volume | PIER Journals

Abstract

Aiming at the problem of high false alarm rate with respect to adaptive threshold in the ship detection from synthetic aperture radar (SAR) images, a novel strategy increasing robustness when using local adaptive threshold is proposed. In this article, we establish a fusion detection model based on a combination of the information geometry and surface geometry. Information geometry from a metric viewpoint can increase the contrast between targets and clutter in SAR image. Local surface feature gives a brief application of adaptive threshold method in ship detection from SAR images by means of the constant false-alarm-rate. Experiments indicate that the proposed geometry-based approach can effectively detect ship targets from complex background SAR images by using the method of fusion processing.

1. Gao, G., S. Gao, J. He, and G. Li, "Ship detection using compact polarimetric SAR based on the notch filter," IEEE Trans. Geosci. Remote Sens., Vol. 56, No. 9, 5380-5393, 2018.
doi:10.1109/TGRS.2018.2815582

2. Jiao, J., Y. Zhang, H. Sun, X. Yang, X. Gao, W. Hong, K. Fu, and X. Sun, "A densely connected end-to-end neural network for multiscale and multiscene SAR ship detection," IEEE Access, Vol. 6, 20881-20892, 2018.
doi:10.1109/ACCESS.2018.2825376

3. Li, T., Z. Liu, L. Ran, and R. Xie, "Target detection by exploiting superpixel-level statistical dissimilarity for SAR imagery," IEEE Geosci. Remote Sens. Lett., Vol. 15, No. 4, 562-566, 2018.
doi:10.1109/LGRS.2018.2805714

4. Li, T., Z. Liu, R. Xie, and L. Ran, "An improved superpixel-level CFAR detection method for ship targets in high-resolution SAR images," IEEE J. Sel. Topics Appl. Earth Observ., Vol. 11, No. 1, 184-194, 2018.
doi:10.1109/JSTARS.2017.2764506

5. Odysseas, P., A. Alin, and B. David, "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

6. Ai, J., X. Yang, J. Song, Z. Dong, L. Jia, and F. Zhou, "An adaptively truncated clutter-statistics-based two-parameter CFAR detector in SAR imagery," IEEE J. Oceanic Eng., Vol. 43, No. 1, 267-279, 2018.
doi:10.1109/JOE.2017.2768198

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

8. Nielsen, F. and R. Bhatia, Matrix Information Geometry, Springer-Verlag, 2013.
doi:10.1007/978-3-642-30232-9

9. Forbes, C., M. Evans, N. Hastings, and B. Peacock, Statistical Distributions, Wiley, 2010.
doi:10.1002/9780470627242

10. Arwini, K. A. and C. T. J. Dodson, Information Geometry - Near Randomness and Near Independence, Springer-Verlag, 2008.

11. Xue, J.-H. and D. M. Titterington, "T-tests, F-tests and Otsu’s methods for image thresholding," IEEE Trans. Image Process., Vol. 20, No. 8, 2392-2396, 2011.
doi:10.1109/TIP.2011.2114358

12. Fabbrini, L., M. Greco, M. Messina, and G. Pinelli, "Improved edge enhancing diffusion filter for speckle-corrupted images," IEEE Geosci. Remote Sens. Lett., Vol. 11, No. 1, 119-123, 2014.
doi:10.1109/LGRS.2013.2247377

Dr. Xiangxiang Yang

Prof. Meng Yang

Dr. Yinhua Zhang

Dr. Gong Zhang