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首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >A Polarimetric Extension of Low-Rank Plus Sparse Decomposition and Radon Transform for Ship Wake Detection in Synthetic Aperture Radar Images
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A Polarimetric Extension of Low-Rank Plus Sparse Decomposition and Radon Transform for Ship Wake Detection in Synthetic Aperture Radar Images

机译：低阶加稀疏分解和Radon变换的极化扩展，用于合成孔径雷达图像中的舰船唤醒检测

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摘要

In past research, the problem of obtaining stable motion estimation of maritime targets in sea clutter making wake structure detection and reconnaissance difficult has been tackled. This new research presents an upgrade for automatic estimation of maritime target motion parameters by evaluating the generated Kelvin waves detected in synthetic aperture radar (SAR) images. The algorithm consists in considering the polarimetric (Pol) information of SAR images and evaluating a multiple-channel and dual-stage Pol low-rank plus sparse decomposition (Pol-LRSD) assisted by Radon transform (RT) for clutter reduction, sparse object detection, precise wake inclination estimation, and targets classification. This upgraded algorithm is based on Pol robust principal component analysis (Pol-RPCA) implemented by convex programming. This upgraded Pol-LRSD algorithm permits the extrapolation of the Pol signature of sparse objects of interest consisting of the maritime targets and the Kelvin pattern from the unchanging low-rank background. Pol-RPCA and RT methods applied to Pol SAR surveillance permit more precise detection and segmentation of maritime targets.
机译：在过去的研究中，解决了在海杂波中获得海上目标的稳定运动估计的问题，这使得尾流结构的检测和侦察变得困难。这项新研究提出了一种通过评估在合成孔径雷达（SAR）图像中检测到的产生的开尔文波来自动估计海上目标运动参数的升级方法。该算法包括考虑SAR图像的极化（Pol）信息并评估由Radon变换（RT）辅助的多通道和双级Pol低秩加稀疏分解（Pol-LRSD），以减少杂波，稀疏物体检测，精确的尾流倾角估算和目标分类。该升级算法基于通过凸编程实现的Pol健壮主成分分析（Pol-RPCA）。这种升级的Pol-LRSD算法允许从不变的低等级背景推断出包括海事目标和Kelvin模式在内的稀疏目标物体的Pol签名。用于Pol SAR监视的Pol-RPCA和RT方法可以更精确地检测和分割海上目标。

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