论文标题
利用sar图像中的双树复合物小波变换进行船舶唤醒检测
Exploiting the Dual-Tree Complex Wavelet Transform for Ship Wake Detection in SAR Imagery
论文作者
论文摘要
在本文中，我们使用反问题制剂分析了海面的合成孔径雷达（SAR）图像，从而增强了radon域信息，以便准确检测船只唤醒。这是通过促进图像中的线性特征来实现的。对于解决问题的阶段，我们提出了一个惩罚函数，该惩罚函数将双树复杂小波变换（DT-CWT）与非convex cauchy惩罚函数结合在一起。解决这个逆问题的解决方案是基于前进（FB）拆分算法，以在radon域中获得增强的图像。与最先进的船舶尾流检测方法相比，所提出的方法取得了最佳结果，并在各种绩效指标方面取得了显着改善。在具有不同频带和空间分辨率的SAR图像中检测船的准确性超过90％，与第二好的方法相比，这清楚地表明准确性增长了7％。
In this paper, we analyse synthetic aperture radar (SAR) images of the sea surface using an inverse problem formulation whereby Radon domain information is enhanced in order to accurately detect ship wakes. This is achieved by promoting linear features in the images. For the inverse problem-solving stage, we propose a penalty function, which combines the dual-tree complex wavelet transform (DT-CWT) with the non-convex Cauchy penalty function. The solution to this inverse problem is based on the forward-backward (FB) splitting algorithm to obtain enhanced images in the Radon domain. The proposed method achieves the best results and leads to significant improvement in terms of various performance metrics, compared to state-of-the-art ship wake detection methods. The accuracy of detecting ship wakes in SAR images with different frequency bands and spatial resolution reaches more than 90%, which clearly demonstrates an accuracy gain of 7% compared to the second-best approach.
