Ship detection in SAR images by robust principle component analysis

SONG Shengli; YANG Jian

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Journal of Tsinghua University(Science and Technology) ›› 2015, Vol. 55 ›› Issue (8) : 844-848.

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Ship detection in SAR images by robust principle component analysis

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Abstract

Single polarization SAR ship detection using a constant false alarm rate (CFAR) detector does not have a standard clutter model, so the system often gives incomplete targets and misses weak targets in multitarget detection. A ship detection method was developed based on robust principle component analysis (RPCA) to improve the detection. This method leverages the intrinsic properties of SAR images that the sea area is approximately low rank and there are few ships. SAR images can be decomposed into the sum of a low rank component, a noise component and a sparse component via RPCA, with the sum of the first two corresponding to the sea surface and the third corresponding to ships. Thus, ship detection and clutter suppression are achieved in one step without a clutter model or statistics. The augmented Lagrange multiplier method for RPCA is verified by simulations. For comparison, cell averaging CFAR (CA-CFAR) and mean square error CFAR (MSE-CFAR) are also used. Tests with real data show that this method correctly detects ships from sea clutter with robust detection performance.

Key words

synthetic aperture radar (SAR) / ship detection / robust principle component analysis (RPCA) / constant false alarm rate (CFAR)

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SONG Shengli, YANG Jian. Ship detection in SAR images by robust principle component analysis[J]. Journal of Tsinghua University(Science and Technology). 2015, 55(8): 844-848

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