volume | PIER Journals

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.

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 Google Scholar

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 Google Scholar

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 Google Scholar

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 Google Scholar

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. Google Scholar

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 Google Scholar

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 Google Scholar

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 Google Scholar

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. Google Scholar

Dr. Huafei Zhao

Prof. Meng Yang