Vol. 105
Latest Volume
All Volumes
PIERL 121 [2024] PIERL 120 [2024] PIERL 119 [2024] PIERL 118 [2024] PIERL 117 [2024] PIERL 116 [2024] PIERL 115 [2024] PIERL 114 [2023] PIERL 113 [2023] PIERL 112 [2023] PIERL 111 [2023] PIERL 110 [2023] PIERL 109 [2023] PIERL 108 [2023] PIERL 107 [2022] PIERL 106 [2022] PIERL 105 [2022] PIERL 104 [2022] PIERL 103 [2022] PIERL 102 [2022] PIERL 101 [2021] PIERL 100 [2021] PIERL 99 [2021] PIERL 98 [2021] PIERL 97 [2021] PIERL 96 [2021] PIERL 95 [2021] PIERL 94 [2020] PIERL 93 [2020] PIERL 92 [2020] PIERL 91 [2020] PIERL 90 [2020] PIERL 89 [2020] PIERL 88 [2020] PIERL 87 [2019] PIERL 86 [2019] PIERL 85 [2019] PIERL 84 [2019] PIERL 83 [2019] PIERL 82 [2019] PIERL 81 [2019] PIERL 80 [2018] PIERL 79 [2018] PIERL 78 [2018] PIERL 77 [2018] PIERL 76 [2018] PIERL 75 [2018] PIERL 74 [2018] PIERL 73 [2018] PIERL 72 [2018] PIERL 71 [2017] PIERL 70 [2017] PIERL 69 [2017] PIERL 68 [2017] PIERL 67 [2017] PIERL 66 [2017] PIERL 65 [2017] PIERL 64 [2016] PIERL 63 [2016] PIERL 62 [2016] PIERL 61 [2016] PIERL 60 [2016] PIERL 59 [2016] PIERL 58 [2016] PIERL 57 [2015] PIERL 56 [2015] PIERL 55 [2015] PIERL 54 [2015] PIERL 53 [2015] PIERL 52 [2015] PIERL 51 [2015] PIERL 50 [2014] PIERL 49 [2014] PIERL 48 [2014] PIERL 47 [2014] PIERL 46 [2014] PIERL 45 [2014] PIERL 44 [2014] PIERL 43 [2013] PIERL 42 [2013] PIERL 41 [2013] PIERL 40 [2013] PIERL 39 [2013] PIERL 38 [2013] PIERL 37 [2013] PIERL 36 [2013] PIERL 35 [2012] PIERL 34 [2012] PIERL 33 [2012] PIERL 32 [2012] PIERL 31 [2012] PIERL 30 [2012] PIERL 29 [2012] PIERL 28 [2012] PIERL 27 [2011] PIERL 26 [2011] PIERL 25 [2011] PIERL 24 [2011] PIERL 23 [2011] PIERL 22 [2011] PIERL 21 [2011] PIERL 20 [2011] PIERL 19 [2010] PIERL 18 [2010] PIERL 17 [2010] PIERL 16 [2010] PIERL 15 [2010] PIERL 14 [2010] PIERL 13 [2010] PIERL 12 [2009] PIERL 11 [2009] PIERL 10 [2009] PIERL 9 [2009] PIERL 8 [2009] PIERL 7 [2009] PIERL 6 [2009] PIERL 5 [2008] PIERL 4 [2008] PIERL 3 [2008] PIERL 2 [2008] PIERL 1 [2008]
2022-07-24
Finsler Metric Method for Ship Detection in SAR Image
By
Progress In Electromagnetics Research Letters, Vol. 105, 63-69, 2022
Abstract
In this article, we focus on metric space in Finsler geometry and propose a method of ship detection in synthetic aperture radar (SAR) amplitude image based on Finsler information geometry. This provides deep unified perspectives of Finsler geometric application. The proposed method consists of three stages: The Weibull manifold model is used to represent the statistical information of intensity SAR images; then the Finsler metric is constructed to realize the distance measurement between probability distributions in Weibull manifold space; finally, Finsler metric space is used to achieve saliency representation and detection of ships. Theoretical analysis and comprehensive experimental results demonstrate the robustness and effectiveness of the proposed approach using typical real SAR images.
Citation
Huafei Zhao, and Meng Yang, "Finsler Metric Method for Ship Detection in SAR Image," Progress In Electromagnetics Research Letters, Vol. 105, 63-69, 2022.
doi:10.2528/PIERL22032704
References

1. Wang, X., G. Li, X. Zhang, and Y. He, "Ship detection in SAR images via local contrast of fisher vectors," IEEE Trans. Geosci. Remote Sens., Vol. 58, No. 9, 6467-6479, 2020.
doi:10.1109/TGRS.2020.2976880

2. Wang, X., G. Li, X. Zhang, and Y. He, "A fast CFAR algorithm based on density-censoring operation for ship detection in SAR images," IEEE Signal Process. Lett., Vol. 28, 1085-1089, 2021.
doi:10.1109/LSP.2021.3082034

3. Ai, J., Y. Mao, Q. Luo, M. Xing, K. Jiang, L. Jia, and X. Yang, "Robust CFAR ship detector based on bilateral-trimmed-statistics of complex ocean scenes in SAR imagery: A closed-form solution," IEEE Trans. Aerosp. Electron. Syst., Vol. 57, No. 3, 1872-1890, 2021.
doi:10.1109/TAES.2021.3050654

4. Yang, R., G. Wang, Z. Pan, H. Lu, H. Zhang, and X. Jia, "A novel false alarm suppression method for CNN-based SAR ship detector," IEEE Geosci. Remote Sens. Lett., Vol. 18, No. 8, 1401-1405, 2021.
doi:10.1109/LGRS.2020.2999506

5. Li, X., W. Huang, K. D. Peters, and D. Power, "Assessment of synthetic aperture radar image preprocessing methods for iceberg and ship recognition with convolutional neural networks," Proc. IEEE Radar Conf., 1-5, 2019.

6. Amari, S., Information Geometry and Its Application, Springer, Tokyo, 2016.
doi:10.1007/978-4-431-55978-8

7. Shen, Y. and Z. Shen, Introduction to Modern Finsler Geometry, Science Press, Beijing, 2013.

8. Forbes, C., M. Evans, N. Hastings, and B. Peacock, Statistical Distributions, John Wiley & Sons, New York, 2010.
doi:10.1002/9780470627242

9. Wang, X., C. Chen, Z. Pan, and Z. Pan, "Superpixel-based LCM detector for faint ships hidden in strong noise background SAR imagery," IEEE Geosci. Remote Sens. Lett., Vol. 16, No. 3, 417-421, 2019.
doi:10.1109/LGRS.2018.2873637

10. Khambampati, A. K., D. Liu, S. K. Konki, and K. Y. Kim, "An automatic detection of the ROI using Otsu thresholding in nonlinear difference EIT imaging," IEEE Sensors J., Vol. 18, No. 12, 5133-5142, 2018.
doi:10.1109/JSEN.2018.2828312

11. Pappas, O., A. Achim, and D. Bull, "Superpixel-level CFAR detectors for ship detection in SAR imagery," IEEE Geosci. Remote Sens. Lett., Vol. 15, No. 9, 1397-1401, 2018.
doi:10.1109/LGRS.2018.2838263

12. Xian, S., Z. Wang, Y. Sun, W. Diao, Y. Zhang, and K. Fu, "AIR-SARShip-1.0: High-resolution SAR ship detection dataset," J. Radars, Vol. 8, No. 6, 852-862, 2019.