Key points are not available for this paper at this time.
Model-based decomposition methods are widely used in full-polarization synthetic aperture radar (SAR), for the inversion and interpretation of ground features and constitute an important approach for understanding the behavior of backscattering. However, owing to the substantial differences between land and marine environments, different man-made and natural vegetation scattering structures render existing decomposition models unable to reasonably characterize scatterers on ships. Moreover, the combination of polarization decomposition models and neural networks for ship detection has rarely been investigated. Therefore, this study proposes a four-component decomposition model (Ship-4SD) suitable for describing the scattering characteristics of ships based on the surface scattering, double-bounce scattering, ±45° oriented dipole, and asymmetric scattering components. Furthermore, based on the differences in the scattering properties exhibited by different scattering components in ships and the powerful feature extraction capability of convolutional neural networks (CNNs), a scattering characteristic-aware fully polarized SAR ship detection network (SCANet) was designed to make full use of the scattering components in the decomposition model. Finally, the experimental results on a large amount of GF-3 fully polarized SAR data validated that the reasonability and superiority of Ship-4SD and SCANet. The Ship-4SD can better distinguish ship and clutter pixels compared to other four-component models and has a higher target-clutter ratio with respect to the multi-component models. SCANet proposed in this paper achieved an average precision of 94.43% and 96.56% on the GF-3 and SSDD datasets, respectively, which is better than that of other competitive CNN algorithms.
Gao et al. (Sun,) studied this question.