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Ship detection in polarimetric synthetic aperture radar (PolSAR) images has attracted widespread attention in recent years. However, pixel level detection methods are heavily affected by inherent speckle noise. In this letter, we proposed a detection method that enhances the ship-sea contrast beforehand by combining local statistical saliency and scattering mechanism coherence in superpixel-level. Firstly, simple linear iterative clustering (SLIC) based segmentation method is adopted for PolSAR images to generate superpixels. Then, local saliency is calculated based on superpixel-level similarity from the perspective of statistical characteristics. Based on this, the superpixel-level modified polarimetric coherence metric is obtained from the perspective of physical scattering mechanisms, which can help distinguish small ships with low saliency and strong sea clutters with high saliency. Ship detection is achieved by combining the two features above. The experimental results based on real PolSAR data show that compared with other classic and state-of-the-art methods, the proposed method has improved the figure of merit by at least 4.28% and has increased the target clutter ratio by at least 8.43 decibel (dB) on average.
Deng et al. (Mon,) studied this question.