volume | PIER Journals

2014-03-18

A Coarse-to-Fine Approach for Ship Detection in SAR Image Based on CFAR Algorithm

Meng Yang,

Prof. Meng Yang

School of Communication Engineering

Hangzhou Dianzi University

China

Homepage

Gong Zhang,

Dr. Gong Zhang

College of Information Science and Technology

Nanjing University of Aeronautics and Astronautics

China

Homepage

Chunsheng Guo,

Dr. Chunsheng Guo

College of Communication Engineering

Hangzhou Dianzi University

China

Homepage

Minhong Sun

Dr. Minhong Sun

College of Communication Engineering

Hangzhou Dianzi University

China

Homepage

Progress In Electromagnetics Research M, Vol. 35, 105-111, 2014

doi:10.2528/PIERM14012201

Abstract

Among ship detection methods for SAR image, constant false alarm rate (CFAR) is the most important one. However, several factors, such as detector parameter and distribution of ocean clutter, affect the performance of CFAR detection. This paper proposes a novel hierarchical complete and operational ship detection approach based on detector parameter estimation and clutter pixel replacement, which is considered a sequential coarse-to-fine elimination process of false alarms. First, a simple barycentric algorithm is adopted to estimate target-window size, and a morphology method is used to estimate false alarm rate for CFAR detector. Second, a clutter pixel replacement approach based on the statistical features of sea clutter is presented to obtain statistically independent, stationary, and Weibull distributed random data for CFAR detector to remove all false alarms. Experimental results of the detection methods on a SAR image dataset show that the proposed approach is effective in reducing false alarms and obtains a satisfactory ship detection performance.

Citation

Copy Export

Meng Yang, Gong Zhang, Chunsheng Guo, and Minhong Sun, "A Coarse-to-Fine Approach for Ship Detection in SAR Image Based on CFAR Algorithm," Progress In Electromagnetics Research M, Vol. 35, 105-111, 2014.
doi:10.2528/PIERM14012201

References

1. Wang, Y. and H. Liu, "A hierarchical ship detection scheme for high-resolution SAR images," IEEE Transaction on Geoscience and Remote Sensing, Vol. 50, No. 10, 4173-4184, 2012.
doi:10.1109/TGRS.2012.2189011

2. Qin, X., S. Zhou, H. Zou, and G. Gao, "A CFAR detection algorithm for generalized Gamma distributed background in high-resolution SAR images," IEEE Geoscience and Remote Sensing Letters, Vol. 10, No. 4, 806-810, 2013.
doi:10.1109/LGRS.2012.2224317

3. Paes, R. L., J. A. Lorenzzetti, and D. F. M. Gherardi, "Ship detection using TerraSAR-X images in the Campos basin (Brazil)," IEEE Geoscience and Remote Sensing Letters, Vol. 7, No. 3, 545-548, 2010.
doi:10.1109/LGRS.2010.2041322

4. Erfanian, S. and V. T. Vakili, "Introducing excision switching-CFAR in K distributed sea clutter," Signal Processing,, Vol. 89, No. 6, 1023-1031, 2009.
doi:10.1016/j.sigpro.2008.12.001

5. Ai, J., X. Qi, W. Yu, Y. Deng, F. Liu, and L. Shi, "A new CFAR ship detection algorithm based on 2-D joint log-normal distribution in SAR images," IEEE Geoscience and Remote Sensing Letters, Vol. 7, No. 4, 806-810, 2010.
doi:10.1109/LGRS.2010.2048697

6. Guida, M., M. Longo, and M. Lops, "Biparameter linear estimation for CFAR against Weibull clutter," IEEE Transaction on Aerospace and Electronic System, Vol. 28, No. 1, 138-152, 1992.
doi:10.1109/7.135440

7. Soille, P., Morphological Image Analysis: Principles and Applications, Springer-Verlag, New York, 2003.

8. Pham, H., Springer Handbook of Engineering Statistics, Springer-Verlag, London, 2006.
doi:10.1007/978-1-84628-288-1

9. European Space Agency, Oct. 2013, , Available: https://earth.esa.int/web/guest/missions/esa-operational-eo-missions/ers.

10. Canadian Space Agency, Oct. 2013, , Available: http://www.asc-csa.gc.ca/eng/satellites/radarsat/radarsat-tableau.asp.

11. Liao, M., C. Wang, Y. Wang, and L. Jiang, "Using SAR images to detect ships from sea clutter," IEEE Geoscience and Remote Sensing Letters, Vol. 5, No. 2, 194-198, 2008.
doi:10.1109/LGRS.2008.915593