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Ship wake detection for SAR images with complex backgrounds based on morphological dictionary learning

The ship wake detection of SAR images is useful not only in estimating the speed and the direction of moving ships, but also in finding small ships which are hard to be detected. The traditional ship wake detection methods of SAR images can achieve satisfactory results in simple backgrounds, but hardly work in complex backgrounds. In this paper, we propose a novel method based on the morphological component analysis and the dictionary learning to detect ship wakes in complex backgrounds. In our method, the SAR image is decomposed into a cartoon component containing ship wakes and a sea-background texture component by adaptive-ly learning the ship wake dictionary and the sea-background texture dictionary; and then the shearlet transform is used to enhance ship wakes in the cartoon component. Experimental results show our method outperforms the traditional methods for SAR images in complex backgrounds.