Man-made target detection in polarimetric SAR images using the Riemannian kernel Fisher criterion

GAO Wei; YIN Junjun; YANG Jian

doi:10.16511/j.cnki.qhdxxb.2016.21.055

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Journal of Tsinghua University(Science and Technology) ›› 2016, Vol. 56 ›› Issue (9) : 920-924,929. DOI: 10.16511/j.cnki.qhdxxb.2016.21.055

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Man-made target detection in polarimetric SAR images using the Riemannian kernel Fisher criterion

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Abstract

Detection of man-made targets is essential for automatic interpretation of polarimetric synthetic aperture radar (SAR) images. This paper describes a man-made target detection method that utilizes the Riemannian kernel Fisher criterion. The kernel function is constructed by means of a Riemannian metric defined on the manifold of Hermitian positive definite matrices. The polarimetric covariance matrices are mapped into the high-dimensional feature space induced by the kernel function and then discriminated by the Fisher criterion. This method takes into account the special matrix structure of the polarimetric SAR data and does not assume any statistical models; therefore, it is particularly suitable for detecting man-made targets in polarimetric SAR images. The effectiveness of this method is verified in the context of ship detection. Tests show that this method outperforms other detectors, especially for low target-to-clutter ratio.

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polarimetric SAR / man-made target detection / Riemannian manifold / kernel Fisher criterion

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GAO Wei, YIN Junjun, YANG Jian. Man-made target detection in polarimetric SAR images using the Riemannian kernel Fisher criterion[J]. Journal of Tsinghua University(Science and Technology). 2016, 56(9): 920-924,929 https://doi.org/10.16511/j.cnki.qhdxxb.2016.21.055

References

[1] Novak L M, Sechtin M B, Cardullo M J. Studies of target detection algorithms that use polarimetric radar data [J]. IEEE Trans Aerosp Electron Syst, 1989, 25(2): 150-165.
[2] Novak L M, Burl M C. Optimal speckle reduction in polarimetric SAR imagery [J]. IEEE Trans Aerosp Electron Syst, 1990, 26(2): 293-305.
[3] Yang J, Yamaguchi Y, Boerner W M, et al. Numerical methods for solving the optimal problem of contrast enhancement [J]. IEEE Trans Geosci Remote Sens, 2000, 38(2): 965-971.
[4] Chaney R D, Bud M C, Novak L M. On the performace of polarimetric target detection algorithms [J]. IEEE Aerosp Electron Syst Mag, 1990, 5(11):10-15.
[5] D'Hondt O, Guillaso S, Hellwich O. Iterative bilateral filtering of polarimetric SAR data [J]. IEEE J Sel Topics Appl Earth Observ Remote Sens, 2013, 6(3): 1628-1639.
[6] Song H, Yang W, Xu X, et al. Unsupervised PolSAR imagery classification based on Jensen-Bregman LogDet divergence [C]//Proceedings of the 10th European Conference on Synthetic Aperture Radar. Berlin, Germany: VDE, 2014: 915-918.
[7] Song H, Yang W, Bai Y, et al. Unsupervised classification of polarimetric SAR imagery using large-scale spectral clustering with spatial constraints [J]. Int J Remote Sens, 2015, 36(11): 2816-2830.
[8] Wang Y, Han C. PolSAR image segmentation by mean shift clustering in the tensor space [J]. Acta Autom Sin, 2010, 36(6): 798-806.
[9] Yang F, Gao W, Xu B, et al. Multi-frequency polarimetric SAR classification based on Riemannian manifold and simultaneous sparse representation [J]. Remote Sens, 2015, 7(7): 8469-8488.
[10] Sra S. Positive definite matrices and the S-divergence [J]. Proc Amer Math Soc, 2015.
[11] Jayasumana S, Hartley R, Salzmann M, et al. Kernel methods on Riemannian manifolds with Gaussian RBF kernels [J]. IEEE Trans Pattern Anal Mach Intell, 2015, 37(12): 2464-2477.
[12] Shawe-Taylor J, Cristianini N. Kernel Methods for Pattern Analysis [M]. Cambridge, UK: Cambridge University Press, 2004.