论文标题
使用Cauchy正规化检测SAR图像中的船只唤醒
Detection of Ship Wakes in SAR Imagery Using Cauchy Regularisation
论文作者
论文摘要
在表征海面的合成孔径雷达（SAR）图像中，船舶尾流检测非常重要，因为醒来通常带有有关船舶的基本信息。大多数检测方法利用了船的线性特性，并将空间域中的线转换为变换域中的明亮或黑点，例如ra或霍夫变换。本文提出了一种基于稀疏正则化的创新船唤醒检测方法，以获得SAR图像的ra transform，其中线性特征的增强。相应的成本函数利用了Cauchy先验，并在此基础上提出了Cauchy近端操作员。一种贝叶斯方法，即莫罗 - 伊希达未经调整的langevin算法（Myula），该算法是计算上有效且可靠的，可通过最大程度地降低负对数形状分布来估算变换域中的图像。基于凯奇先验的方法的检测准确性为86.7％，这是通过六个宇宙伴侣图像的实验证明的。
Ship wake detection is of great importance in the characterisation of synthetic aperture radar (SAR) images of the ocean surface since wakes usually carry essential information about vessels. Most detection methods exploit the linear characteristics of the ship wakes and transform the lines in the spatial domain into bright or dark points in a transform domain, such as the Radon or Hough transforms. This paper proposes an innovative ship wake detection method based on sparse regularisation to obtain the Radon transform of the SAR image, in which the linear features are enhanced. The corresponding cost function utilizes the Cauchy prior, and on this basis, the Cauchy proximal operator is proposed. A Bayesian method, the Moreau-Yoshida unadjusted Langevin algorithm (MYULA), which is computationally efficient and robust is used to estimate the image in the transform domain by minimizing the negative log-posterior distribution. The detection accuracy of the Cauchy prior based approach is 86.7%, which is demonstrated by experiments over six COSMO-SkyMed images.
