 [2015] PIER 149 [2014] PIER 148 [2014] PIER 147 [2014] PIER 146 [2014] PIER 145 [2014] PIER 144 [2014] PIER 143 [2013] PIER 142 [2013] PIER 141 [2013] PIER 140 [2013] PIER 139 [2013] PIER 138 [2013] PIER 137 [2013] PIER 136 [2013] PIER 135 [2013] PIER 134 [2013] PIER 133 [2013] PIER 132 [2012] PIER 131 [2012] PIER 130 [2012] PIER 129 [2012] PIER 128 [2012] PIER 127 [2012] PIER 126 [2012] PIER 125 [2012] PIER 124 [2012] PIER 123 [2012] PIER 122 [2012] PIER 121 [2011] PIER 120 [2011] PIER 119 [2011] PIER 118 [2011] PIER 117 [2011] PIER 116 [2011] PIER 115 [2011] PIER 114 [2011] PIER 113 [2011] PIER 112 [2011] PIER 111 [2011] PIER 110 [2010] PIER 109 [2010] PIER 108 [2010] PIER 107 [2010] PIER 106 [2010] PIER 105 [2010] PIER 104 [2010] PIER 103 [2010] PIER 102 [2010] PIER 101 [2010] PIER 100 [2010] PIER 99 [2009] PIER 98 [2009] PIER 97 [2009] PIER 96 [2009] PIER 95 [2009] PIER 94 [2009] PIER 93 [2009] PIER 92 [2009] PIER 91 [2009] PIER 90 [2009] PIER 89 [2009] PIER 88 [2008] PIER 87 [2008] PIER 86 [2008] PIER 85 [2008] PIER 84 [2008] PIER 83 [2008] PIER 82 [2008] PIER 81 [2008] PIER 80 [2008] PIER 79 [2008] PIER 78 [2008] PIER 77 [2007] PIER 76 [2007] PIER 75 [2007] PIER 74 [2007] PIER 73 [2007] PIER 72 [2007] PIER 71 [2007] PIER 70 [2007] PIER 69 [2007] PIER 68 [2007] PIER 67 [2007] PIER 66 [2006] PIER 65 [2006] PIER 64 [2006] PIER 63 [2006] PIER 62 [2006] PIER 61 [2006] PIER 60 [2006] PIER 59 [2006] PIER 58 [2006] PIER 57 [2006] PIER 56 [2006] PIER 55 [2005] PIER 54 [2005] PIER 53 [2005] PIER 52 [2005] PIER 51 [2005] PIER 50 [2005] PIER 49 [2004] PIER 48 [2004] PIER 47 [2004] PIER 46 [2004] PIER 45 [2004] PIER 44 [2004] PIER 43 [2003] PIER 42 [2003] PIER 41 [2003] PIER 40 [2003] PIER 39 [2003] PIER 38 [2002] PIER 37 [2002] PIER 36 [2002] PIER 35 [2002] PIER 34 [2001] PIER 33 [2001] PIER 32 [2001] PIER 31 [2001] PIER 30 [2001] PIER 29 [2000] PIER 28 [2000] PIER 27 [2000] PIER 26 [2000] PIER 25 [2000] PIER 24 [1999] PIER 23 [1999] PIER 22 [1999] PIER 21 [1999] PIER 20 [1998] PIER 19 [1998] PIER 18 [1998] PIER 17 [1997] PIER 16 [1997] PIER 15 [1997] PIER 14 [1996] PIER 13 [1996] PIER 12 [1996] PIER 11 [1995] PIER 10 [1995] PIER 09 [1994] PIER 08 [1994] PIER 07 [1993] PIER 06 [1992] PIER 05 [1991] PIER 04 [1991] PIER 03 [1990] PIER 02 [1990] PIER 01 [1989]
All Journals
PIER PIER B PIER C PIER M PIER Letters
2013-08-23
A Novel SAR Target Detection Algorithm Based on Contextual Knowledge
By
Fei Gao,

    Dr. Fei Gao

    School of Electronic and Information Engineering

    BeiHang University

    China

    Homepage

Achang Ru,

    Dr. Achang Ru

    College of Electronic and Information Engineering

    Beihang University

    China

    Homepage

Jinping Sun,

    Dr. Jinping Sun

    School of Electronic and Information Engineering

    Beihang University

    China

    Homepage

Amir Hussain

    Dr. Amir Hussain

    School of Electronic and Information Engineering

    Beihang University

    China

    Homepage

Progress In Electromagnetics Research, Vol. 142, 123-140, 2013
doi:10.2528/PIER13062403
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
Fei Gao, Achang Ru, Jinping Sun, and Amir Hussain, "A Novel SAR Target Detection Algorithm Based on Contextual Knowledge," Progress In Electromagnetics Research, Vol. 142, 123-140, 2013.
doi:10.2528/PIER13062403
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

Download Full Article (303) View Full Article volume
The EM Academy piers.org jpier.org Who's Who in EM
Copyright © 2022 The Electromagnetics Academy. All Rights Reserved.