Spaceborne synthetic aperture radar (SAR) image ship detection is an important tool to ensure the safety of sea areas and improve the efficiency of maritime traffic. Due to the sparse distribution of ships in the vast ocean, many imaging results are redundant. Furthermore, SAR imaging consumes huge computing, storage, and communication resources. The range-compressed data, without azimuth compression calculation, has caught our attention. Nevertheless, the echo energy of the ship is scattered in the azimuth direction, making it difficult to detect. Several deep learning-based methods are proposed, yet their performance is constrained by labeled datasets. As they neglect sea clutter interference, these methods are also impractical. To address these issues, this article proposes a constant false alarm rate (CFAR)-based ship detector for range-compressed SAR data. First, the imaging process of spaceborne SAR signal is reviewed and analyzed. Then, a generalized Gamma distribution (G $\Gamma $ D)-based sea clutter model is proposed for the SAR range-focused domain. Next, a CFAR-based method for detecting ships in range-compressed SAR data is customized. Finally, experiments are conducted on Sentinel-1 and ERS-2 SAR data. The results show that the proposed sea clutter model has high goodness-of-fit, and the customized CFAR-based method effectively detects ship targets. In summary, ship detection in range-compressed SAR data is very promising research.

中文翻译：

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一种基于CFAR的星载SAR系统距离压缩数据船舶检测新方法


星载合成孔径雷达（SAR）图像船舶检测是保障海域安全、提高海上交通效率的重要工具。由于浩瀚海洋中船舶分布稀疏，很多成像结果都是冗余的。此外，SAR成像消耗大量的计算、存储和通信资源。距离压缩的数据，没有方位压缩计算，引起了我们的注意。然而，船舶的回波能量在方位角方向上分散，难以被探测到。提出了几种基于深度学习的方法，但它们的性能受到标记数据集的限制。由于忽略了海杂波干扰，这些方法也不切实际。为了解决这些问题，本文提出了一种基于恒定虚警率 (CFAR) 的船舶探测器，用于距离压缩 SAR 数据。首先，对星载SAR信号的成像过程进行了回顾和分析。然后，针对SAR距离聚焦域提出了基于广义伽玛分布（G$\Gamma$D）的海杂波模型。接下来，定制了一种基于恒虚警 (CFAR) 的方法，用于在距离压缩 SAR 数据中检测船舶。最后，对Sentinel-1和ERS-2 SAR数据进行了实验。结果表明，所提出的海杂波模型具有较高的拟合优度，基于CFAR的定制方法能够有效检测船舶目标。总之，距离压缩SAR数据中的船舶检测是非常有前途的研究。